Recurrent Neural Networks (RNNs) are widely used models for sequence data.Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data.However, this works only for a handful of layers.Unlike feedforward networks, stacking more than a few recurrent units (e.g., LSTM cells) usually hurts model performance, the reason being vanishing or exploding gradients during training.We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network.We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified, so that with an increasing depth they tend to vanish, respectively explode.Based on our analysis we design a new type of gated cell that better preserves gradient magnitude, and therefore makes it possible to train deeper RNNs.We experimentally validate our design with five different sequence modelling tasks on three different datasets.The proposed stackable recurrent (STAR) cell allows for substantially deeper recurrent architectures, with improved performance.

Analizziamo la propagazione del gradiente nelle deep RNN e dalla nostra analisi, proponiamo una nuova deep RNN multilayer.


Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive.In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary least squares (OLS).We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to choice of learning rates.We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems.This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.

Analizziamo matematicamente l'effetto della batch normalization su un modello semplice e otteniamo nuove intuizioni chiave che si applicano all'apprendimento supervised generale.


Solving tasks in Reinforcement Learning is no easy feat.As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior.While constraints may solve this issue, there is no closed form solution for general constraints.In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one.We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.

Per i vincoli complessi in cui non è facile stimare il gradiente, usiamo la discounted penalty come segnale guida. Dimostriamo che sotto certe ipotesi converge ad una soluzione fattibile.


The Handheld Virtual Panel (HVP) is the virtual panel attached to the non-dominant hand’s controller in virtual reality (VR).The HVP is the go-to technique for enabling menus and toolboxes in VR devices.In this paper, we investigate target acquisition performance for the HVP as a function of four factors: target width, target distance, the direction of approach with respect to gravity, and the angle of approach.Our results show that all four factors have significant effects on user performance.Based on the results, we propose guidelines towards the ergonomic and performant design of the HVP interfaces.

L'articolo studia target acquisition per i pannelli virtuali palmari in VR e mostra che la larghezza del bersaglio, la distanza, la direzione dell'approccio rispetto alla gravità e l'angolo di approccio, hanno tutti un impatto sulle prestazioni dell'utente.


Deep neural networks have demonstrated unprecedented success in various knowledge management applications.However, the networks created are often very complex, with large numbers of trainable edges which require extensive computational resources.We note that many successful networks nevertheless often contain large numbers of redundant edges.Moreover, many of these edges may have negligible contributions towards the overall network performance.In this paper, we propose a novel iSparse framework and experimentally show, that we can sparsify the network, by 30-50%, without impacting the network performance.iSparse leverages a novel edge significance score, E, to determine the importance of an edge with respect to the final network output.Furthermore, iSparse can be applied both while training a model or on top of a pre-trained model, making it a  retraining-free approach - leading to a minimal computational overhead.Comparisons of iSparse against PFEC, NISP, DropConnect, and Retraining-Free on benchmark datasets show that iSparse leads to effective network sparsifications.

iSparse elimina gli archi della rete irrilevanti o insignificanti con un impatto minimo sulle prestazioni della rete, determinando l'importanza degli archi rispetto all'output finale della rete.


Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.Motivated by the observation that efforts to codeworld knowledge into machine readable knowledge bases tend to be entity-centric,we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the contexts in which those entities were mentioned.We show that large scale training of neural models allows us to learn extremely high fidelity entity typing information, which we demonstrate with few-shot reconstruction of Wikipedia categories.Our learning approach is powerful enoughto encode specialized topics such as Giro d’Italia cyclists.

Impariamo rappresentazioni di entità che possono ricostruire le categorie di Wikipedia con pochi esempi.


Variational autoencoders (VAEs) have been successful at learning a low-dimensional manifold from high-dimensional data with complex dependencies.At their core, they consist of a powerful Bayesian probabilistic inference model, to capture the salient features of the data.In training, they exploit the power of variational inference, by optimizing a lower bound on the model evidence.The latent representation and the performance of VAEs are heavily influenced by the type of bound used as a cost function.Significant research work has been carried out into the development of tighter bounds than the original ELBO, to more accurately approximate the true log-likelihood.By leveraging the q-deformed logarithm in the traditional lower bounds, ELBO and IWAE, and the upper bound CUBO, we bring contributions to this direction of research.In this proof-of-concept study, we explore different ways of creating these q-deformed bounds that are tighter than the classical ones and we show improvements in the performance of such VAEs on the binarized MNIST dataset.

Usando il logaritmo q-deformato, deriviamo limiti più stretti di IWAE, per addestrare autoencoder variazionali.


A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness.Yet Breiman shows a dilemma (Breiman, 1999) that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization error.In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed normalized margins using Lipschitz constant bound by spectral norm products.With both simplified theory and extensive experiments, Breiman's dilemma is shown to rely on dynamics of normalized margin distributions, that reflects the trade-off between model expression power and data complexity.When the complexity of data is comparable to the model expression power in the sense that training and test data share similar phase transitions in normalized margin dynamics, two efficient ways are derived via classic margin-based generalization bounds to successfully predict the trend of generalization error.On the other hand, over-expressed models that exhibit uniform improvements on training normalized margins may lose such a prediction power and fail to prevent the overfitting.

Il dilemma di Bregman è dimostrato nel deep learning dal fatto che il miglioramento dei margini dei modelli iper-parametrizzati può risultare in un overfitting, e le dinamiche delle distribuzioni di margine normalizzate sono proposte per prevedere l'errore di generalizzazione e identificare tale dilemma.


It has been an open research challenge for developing an end-to-end multi-domain task-oriented dialogue system, in which a human can converse with the dialogue agent to complete tasks in more than one domain.First, tracking belief states of multi-domain dialogues is difficult as the dialogue agent must obtain the complete belief states from all relevant domains, each of which can have shared slots common among domains as well as unique slots specifically for the domain only.Second, the dialogue agent must also process various types of information, including contextual information from dialogue context, decoded dialogue states of current dialogue turn, and queried results from a knowledge base, to semantically shape context-aware and task-specific responses to human.To address these challenges, we propose an end-to-end neural architecture for task-oriented dialogues in multiple domains.We propose a novel Multi-level Neural Belief Tracker which tracks the dialogue belief states by learning signals at both slot and domain level independently.The representations are combined in a Late Fusion approach to form joint feature vectors of (domain, slot) pairs.Following recent work in end-to-end dialogue systems, we incorporate the belief tracker with generation components to address end-to-end dialogue tasks.We achieve state-of-the-art performance on the MultiWOZ2.1 benchmark with 50.91% joint goal accuracy and competitive measures in task-completion and response generation.

Abbiamo proposto un sistema di dialogo end-to-end con un nuovo state tracker di dialogo multilivello e abbiamo ottenuto prestazioni coerenti su MultiWOZ2.1 nello state tracking, nel completamento dei task e nella generazione di risposte.


Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.  In this paper,we connect a general family of learning objectives including score matching to Wassersteingradient flows.This connection enables us to design a scalable approximation to theseobjectives, with a form similar to single-step contrastive divergence.We present applications in training implicit variational and Wasserstein auto-encoders with manifold-valued priors.

Presentiamo un'approssimazione scalabile per una vasta gamma di obiettivi EBM, e applicazioni in VAE impliciti e WAE


This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge.Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.

