We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world.We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body.That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals.Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape.We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control.We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world.Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.

Addestriamo modelli predittivi sulle informazioni propriocettive e dimostriamo che rappresentano le proprietà degli oggetti esterni.


Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.The hierarchical architecture consisting of multiple GANs preserves both local and global topological features, and automatically partitions the input graph into representative stages for feature learning.The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures.Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages.This paper contains original research on combining the use of GANs and graph topological analysis.

Un metodo basato su GAN per imparare importanti feature topologiche di un grafo arbitrario in input.


In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions.Unlike other valuable items, license plates are not allocated an estimated price before auction. I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics.I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate.I demonstrate the importance of having a deep network and of retraining.Evaluated on 13 years of historical auction prices, the deep RNN's predictions can explain over 80 percent of price variations, outperforming previous models by a significant margin.  I also demonstrate how the model can be extended to become a search engine for plates and to provide estimates of the expected price distribution.

Previsione del prezzo d'asta delle targhe dei veicoli a Hong Kong con una deep recurrent neural network, basata sui caratteri delle targhe.


We present a new latent model of natural images that can be learned on large-scale datasets.The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space.After training, the new model provides a strong and universal image prior for a variety of image restoration tasks such as large-hole inpainting, superresolution, and colorization.To model high-resolution natural images, our approach uses latent spaces of very high dimensionality (one to two orders of magnitude higher than previous latent image models).To tackle this high dimensionality, we use latent spaces with a special manifold structure (convolutional manifolds) parameterized by a ConvNet of a certain architecture.In the experiments, we compare the learned latent models with latent models learned by autoencoders, advanced variants of generative adversarial networks, and a strong baseline system using simpler parameterization of the latent space.Our model outperforms the competing approaches over a range of restoration tasks.

Presentiamo un nuovo modello latente profondo di immagini naturali che può essere addestrato da dataset non annotati e può essere utilizzato per risolvere vari task di restauro delle immagini.


Automatic Essay Scoring (AES) has been an active research area as it can greatly reduce the workload of teachers and prevents subjectivity bias .Most recent AES solutions apply deep neural network (DNN)-based models with regression, where the neural neural-based encoder learns an essay representation that helps differentiate among the essays and the corresponding essay score is inferred by a regressor.Such DNN approach usually requires a lot of expert-rated essays as training data in order to learn a good essay representation for accurate scoring.However, such data is usually expensive and thus is sparse.Inspired by the observation that human usually scores an essay by comparing it with some references, we propose a Siamese framework called Referee Network (RefNet) which allows the model to compare the quality of two essays by capturing the relative features that can differentiate the essay pair.The proposed framework can be applied as an extension to regression models as it can capture additional relative features on top of internal information.Moreover, it intrinsically augment the data by pairing thus is ideal for handling data sparsity.Experiment shows that our framework can significantly improve the existing regression models and achieve acceptable performance even when the training data is greatly reduced.

Assegna automaticamente un punteggio ai saggi su dati sparsi, confrontando i nuovi saggi con sample noti con Referee Network.


One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs).With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task.There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as “motifs” and (2) interactions among TFs known as co-binding effects.In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms.Our PMN model automatically extracts prototypes (“motif”-like features) for each TF through a novel prototype-matching loss.Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences.On a reference TFBS dataset with 2.1 million genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically.To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large scale TFBS prediction.Not only is the proposed architecture accurate, but it also models the underlying biology.

Combiniamo il framework della matching network per few shot learning in un modello multilabel su larga scala per la classificazione delle sequenze genomiche.


Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning.We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training.This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness.Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes.We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.

L'applicazione della funzione softmax nel training porta a una supervisione indiretta e inaspettata sulle feature. Proponiamo un nuovo obiettivo di training per indurre esplicitamente regioni di feature dense perché i sample localmente sufficienti possano  beneficiare dell'adversarial robustness.


We present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level.We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method.Then, we examine the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images.We show several practical applications enabled by our framework, from comparing internal representations across different layers and models, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene.

Le rappresentazioni GAN sono esaminate in dettaglio, e si trovano insiemi di unità di rappresentazione che controllano la generazione di concetti semantici nell'output.


We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges.Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem.(2) they are not interpretable, making it difficult to control the synthesized output.We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets.We design a simple pipeline that combines the best of both worlds:  the first stage uses a convolutional neural network (CNN) to map the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner.Importantly, pixel-wise matching allows our method to compose novel high-frequency content by cutting-and-pasting pixels from different training exemplars.  We demonstrate our approach for various input modalities, and for various domains ranging from human faces, pets, shoes, and handbags.

Nearest neighbor pixel-wise usati per generare immagini multiple da prior incompleti come immagini a bassa risoluzione, normali alle superfici, bordi, ecc.


Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms.We address this problem through the principled lens of distributionally robust optimization, which guarantees performance under adversarial input perturbations.  By considering a Lagrangian penalty formulation of perturbing the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data.For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization.Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss.For imperceptible perturbations, our method matches or outperforms heuristic approaches.