Questo articolo sviluppa un metodo di principio per continual learning nei deep model.


In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy.In this study, we propose an RL algorithm for solving PO tasks.Our method comprises two parts: a variational recurrent model (VRM) for modeling the environment, and an RL controller that has access to both the environment and the VRM.The proposed algorithm was tested in two types of PO robotic control tasks, those in which either coordinates or velocities were not observable and those that require long-term memorization.Our experiments show that the proposed algorithm achieved better data efficiency and/or learned more optimal policy than other alternative approaches in tasks in which unobserved states cannot be inferred from raw observations in a simple manner.

Un algoritmo di deep RL per risolvere POMDP autocodificando gli stati sottostanti utilizzando un modello ricorrente variazionale


This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning (RL).The setup is as follows: given an episodic task and a finite number of off-policy RL algorithms, a meta-algorithm has to decide which RL algorithm is in control during the next episode so as to maximize the expected return.The article presents a novel meta-algorithm, called Epochal Stochastic Bandit Algorithm Selection (ESBAS).Its principle is to freeze the policy updates at each epoch, and to leave a rebooted stochastic bandit in charge of the algorithm selection.Under some assumptions, a thorough theoretical analysis demonstrates its near-optimality considering the structural sampling budget limitations.ESBAS is first empirically evaluated on a dialogue task where it is shown to outperform each individual algorithm in most configurations.ESBAS is then adapted to a true online setting where algorithms update their policies after each transition, which we call SSBAS.SSBAS is evaluated on a fruit collection task where it is shown to adapt the stepsize parameter more efficiently than the classical hyperbolic decay, and on an Atari game, where it improves the performance by a wide margin.

Questo articolo formalizza il problema della selezione degli algoritmi online nel contesto del Reinforcement Learning.


In this paper, we present a new generative model for learning latent embeddings.Compared to the classical generative process, where each observed data point is generated from an individual latent variable, our approach assumes a global latent variable to generate the whole set of observed data points.We then propose a learning objective that is derived as an approximation to a lower bound to the data log likelihood, leading to our algorithm, WiSE-ALE.Compared to the standard ELBO objective, where the variational posterior for each data point is encouraged to match the prior distribution, the WiSE-ALE objective matches the averaged posterior, over all samples, with the prior, allowing the sample-wise posterior distributions to have a wider range of acceptable embedding mean and variance and leading to better reconstruction quality in the auto-encoding process.Through various examples and comparison to other state-of-the-art VAE models, we demonstrate that WiSE-ALE has excellent information embedding properties, whilst still retaining the ability to learn a smooth, compact representation.

Proponiamo un nuovo modello con variabili latenti per imparare embedding latenti per alcuni dati ad alta dimensionalità.


We improve the robustness of deep neural nets  to adversarial attacks by using an interpolating function as the output activation.   This data-dependent activation function remarkably improves both classification accuracy and stability to adversarial perturbations.Together with the total variation minimization of adversarial images and augmented training, under the strongest attack, we achieve up to 20.6%, 50.7%, and 68.7% accuracy improvement w.r.t.  the fast gradient sign method, iterative fast gradient sign method, and Carlini-WagnerL2attacks, respectively.  Our defense strategy is additive to many of the existing methods.  We give an intuitive explanation of our defense strategy via analyzing the geometry of the feature space.For reproducibility, the code will be available on GitHub.

Proponiamo strategie per la difesa adversarial basate sulla funzione di attivazione dipendente dai dati, la minimizzazione della variazione totale e training data augmentation


This work presents a scalable solution to continuous visual speech recognition.To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video).In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words.The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set.In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information.Our approach significantly improves on previous lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

Questo lavoro presenta una soluzione scalabile per continuous visual speech recognition.


Previous work (Bowman et al., 2015; Yang et al., 2017) has found difficulty developing generative models based on variational autoencoders (VAEs) for text.To address the problem of the decoder ignoring information from the encoder (posterior collapse), these previous models weaken the capacity of the decoder to force the model to use information from latent variables.However, this strategy is not ideal as it degrades the quality of generated text and increases hyper-parameters.In this paper, we propose a new VAE for text utilizing a multimodal prior distribution, a modified encoder, and multi-task learning.We show our model can generate well-conditioned sentences without weakening the capacity of the decoder.Also, the multimodal prior distribution improves the interpretability of acquired representations.

Proponiamo un modello di autoencoder variazionale per la modellazione del testo senza indebolire il decoder, che migliora la qualità della generazione del testo e l'interpretabilità delle rappresentazioni acquisite.


Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model.Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size.This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy.We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process.We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint.Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy.

Dimostriamo che i modelli grandi ma pruned (large-sparse) superano le loro controparti più piccole ma dense (small-dense) con un'impronta di memoria identica.


Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining.We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM.In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM.Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation.Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency.PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

Controlliamo il topic e il sentimento in text generation (quasi) senza alcun training.


Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering.Such an organization significantly constricts the types of shared structure that can be learned.The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers.The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks.Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains.These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.

Rilassare il vincolo delle gerarchie condivise permette un deep multitask learning più efficace.


We propose a generic framework to calibrate accuracy and confidence (score) of a prediction through stochastic inferences in deep neural networks.We first analyze relation between variation of multiple model parameters for a single example inference and variance of the corresponding prediction scores by Bayesian modeling of stochastic regularization.Our empirical observation shows that accuracy and score of a prediction are highly correlated with variance of multiple stochastic inferences given by stochastic depth or dropout.Motivated by these facts, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores.The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference.Our algorithm presents outstanding confidence calibration performance and improves classification accuracy with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.

Proponiamo un framework per imparare reti calibrate sulla fiducia progettando una nuova loss che incorpora l'incertezza predittiva stimata attraverso inferenze stocastiche.


Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors.This poses challenges to traditional rigid-body physics engines.Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term.In this paper, we propose to learn a particle-based simulator for complex control tasks.Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within.This enables the model to quickly adapt to new environments of unknown dynamics within a few observations.We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world.Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.

Apprendimento della dinamica delle particelle con grafi di interazione dinamica per la simulazione e il controllo di corpi rigidi, oggetti deformabili e fluidi.


Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes.We hypothesize that this problem occurs due to the interaction between two facts: (1) For datasets with large variety, it is likely that the modes lie on separate manifolds.(2) The generator (G) is formulated as a continuous function, and the input noise is derived from a connected set, due to which G's output is a connected set.If G covers all modes, then there must be some portion of G's output which connects them.This corresponds to undesirable, conflated images.We develop theoretical arguments to support these intuitions.We propose a novel method to break the second assumption via learnable discontinuities in the latent noise space.Equivalently, it can be viewed as training several generators, thus creating discontinuities in the G function.We also augment the GAN formulation with a classifier C that predicts which noise partition/generator produced the output images, encouraging diversity between each partition/generator.We experiment on MNIST, celebA, STL-10, and a difficult dataset with clearly distinct modes, and show that the noise partitions correspond to different modes of the data distribution, and produce images of superior quality.

Introduciamo una teoria per spiegare il fallimento delle GAN su dataset complessi e proponiamo una soluzione per risolverlo.


To leverage crowd-sourced data to train multi-speaker text-to-speech (TTS) models that can synthesize clean speech for all speakers, it is essential to learn disentangled representations which can independently control the speaker identity and background noise in generated signals.However, learning such representations can be challenging, due to the lack of labels describing the recording conditions of each training example, and the fact that  speakers and recording conditions are often correlated, e.g. since users often make many recordings using the same equipment.This paper proposes three components to address this problem by: (1) formulating a conditional generative model with factorized latent variables, (2) using data augmentation to add noise that is not correlated with speaker identity and whose label is known during training, and (3) using adversarial factorization to improve disentanglement.Experimental results demonstrate that the proposed method can disentangle speaker and noise attributes even if they are correlated in the training data, and can be used to consistently synthesize clean speech for all speakers.Ablation studies verify the importance of each proposed component.

Data augmentation e adversarial training sono molto efficaci per distinguere il parlante correlato e il rumore, permettendo un controllo indipendente di ogni attributo per la sintesi text-to-speech.


LSTM-based language models exhibit compositionality in their representations, but how this behavior emerges over the course of training has not been explored.Analyzing synthetic data experiments with contextual decomposition, we find that LSTMs learn long-range dependencies compositionally by building them from shorter constituents during training.

Le LSTM imparano le long-range dependency in modo composito costruendole da costituenti più brevi nel corso del training.


Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms.The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants.However, SGD requires a hand-designed schedule for the learning rate.In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule.We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter.Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design.Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step.We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD.We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad.Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while often converging faster.The code is publicly available at https://github.com/oval-group/dfw.

Addestriamo le reti neurali linearizzandole localmente e utilizzando un solutore SVM lineare (Frank-Wolfe) ad ogni iterazione.


In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target-driven navigation using the photorealisticAI2THOR simulator.Specifically, we build on the concept of Universal SuccessorFeatures with an A3C agent.We introduce the novel architectural1contribution of a Successor Feature Dependent Policy (SFDP) and adopt the concept of VariationalInformation Bottlenecks to achieve state of the art performance.VUSFA, our final architecture, is a straightforward approach that can be implemented using our open source repository.Our approach is generalizable, showed greater stability in training, and outperformed recent approaches in terms of transfer learning ability.

Presentiamo una versione migliorata del metodo DRL basato su Universal Successor Feature che può migliorare il transfer learning degli agenti.


A major challenge in learning image representations is the disentangling of the factors of variation underlying the image formation.  This is typically achieved with an autoencoder architecture where a subset of the latent variables is constrained to correspond to specific factors, and the rest of them are considered nuisance variables.This approach has an important drawback: as the dimension of the nuisance variables is increased, image reconstruction is improved, but the decoder has the flexibility to ignore the specified factors, thus losing the ability to condition the output on them.  In this work, we propose to overcome this trade-off by progressively growing the dimension of the latent code, while constraining the Jacobian of the output image with respect to the disentangled variables to remain the same.  As a result, the obtained models are effective at both disentangling and reconstruction.  We demonstrate the applicability of this method in both unsupervised and supervised scenarios for learning disentangled representations.In a facial attribute manipulation task, we obtain high quality image generation while smoothly controlling dozens of attributes with a single model.This is an order of magnitude more disentangled factors than state-of-the-art methods, while obtaining visually similar or superior results, and avoiding adversarial training.

Un metodo per l'apprendimento di rappresentazioni di immagini che sono buone sia per dissociare i fattori di variazione che per ottenere ricostruzioni fedeli.


We propose a new notion of 'non-linearity' of a network layer with respect to an input batch that is based on its proximity to a linear system, which is reflected in the non-negative rank of the activation matrix.We measure this non-linearity by applying non-negative factorization to the activation matrix.Considering batches of similar samples, we find that high non-linearity in deep layers is indicative of memorization.Furthermore, by applying our approach layer-by-layer, we find that the mechanism for memorization consists of distinct phases.We perform experiments on fully-connected and convolutional neural networks trained on several image and audio datasets.Our results demonstrate that as an indicator for memorization, our technique can be used to perform early stopping.

Usiamo il rango non negativo delle matrici di attivazione ReLU come misura di complessità e dimostriamo che è correlato (negativamente) a una buona generalizzazione.


Deep neural networks have achieved state-of-the-art performance in various fields, but they have to be scaled down to be used for real-world applications.As a means to reduce the size of a neural network while preserving its performance, knowledge transfer has brought a lot of attention.One popular method of knowledge transfer is knowledge distillation (KD), where softened outputs of a pre-trained teacher network help train student networks.Since KD, other transfer methods have been proposed, and they mainly focus on loss functions, activations of hidden layers, or additional modules to transfer knowledge well from teacher networks to student networks.In this work, we focus on the structure of a teacher network to get the effect of multiple teacher networks without additional resources.We propose changing the structure of a teacher network to have stochastic blocks and skip connections.In doing so, a teacher network becomes the aggregate of a huge number of paths.In the training phase, each sub-network is generated by dropping stochastic blocks randomly and used as a teacher network.This allows training the student network with multiple teacher networks and further enhances the student network on the same resources in a single teacher network.We verify that the proposed structure brings further improvement to student networks on benchmark datasets.

L'obiettivo di questo articolo è quello di ottenere l'effetto di multiple reti di teacher sfruttando i blocchi stocastici e le skip connection.


Emoji suggestion systems based on typed text have been proposed to encourage emoji usage and enrich text messaging; however, such systems’ actual effects on the chat experience remain unknown.We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared these in two different studies.To investigate the effect of emoji suggestion in online conversations, we conducted a laboratory text-messaging study with 24 participants, and also a 15-day longitudinal field deployment with 18 participants.We found that lexical emoji suggestions increased emoji usage by 31.5% over a keyboard without suggestions, while semantic suggestions increased emoji usage by 125.1%.However, suggestion mechanisms did not affect the chatting experience significantly.From these studies, we formulate a set of design guidelines for future emoji suggestion systems that better support users’ needs.

Abbiamo costruito una tastiera Android con capacità di suggerimento emoji sia lessicale (basato sulle parole) che semantico (basato sul significato) e abbiamo confrontato i loro effetti in due diversi studi di chat.


Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples.To address the issues, we propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generation.In contrast to standard GANs that use a binary classifier as its discriminator to predict whether a sample is real or generated, SAL employs a comparative discriminator which is a pairwise classifier for comparing the text quality between a pair of samples.During training, SAL rewards the generator when its currently generated sentence is found to be better than its previously generated samples.This self-improvement reward mechanism allows the model to receive credits more easily and avoid collapsing towards the limited number of real samples, which not only helps alleviate the reward sparsity issue but also reduces the risk of mode collapse.Experiments on text generation benchmark datasets show that our proposed approach substantially improves both the quality and the diversity, and yields more stable performance compared to the previous GANs for text generation.

Proponiamo un paradigma di self-adversarial learning (SAL) che migliora il generatore usando il self-play per migliorare le prestazioni delle GAN nella generazione del testo.


Determining the number of latent dimensions is a ubiquitous problem in machinelearning.In this study, we introduce a novel method that relies on SVD to discoverthe number of latent dimensions.The general principle behind the method is tocompare the curve of singular values of the SVD decomposition of a data set withthe randomized data set curve.The inferred number of latent dimensions correspondsto the crossing point of the two curves.To evaluate our methodology, wecompare it with competing methods such as Kaisers eigenvalue-greater-than-onerule (K1), Parallel Analysis (PA), Velicers MAP test (Minimum Average Partial).We also compare our method with the Silhouette Width (SW) technique which isused in different clustering methods to determine the optimal number of clusters.The result on synthetic data shows that the Parallel Analysis and our method havesimilar results and more accurate than the other methods, and that our methods isslightly better result than the Parallel Analysis method for the sparse data sets.

In questo studio, introduciamo un nuovo metodo che si basa su SVD per scoprire il numero di dimensioni latenti.


Deep learning models are often sensitive to adversarial attacks, where carefully-designed input samples can cause the system to produce incorrect decisions.Here we focus on the problem of detecting attacks, rather than robust classification, since detecting that an attack occurs may be even more important than avoiding misclassification.We build on advances in explainability, where activity-map-like explanations are used to justify and validate decisions, by highlighting features that are involved with a classification decision.The key observation is that it is hard to create explanations for incorrect decisions. We propose EXAID, a novel attack-detection approach, which uses model explainability to identify images whose explanations are inconsistent with the predicted class. Specifically, we use SHAP, which uses Shapley values in the space of the input image, to identify which input features contribute to a class decision. Interestingly, this approach does not require to modify the attacked model, and it can be applied without modelling a specific attack. It can therefore be applied successfully to detect unfamiliar attacks, that were unknown at the time the detection model was designed. We evaluate EXAID on two benchmark datasets CIFAR-10 and SVHN, and against three leading attack techniques, FGSM, PGD and C&W.We find that EXAID improves over the SoTA detection methods by a large margin across a wide range of noise levels, improving detection from 70% to over 90% for small perturbations.

Un nuovo approccio di adversarial detection, che utilizza metodi di explainability per identificare le immagini le cui spiegazioni non sono coerenti con la classe prevista.


We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a “latent” space, amounting to a reparameterization.This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using a simple arithmetic coder after training.Classification accuracy and model compressibility is maximized jointly, with the bitrate--accuracy trade-off specified by a hyperparameter.We evaluate the method on the MNIST, CIFAR-10 and ImageNet classification benchmarks using six distinct model architectures.Our results show that state-of-the-art model compression can be achieved in a scalable and general way without requiring complex procedures such as multi-stage training.

Un metodo di compressione del modello addestrabile end-to-end che ottimizza la precisione insieme alla dimensione prevista del modello.


Neural networks can converge faster with help from a smarter batch selection strategy.In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to the learning progress of the model.Our key idea is to present confusing samples what the true label is.Thus, the samples near the current decision boundary are considered as the most effective to expedite convergence.Taking advantage of our design, Ada-Boundary maintains its dominance in various degrees of training difficulty.We demonstrate the advantage of Ada-Boundary by extensive experiments using two convolutional neural networks for three benchmark data sets.The experiment results show that Ada-Boundary improves the training time by up to 31.7% compared with the state-of-the-art strategy and by up to 33.5% compared with the baseline strategy.

Suggeriamo una tecnica di selezione intelligente delle batch chiamata Ada-Boundary.


State of the art sound event classification relies in neural networks to learn the associations between class labels and audio recordings within a dataset.These datasets typically define an ontology to create a structure that relates these sound classes with more abstract super classes.Hence, the ontology serves as a source of domain knowledge representation of sounds.However, the ontology information is rarely considered, and specially under explored to model neural network architectures.We propose two ontology-based neural network architectures for sound event classification.We defined a framework to design simple network architectures that preserve an ontological structure.The networks are trained and evaluated using two of the most common sound event classification datasets.Results show an improvement in classification performance demonstrating the benefits of including the ontological information.

Presentiamo architetture di reti neurali basate sull'ontologia per la classificazione degli eventi sonori.


In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent.An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image.Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so.In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks.A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs.

Il nostro lavoro mostra che le informazioni posizionali sono state implicitamente codificate in una rete. Queste informazioni sono importanti per rilevare le feature dipendenti dalla posizione, ad esempio la semantica e la salienza.


Semantic parsing which maps a natural language sentence into a formal machine-readable representation of its meaning, is highly constrained by the limited annotated training data.Inspired by the idea of coarse-to-fine, we propose a general-to-detailed neural network(GDNN) by incorporating cross-domain sketch(CDS) among utterances and their logic forms.For utterances in different domains, the General Network will extract CDS using an encoder-decoder model in a multi-task learning setup.Then for some utterances in a specific domain, the Detailed Network will generate the detailed target parts using sequence-to-sequence architecture with advanced attention to both utterance and generated CDS.Our experiments show that compared to direct multi-task learning, CDS has improved the performance in semantic parsing task which converts users' requests into meaning representation language(MRL).We also use experiments to illustrate that CDS works by adding some constraints to the target decoding process, which further proves the effectiveness and rationality of CDS.

General-to-detailed neural network (GDNN) con multi-task learning incorporando il cross-domain sketch (CDS) per il semantic parsing


The learnability of different neural architectures can be characterized directly by computable measures of data complexity.In this paper, we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data, beyond inductive bias.After suggesting algebraic topology as a measure for data complexity, we show that the power of a network to express the topological complexity of a dataset in its decision boundary is a strictly limiting factor in its ability to generalize.We then provide the first empirical characterization of the topological capacity of neural networks.Our empirical analysis shows that at every level of dataset complexity, neural networks exhibit topological phase transitions and stratification.This observation allowed us to connect existing theory to empirically driven conjectures on the choice of architectures for a single hidden layer neural networks.

Mostriamo che la capacità di apprendimento di diverse architetture neurali può essere caratterizzata direttamente da misure calcolabili di complessità dei dati.


Challenges in natural sciences can often be phrased as optimization problems.Machine learning techniques have recently been applied to solve such problems.One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space.We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA.We show that our algorithm outperforms other generative models in optimization tasks.We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles

Affrontare la progettazione inversa tramite algoritmi genetici aumentati con deep neural network.


Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art results in a variety of Natural Language Processing tasks.However, understanding of their internal functioning is still insufficient and unsatisfactory.In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states.Unlike previous research, which mainly focuses on explaining Transformer models by their \hbox{attention} weights, we argue that hidden states contain equally valuable information.Specifically, our analysis focuses on models fine-tuned on the task of Question Answering (QA) as an example of a complex downstream task.We inspect how QA models transform token vectors in order to find the correct answer.To this end, we apply a set of general and QA-specific probing tasks that reveal the information stored in each representation layer.Our qualitative analysis of hidden state visualizations provides additional insights into BERT's reasoning process.Our results show that the transformations within BERT go through phases that are related to traditional pipeline tasks.The system can therefore implicitly incorporate task-specific information into its token representations.Furthermore, our analysis reveals that fine-tuning has little impact on the models' semantic abilities and that prediction errors can be recognized in the vector representations of even early layers.

Studiamo le attivazioni degli hidden state dei transformer nei task di question answering.


We propose a general deep reinforcement learning method and apply it to robot manipulation tasks.Our approach leverages demonstration data to assist a reinforcement learning agent in learning to solve a wide range of tasks, mainly previously unsolved.We train visuomotor policies end-to-end to learn a direct mapping from RGB camera inputs to joint velocities.Our experiments indicate that our reinforcement and imitation approach can solve contact-rich robot manipulation tasks that neither the state-of-the-art reinforcement nor imitation learning method can solve alone.We also illustrate that these policies achieved zero-shot sim2real transfer by training with large visual and dynamics variations.

Combinare il reinforcement learning e l'imitation learning per risolvere complessi task di manipolazione dei robot dai pixel


Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks.However, an ensemble's cost for both training and testing increases linearly with the number of networks.In this paper, we propose BatchEnsemble, an ensemble method whose computational and memory costs are significantly lower than typical ensembles.BatchEnsemble achieves this by defining each weight matrix to be the Hadamard product of a shared weight among all ensemble members and a rank-one matrix per member.Unlike ensembles, BatchEnsemble is not only parallelizable across devices, where one device trains one member, but also parallelizable within a device, where multiple ensemble members are updated simultaneously for a given mini-batch.Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and contextual bandits tasks, BatchEnsemble yields competitive accuracy and uncertainties as typical ensembles; the speedup at test time is 3X and memory reduction is 3X at an ensemble of size 4.We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs.We further show that BatchEnsemble can easily scale up to lifelong learning on Split-ImageNet which involves 100 sequential learning tasks.

Abbiamo introdotto BatchEnsemble, un metodo efficiente per l'ensembling e il lifelong learning che può essere usato per migliorare la precisione e l'incertezza di qualsiasi rete neurale come i tipici metodi di ensemble.


Reinforcement learning typically requires carefully designed reward functions in order to learn the desired behavior.We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.The optimal state trajectories are used to learn a generative or predictive model of the “good” states distribution.The reward signal is computed by a function of the difference between the actual next state acquired by the agent and the predicted next state given by the learned generative or predictive model.With this inferred reward function, we perform standard reinforcement learning in the inner loop to guide the agent to learn the given task.Experimental evaluations across a range of tasks demonstrate that the proposed method produces superior performance compared to standard reinforcement learning with both complete or sparse hand engineered rewards.Furthermore, we show that our method successfully enables an agent to learn good actions directly from expert player video of games such as the Super Mario Bros and Flappy Bird.


Deep neural networks have achieved impressive performance in handling complicated semantics in natural language, while mostly treated as black boxes.To explain how the model handles compositional semantics of words and phrases, we study the hierarchical explanation problem.We highlight the key challenge is to compute non-additive and context-independent importance for individual words and phrases.We show some prior efforts on hierarchical explanations, e.g. contextual decomposition,  do not satisfy the desired properties mathematically, leading to inconsistent explanation quality in different models.In this paper, we propose a formal way to quantify the importance of each word or phrase to generate hierarchical explanations.We modify contextual decomposition algorithms according to our formulation, and propose a model-agnostic explanation algorithm with competitive performance.Human evaluation and automatic metrics evaluation on both LSTM models and fine-tuned BERT Transformer models on multiple datasets show that our algorithms robustly outperform prior works on hierarchical explanations.We show our algorithms help explain compositionality of semantics, extract classification rules, and improve human trust of models.

Proponiamo la misurazione dell'importanza della frase e algoritmi per la spiegazione gerarchica delle previsioni del modello di sequenza neurale


Stochastic gradient descent (SGD) with stochastic momentum is popular in nonconvex stochastic optimization and particularly for the training of deep neural networks.In standard SGD, parameters are updated by improving along the path of the gradient at the current iterate on a batch of examples, where the addition of a ``momentum'' term biases the update in the direction of the previous change in parameters.In non-stochastic convex optimization one can show that a momentum adjustment provably reduces convergence time in many settings, yet such results have been elusive in the stochastic and non-convex settings.At the same time, a widely-observed empirical phenomenon is that in training deep networks stochastic momentum appears to significantly improve convergence time, variants of it have flourished in the development of other popular update methods, e.g. ADAM, AMSGrad, etc.Yet theoretical justification for the use of stochastic momentum has remained a significant open question.In this paper we propose an answer: stochastic momentum improves deep network training because it modifies SGD to escape saddle points faster and, consequently, to more quickly find a second order stationary point.Our theoretical results also shed light on the related question of how to choose the ideal momentum parameter--our analysis suggests that $\beta \in [0,1)$ should be large (close to 1), which comports with empirical findings.We also provide experimental findings that further validate these conclusions.

Un parametro $\beta$ di momentum più alto aiuta a sfuggire più velocemente ai punti di sella


GANs provide a framework for training generative models which mimic a data distribution.However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images.In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction.Here, we develop a system for efficiently training a GAN to increase a generic rate of positive user interactions, for example aesthetic ratings.To do this, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model.As a proof of concept, we demonstrate that this system is successful at improving positive interaction rates simulated from a variety of objectives, and characterize s

Descriviamo come migliorare un modello generativo per le immagini secondo un obiettivo lento o difficile da valutare, come il feedback umano, che potrebbe avere molte applicazioni, come ad esempio generare immagini più estetiche.


Semi-supervised learning (SSL) is a study that efficiently exploits a large amount of unlabeled data to improve performance in conditions of limited labeled data.Most of the conventional SSL methods assume that the classes of unlabeled data are included in the set of classes of labeled data.In addition, these methods do not sort out useless unlabeled samples and use all the unlabeled data for learning, which is not suitable for realistic situations.In this paper, we propose an SSL method called selective self-training (SST), which selectively decides whether to include each unlabeled sample in the training process.It is also designed to be applied to a more real situation where classes of unlabeled data are different from the ones of the labeled data.For the conventional SSL problems which deal with data where both the labeled and unlabeled samples share the same class categories, the proposed method not only performs comparable to other conventional SSL algorithms but also can be combined with other SSL algorithms.While the conventional methods cannot be applied to the new SSL problems where the separated data do not share the classes, our method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.

Il nostro algoritmo proposto non usa tutti i dati non annotati per il training, ma li usa selettivamente.


Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions.Conditional generation enables interactive control, but creating new controls often requires expensive retraining.In this paper, we develop a method to condition generation without retraining the model.By post-hoc learning latent constraints, value functions identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions.Combining attribute constraints with a universal “realism” constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder.Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image.Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function.

Un nuovo approccio alla generazione condizionale vincolando lo spazio latente di un modello generativo incondizionato.


Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data.These models have obtained notable gains in accuracy across many NLP tasks.However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption.As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP.Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.

Quantifichiamo il costo energetico in termini di denaro (crediti cloud) e l'impronta di carbonio della training di modelli di reti neurali di recente successo per il NLP. I costi sono alti.


Many models based on the Variational Autoencoder are proposed to achieve disentangled latent variables in inference.However, most current work is focusing on designing powerful disentangling regularizers, while the given number of dimensions for the latent representation at initialization could severely inﬂuence the disentanglement.Thus, a pruning mechanism is introduced, aiming at automatically seeking for the intrinsic dimension of the data while promoting disentangled representations.The proposed method is validated on MPI3D and MNIST to be advancing state-of-the-art methods in disentanglement, reconstruction, and robustness.The code is provided on the https://github.com/WeyShi/FYP-of-Disentanglement.

Il Pruning VAE è proposto per cercare le variabili disentangled con dimensione intrinseca.


We explore the properties of byte-level recurrent language models.When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts.Specifically, we find a single unit which performs sentiment analysis.These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank.They are also very data efficient.When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets.We also demonstrate the sentiment unit has a direct influence on the generative process of the model.Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.

I language model ricorrenti a livello di byte imparano rappresentazioni di testo di alta qualità specifiche del dominio.


Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons:1) branching on the samples within the model, and2) the lack of a pathwise derivative for the samples.While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model.Here, we revisit the Reweighted Wake Sleep (RWS; Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models.Moreover, we observe that, unlike the Importance-weighted Autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well.Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.

Analisi empirica e spiegazione degli stimatori di gradiente particle-based per l'inferenza approssimativa con deep generative model.


The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.Unfortunately, state-of-the-art deep extreme classifiers are either not scalable or inaccurate for short text documents. This paper develops the DeepXML algorithm which addresses both limitations by introducing a novel architecture that splits training of head and tail labels.  DeepXML increases accuracyby (a) learning word embeddings on head labels and transferring them through a novel residual connection to data impoverished tail labels; (b) increasing the amount of negative training data available by extending state-of-the-art negative sub-sampling techniques; and(c) re-ranking the set of predicted labels to eliminate the hardest negatives for the original classifier.All of these contributions are implemented efficiently by extending the highly scalable Slice algorithm for pretrained embeddings to learn the proposed DeepXML architecture.As a result, DeepXML could efficiently scale to problems involving millions of labels that were beyond the pale of state-of-the-art deep extreme classifiers as it could be more than 10x faster at training than XML-CNN and AttentionXML.At the same time, DeepXML was also empirically determined to be up to 19% more accurate than leading techniques for matching search engine queries to advertiser bid phrases.

Deep learning multilabel scalabile e accurato con milioni di label.


Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science.Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability.In this paper, we establish an intriguing connection between f-GANs and various depth functions through the lens of f-Learning.Similar to the derivation of f-GAN, we show that these depth functions that lead to rate-optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning.This connection opens the door of computing robust estimators using tools developed for training GANs.In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model.Interestingly, the hidden layers of the neural net structure in the discriminator class are shown to be necessary for robust estimation.

Si dimostra che le GAN ci forniscono una nuova stima robusta ed efficace della media contro le contaminazioni agnostiche con un'ottimalità statistica e una trattabilità pratica.


Long Short-Term Memory (LSTM) is one of the most powerful sequence models.Despite the strong performance, however, it lacks the nice interpretability as in state space models.In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \cite{zaheer2017latent} of combining topic models with LSTM.However, unlike \cite{zaheer2017latent}, we do not make any factorization assumptions in our inference algorithm.We present an efficient sampler based on sequential Monte Carlo (SMC) method that draws from the joint posterior directly.Experimental results confirms the superiority and stability of this SMC inference algorithm on a variety of domains.

Presentiamo modelli State Space LSTM, una combinazione di modelli State Space e LSTM, e proponiamo un algoritmo di inferenza basato su Monte Carlo sequenziale.


Given a large database of concepts but only one or a few examples of each, can we learn models for each concept that are not only generalisable, but interpretable?In this work, we aim to tackle this problem through hierarchical Bayesian program induction.We present a novel learning algorithm which can infer concepts as short, generative, stochastic programs, while learning a global prior over programs to improve generalisation and a recognition network for efficient inference.Our algorithm, Wake-Sleep-Remember (WSR), combines gradient learning for continuous parameters with neurally-guided search over programs.We show that WSR learns compelling latent programs in two tough symbolic domains: cellular automata and Gaussian process kernels.We also collect and evaluate on a new dataset, Text-Concepts, for discovering structured patterns in natural text data.

Estendiamo l'algoritmo wake-sleep e lo usiamo per imparare modelli strutturati da pochi esempi.


The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes.Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation.The laboratory experiments take a considerable amount of time for annotation of the sequences.This arises the need to use computational techniques to classify proteins based on their functions.In our work, we have collected the data from Swiss-Prot containing 40433 proteins which is grouped into 30 families.We pass it to recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) model and compare it by applying trigram with deep neural network and shallow neural network on the same dataset.Through this approach, we could achieve maximum of around 78% accuracy for the classification of protein families.

Proteine, sequenze di amminoacidi, machine learning, deep learning, rete neurale ricorrente (RNN), long short term memory (LSTM), gated recurrent unit (GRU), deep neural network


Spoken term detection (STD) is the task of determining whether and where a given word or phrase appears in a given segment of speech.Algorithms for STD are often aimed at maximizing the gap between the scores of positive and negative examples.As such they are focused on ensuring that utterances where the term appears are ranked higher than utterances where the term does not appear.However, they do not determine a detection threshold between the two.In this paper, we propose a new approach for setting an absolute detection threshold for all terms by introducing a new calibrated loss function.The advantage of minimizing this loss function during training is that it aims at maximizing not only the relative ranking scores, but also adjusts the system to use a fixed threshold and thus enhances system robustness and maximizes the detection accuracy rates.We use the new loss function in the structured prediction setting and extend the discriminative keyword spotting algorithm for learning the spoken term detector with a single threshold for all terms.We further demonstrate the effectiveness of the new loss function by applying it on a deep neural Siamese network in a weakly supervised setting for template-based spoken term detection, again with a single fixed threshold.Experiments with the TIMIT, WSJ and Switchboard corpora showed that our approach not only improved the accuracy rates when a fixed threshold was used but also obtained higher Area Under Curve (AUC).

Spoken term detection, utilizzando la predizione strutturata e le deep network, implementando una nuova loss che massimizza l'AUC e classifica secondo una soglia predefinita.


Federated learning involves jointly learning over massively distributed partitions of data generated on remote devices.Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices.In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by resource allocation strategies in wireless networks that encourages a more fair accuracy distribution across devices in federated networks.To solve q-FFL, we devise a scalable method, q-FedAvg, that can run in federated networks.We validate both the improved fairness and flexibility of q-FFL and the efficiency of q-FedAvg through simulations on federated datasets.

Proponiamo un nuovo obiettivo di ottimizzazione che incoraggia la fairness nelle federated network eterogenee, e sviluppiamo un metodo scalabile per risolverlo.


We propose a novel autoencoding model called Pairwise Augmented GANs.We train a generator and an encoder jointly and in an adversarial manner.The generator network learns to sample realistic objects.In turn, the encoder network at the same time is trained to map the true data distribution to the prior in  latent space.To ensure good reconstructions, we introduce an augmented adversarial reconstruction loss.Here we train a discriminator to distinguish two types of pairs: an object with its augmentation and the one with its reconstruction.We show that such adversarial loss compares objects based on the content rather than on the exact match.We experimentally demonstrate that our model generates samples and reconstructions of quality competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and achieves good quantitative results on CIFAR10.

Proponiamo un nuovo modello di autoencoding con loss di ricostruzione adversarial aumentata. Induciamo una nuova metrica per la valutazione basata sul contenuto delle ricostruzioni.


We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables.Inspired by dropout, a popular tool for regularization and model ensemble, we assign sparse priors to the weights in deep neural networks (DNN) in order to achieve automatic “dropout” and avoid over-fitting.By alternatively sampling from posterior distribution through stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and optimizing latent variables via stochastic approximation (SA), the trajectory of the target weights is proved to converge to the true posterior distribution conditioned on optimal latent variables.This ensures a stronger regularization on the over-fitted parameter space and more accurate uncertainty quantification on the decisive variables.Simulations from large-p-small-n regressions showcase the robustness of this method when applied to models with latent variables.Additionally, its application on the convolutional neural networks (CNN) leads to state-of-the-art performance on MNIST and Fashion MNIST datasets and improved resistance to adversarial attacks.

Un robusto algoritmo di deep learning bayesiano per dedurre posteriori complesse con variabili latenti


Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions.This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions.Here, we developed an analytical method to factorize a large number of retinal ganglion cells' spike trains into a robust low-dimensional representation that captures efficiently both their spatial and temporal information.In particular, we extended previously used single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity.On data recorded from retinal ganglion cells with strong pre-stimulus baseline, we showed that in situations were the stimulus elicits a strong change in firing rate, our extensions yield a boost in stimulus decoding performance.Our results thus suggest that taking into account the baseline can be important for finding a compact information-rich representation of neural activity.

Abbiamo esteso la decomposizione tensoriale space-by-time single-trial basata su non-negative matrix factorization per scontare in modo efficiente l'attività della baseline pre-stimolo che migliora le prestazioni di decodifica sui dati con baseline non trascurabili.


We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods.This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operates on the full-resolution image and is trained in combination with a multi-scale discriminator.Additionally, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, therefore only requiring the storage of the preserved region and the semantic label map.A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.

Metodo di compressione dell'immagine estrema basato su GAN che utilizza meno della metà dei bit del codec ingegnerizzato SOTA mantenendo la qualità visiva


In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users.Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss.The list is then sorted in the descending order of the scores.Possible interactions between items present in the same list are taken into account in the training phase at the loss level.However, during inference, items are scored individually, and possible interactions between them are not considered.In this paper, we propose a context-aware neural network model that learns item scores by applying a self-attention mechanism.The relevance of a given item is thus determined in the context of all other items present in the list, both in training and in inference.Finally, we empirically demonstrate significant performance gains of self-attention based neural architecture over Multi-Layer Perceptron baselines.This effect is consistent across popular pointwise, pairwise and listwise losses on datasets with both implicit and explicit relevance feedback.

Learning to rank usando l'architettura Transformer.


We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies : Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB).The learner can generalize over its experience to continuously learn new outcomes, by choosing actively what and how to learn guided by empirical measures of its own progress.In this paper, we are considering the learning of a set of interrelated complex outcomes hierarchically organized.We introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly more complex motor policies (combinations of primitive motor policies).Our architecture can actively decide which outcome to focus on and which exploration strategy to apply.Those strategies could be autonomous exploration, or active social guidance, where it relies on the expertise of a human teacher providing demonstrations at the learner's request.We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand.We also show that our "procedures" increases the agent's capability to learn complex tasks.

L'articolo descrive un algoritmo di apprendimento strategico intrinsecamente motivato che affronta l'apprendimento di policy motorie complesse.


Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains.In this work, we introduceTPO, a tree search based policy optimization method for continuous environments.TPO takes a hybrid approach to policy optimization.  Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial.To overcome these challenges, we propose limiting tree search branching factor by drawing only few action samples from the policy distribution and define a new loss function based on the trajectories’ mean and standard deviations.  Our approach led to some non-intuitive findings.  MCTS training generally requires a large number of samples and simulations.However, we observed that bootstrappingtree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few number of simulations.Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it computationally and prohibitively expensive.In our experiments, we use PPO as our baseline policy optimization algorithm.TPO significantly improves the policy on nearly all of our benchmarks.  For example, in complex environments such as Humanoid, we achieve a 2.5×improvement over the baseline algorithm.

Usiamo MCTS per ottimizzare ulteriormente una policy bootstrapped per spazi d'azione continui sotto un setting di iterazione della policy.


The variational autoencoder (VAE) has found success in modelling the manifold of natural images on certain datasets, allowing meaningful images to be generated while interpolating or extrapolating in the latent code space, but it is unclear whether similar capabilities are feasible for text considering its discrete nature.In this work, we investigate the reason why unsupervised learning of controllable representations fails for text.We find that traditional sequence VAEs can learn disentangled representations through their latent codes to some extent, but they often fail to properly decode when the latent factor is being manipulated, because the manipulated codes often land in holes or vacant regions in the aggregated posterior latent space, which the decoding network is not trained to process.Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex.Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text.Empirically, our method significantly outperforms unsupervised baselines and is competitive with strong supervised approaches on text style transfer.Furthermore, when switching the latent factor (e.g., topic) during a long sentence generation, our proposed framework can often complete the sentence in a seemingly natural way -- a capability that has never been attempted by previous methods.

Perché i precedenti VAE sul testo non possono imparare una rappresentazione latente controllabile come sulle immagini, ed un fix per permettere il primo successo verso la generazione controllata di testo senza supervisione


In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet) to understand how spatiotemporal memories might be learned and encoded in a representational hierarchy for predicting future video frames.The model is inspired by the feedforward, feedback and lateral recurrent circuits in the mammalian hierarchical visual system.It assumes that spatiotemporal memories are encoded in the recurrent connections within each level and between different levels of the hierarchy.The model contains a feed-forward path that  computes and encodes spatiotemporal features of successive complexity and a feedback path that projects interpretation from a higher level to the level below.Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit that integrates their signals as well as the circuit's internal memory states to generate  a prediction of the incoming signals.The network learns by comparing the incoming signals with its prediction, updating its internal model of the world by minimizing the prediction errors at each level of the hierarchy in the style of {\em predictive self-supervised learning}. The network processes data in blocks of video  frames rather than  a frame-to-frame basis.  This allows it to learn relationships among movement patterns, yielding state-of-the-art performance in long range video sequence predictions in benchmark datasets.We observed that hierarchical interaction in the network introduces sensitivity to memories of global movement patterns even in the population representation of the units in the earliest level.Finally, we provided neurophysiological evidence, showing that neurons in the early visual cortex of awake monkeys exhibit very similar sensitivity and behaviors.These findings suggest that  predictive self-supervised learning might be an important principle for representational learning in the visual cortex.

Un nuovo modello corticale gerarchico per la codifica della memoria spazio-temporale e la predizione video


Saliency maps are often used to suggest explanations of the behavior of deep rein- forcement learning (RL) agents.However, the explanations derived from saliency maps are often unfalsifiable and can be highly subjective.We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses generated from saliency maps and show that explanations suggested by saliency maps are often not supported by experiments.Our experiments suggest that saliency maps are best viewed as an exploratory tool rather than an explanatory tool.

Proponendo una nuova metodologia basata sul controfattuale per valutare le ipotesi generate dalle mappe di saliency sul comportamento dell'agente di deep RL.


One of the unresolved questions in deep learning is the nature of the solutions that are being discovered.We investigate the collection of solutions reached by the same network architecture, with different random initialization of weights and random mini-batches.These solutions are shown to be rather similar - more often than not, each train and test example is either classified correctly by all the networks, or by none at all.  Surprisingly, all the network instances seem to share the same learning dynamics, whereby initially the same train and test examples are correctly recognized by the learned model, followed by other examples which are learned in roughly the same order.When extending the investigation to heterogeneous collections of neural network architectures, once again examples are seen to be learned in the same order irrespective of architecture, although the more powerful architecture may continue to learn and thus achieve higher accuracy.This pattern of results remains true even when the composition of classes in the test set is unrelated to the train set, for example, when using out of sample natural images or even artificial images.To show the robustness of these phenomena we provide an extensive summary of our empirical study, which includes hundreds of graphs describing tens of thousands of networks with varying NN architectures, hyper-parameters and domains.We also discuss cases where this pattern of similarity breaks down, which show that the reported similarity is not an artifact of optimization by gradient descent.Rather, the observed pattern of similarity is characteristic of learning complex problems with big networks.Finally, we show that this pattern of similarity seems to be strongly correlated with effective generalization.

La maggior parte delle reti neurali approssima la stessa funzione di classificazione, anche tra le architetture, attraverso tutte le fasi di apprendimento.


We propose a unified framework for building unsupervised representations of individual objects or entities (and their compositions), by associating with each object both a distributional as well as a point estimate (vector embedding).This is made possible by the use of optimal transport, which allows us to build these associated estimates while harnessing the underlying geometry of the ground space.Our method gives a novel perspective for building rich and powerful feature representations that simultaneously capture uncertainty (via a distributional estimate) and interpretability (with the optimal transport map).As a guiding example, we formulate unsupervised representations for text, in particular for sentence representation and entailment detection.Empirical results show strong advantages gained through the proposed framework.This approach can be used for any unsupervised or supervised problem (on text or other modalities) with a co-occurrence structure, such as any sequence data.The key tools underlying the framework are Wasserstein distances and Wasserstein barycenters (and, hence the title!).

Rappresentate ogni entità in base al suo istogramma di contesti e poi Wasserstein è tutto ciò di cui avete bisogno!


In this paper, we propose two methods, namely Trace-norm regression (TNR) and Stable Trace-norm Analysis (StaTNA), to improve performances of recommender systems with side information.Our trace-norm regression approach extracts low-rank latent factors underlying the side information that drives user preference under different context.Furthermore, our novel recommender framework StaTNA not only captures latent low-rank common drivers for user preferences, but also considers idiosyncratic taste for individual users.We compare performances of TNR and StaTNA on the MovieLens datasets against state-of-the-art models, and demonstrate that StaTNA and TNR in general outperforms these methods.

Metodologie per reccomender system con informazioni laterali basate sulla regolarizzazione delle trace-norm


This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games.Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text.These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents.Moreover, they provide a learning environment in which these skills can be acquired through interactions with an environment rather than using fixed corpora. One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space.Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions.In this work, we propose to use the exploration approach of Go-Explore (Ecoffet et al., 2019) for solving text-based games.More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories.Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment.Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.

Questo lavoro presenta un agente basato sull'esplorazione e imitation learning capace di prestazioni allo stato dell'arte nel giocare a giochi per computer text-based.


The recent “Lottery Ticket Hypothesis” paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keep the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights.The performance of these networks often exceeds the performance of the non-sparse base model, but for reasons that were not well understood.In this paper we study the three critical components of the Lottery Ticket (LT) algorithm, showing that each may be varied significantly without impacting the overall results.Ablating these factors leads to new insights for why LT networks perform as well as they do.We show why setting weights to zero is important, how signs are all you need to make the re-initialized network train, and why masking behaves like training.Finally, we discover the existence of Supermasks, or masks that can be applied to an untrained, randomly initialized network to produce a model with performance far better than chance (86% on MNIST, 41% on CIFAR-10).

Nel pruning delle reti neurali, l'azzeramento dei pesi pruned è importante, il segno dell'inizializzazione è fondamentale, e il masking può essere pensato come un training.


Fine-tuning with pre-trained models has achieved exceptional results for many language tasks.In this study, we focused on one such self-attention network model, namely BERT, which has performed well in terms of stacking layers across diverse language-understanding benchmarks.However, in many downstream tasks, information between layers is ignored by BERT for fine-tuning.In addition, although self-attention networks are well-known for their ability to capture global dependencies, room for improvement remains in terms of emphasizing the importance of local contexts.In light of these advantages and disadvantages, this paper proposes SesameBERT, a generalized fine-tuning method that (1) enables the extraction of global information among all layers through Squeeze and Excitation and (2) enriches local information by capturing neighboring contexts via Gaussian blurring.Furthermore, we demonstrated the effectiveness of our approach in the HANS dataset, which is used to determine whether models have adopted shallow heuristics instead of learning underlying generalizations.The experiments revealed that SesameBERT outperformed BERT with respect to GLUE benchmark and the HANS evaluation set.

Abbiamo proposto SesameBERT, un metodo generalizzato di fine-tuning che permette l'estrazione di informazioni globali tra tutti i layer attraverso Squeeze and Excitation e arricchisce le informazioni locali catturando i contesti vicini attraverso il blurring gaussiano.


A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel – from GANs and intrinsic curiosity to multi-agent RL.Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others’ updates.Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner’s Dilemma.Although experimentally successful, we show that LOLA agents can exhibit ‘arrogant’ behaviour directly at odds with convergence.In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games.In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead.We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games.SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.

L'opponent shaping è un potente approccio all'apprendimento multi-agente, ma può impedire la convergenza; il nostro algoritmo SOS risolve questo problema con forti garanzie in tutti i giochi differenziabili.


The rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them, leaving many questions unanswered or inadequately answered.Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema.While many research efforts have focused on the problem of general question similarity, these approaches do not generalize well to the medical domain, where medical expertise is often required to determine semantic similarity.In this paper, we show how a semi-supervised approach of pre-training a neural network on medical question-answer pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity.While other pre-training tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, an accuracy of 80.0% with a much smaller training set, and an accuracy of 84.5% when the full corpus of medical question-answer data is used.

Mostriamo che il question-answer matching è un task di pre-training particolarmente buono per question-similarity e rilasciamo un dataset per question-similarity medica.


We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning.We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them resulting in a more effective feature extraction compared to learning a single task.Intuitively, the resulting set of features offers performance benefits when used by Reinforcement Learning algorithms.We prove this by providing theoretical guarantees that highlight the conditions for which is convenient to share representations among tasks, extending the well-known finite-time bounds of Approximate Value-Iteration to the multi-task setting.In addition, we complement our analysis by proposing multi-task extensions of three Reinforcement Learning algorithms that we empirically evaluate on widely used Reinforcement Learning benchmarks showing significant improvements over the single-task counterparts in terms of sample efficiency and performance.

Uno studio sul beneficio della condivisione della rappresentazione nel multi-task reinforcement learning.


We present a 3D capsule architecture for processing of point clouds that is equivariant with respect to the SO(3) rotation group, translation and permutation of the unordered input sets.The network operates on a sparse set of local reference frames, computed from an input point cloud and establishes end-to-end equivariance through a novel 3D quaternion group capsule layer, including an equivariant dynamic routing procedure.The capsule layer enables us to disentangle geometry from pose, paving the way for more informative descriptions and a structured latent space.In the process, we theoretically connect the process of dynamic routing between capsules to the well-known Weiszfeld algorithm, a scheme for solving iterative re-weighted least squares (IRLS) problems with provable convergence properties, enabling robust pose estimation between capsule layers.Due to the sparse equivariant quaternion capsules, our architecture allows joint object classification and orientation estimation, which we validate empirically on common benchmark datasets.

Deep architecture per nuvole di punti 3D che sono equivarianti alle rotazioni SO(3), così come alle traslazioni e alle permutazioni.