Forniamo una procedura di adversarial training veloce e basata su principi teorici, con garanzie di prestazioni computazionali e statistiche.


Amortized inference has led to efficient approximate inference for large datasets.The quality of posterior inference is largely determined by two factors:a) the ability of the variational distribution to model the true posterior andb) the capacity of the recognition network to generalize inference over all datapoints.We analyze approximate inference in variational autoencoders in terms of these factors.We find that suboptimal inference is often due to amortizing inference rather than the limited complexity of the approximating distribution.We show that this is due partly to the generator learning to accommodate the choice of approximation.Furthermore, we show that the parameters used to increase the expressiveness of the approximation play a role in generalizing inference rather than simply improving the complexity of the approximation.

Decomponiamo il divario tra la log-likelihood marginale e l'evidence lower bound e studiamo l'effetto del posteriore approssimato sulla vera distribuzione posteriore nelle VAE.


In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance.To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels.We find that prior approaches for generating pseudo-labels hurt clustering performance because of their low accuracy.Instead, we use an ensemble of deep networks  to construct a similarity graph, from which we extract high accuracy pseudo-labels.The approach of finding high quality pseudo-labels using ensembles and training the semi-supervised model is iterated, yielding continued improvement.We show that our approach outperforms state of the art clustering results for multiple image and text datasets.For example, we achieve 54.6% accuracy for CIFAR-10 and 43.9% for 20news, outperforming state of the art by 8-12% in absolute terms.

Utilizzo di ensemble e pseudo label per il clustering unsupervised


This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem.We show that a subgradient descent algorithm, with random initialization, can recover orthogonal dictionaries on a natural nonsmooth, nonconvex L1 minimization formulation of the problem, under mild statistical assumption on the data.This is in contrast to previous provable methods that require either expensive computation or delicate initialization schemes.Our analysis develops several tools for characterizing landscapes of nonsmooth functions, which might be of independent interest for provable training of deep networks with nonsmooth activations (e.g., ReLU), among other applications.Preliminary synthetic and real experiments corroborate our analysis and show that our algorithm works well empirically in recovering orthogonal dictionaries.

Apprendimento efficiente dei dizionari tramite minimizzazione L1 attraverso una nuova analisi della geometria non convessa e non smooth.


We study model recovery for data classification, where the training labels are generated from a one-hidden-layer fully -connected neural network with sigmoid activations, and the goal is to recover the weight vectors of the neural network.We prove that under Gaussian inputs, the empirical risk function using cross entropy exhibits strong convexity and smoothness uniformly in a local neighborhood of the ground truth, as soon as the sample complexity is sufficiently large.This implies that if initialized in this neighborhood, which can be achieved via the tensor method, gradient descent converges linearly to a critical point that is provably close to the ground truth without requiring a fresh set of samples at each iteration.To the best of our knowledge, this is the first global convergence guarantee established for the empirical risk minimization using cross entropy via gradient descent for learning one-hidden-layer neural networks, at the near-optimal sample and computational complexity with respect to the network input dimension.

Forniamo la prima analisi teorica del recupero garantito di reti neurali a un hidden layer sotto cross-entropy loss per problemi di classificazione.


With the deployment of neural networks on mobile devices and the necessity of transmitting neural networks over limited or expensive channels, the file size of trained model was identified as bottleneck.We propose a codec for the compressionof neural networks which is based on transform coding for convolutional and dense layers and on clustering for biases and normalizations.With this codec, we achieve average compression factors between 7.9–9.3 while the accuracy of the compressed networks for image classification decreases only by 1%–2%, respectively.

Il nostro codec per le reti neurali (che è basato sulla codifica di trasformazione e sul clustering) permette una compressione trasparente a bassa complessità e ad alta efficienza delle reti neurali.


As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations.A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the untrusted software stack.However, these isolation guarantees come at a price in performance, compared to untrusted alternatives.This paper initiates the study of high performance execution of Deep Neural Networks (DNNs) in TEEs by efficiently partitioning DNN computations between trusted and untrusted devices.Building upon an efficient outsourcing scheme for matrix multiplication, we propose Slalom, a framework that securely delegates execution of all linear layers in a DNN from a TEE (e.g., Intel SGX or Sanctum) to a faster, yet untrusted, co-located processor.We evaluate Slalom by running DNNs in an Intel SGX enclave, which selectively delegates work to an untrusted GPU.For canonical DNNs (VGG16, MobileNet and ResNet variants) we obtain 6x to 20x increases in throughput for verifiable inference, and 4x to 11x for verifiable and private inference.

Acceleriamo l'inferenza sicura di DNN in ambienti di esecuzione fidati (di un fattore 4-20) esternalizzando selettivamente il calcolo dei layer lineari a un co-processore più veloce ma non fidato.


While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.Consequently, model size reduction has become an utmost goal in deep learning.A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes.However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates.In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution.By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set.The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family.Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance.

Questo articolo propone un metodo efficace per comprimere le reti neurali basato sui recenti risultati della teoria dell'informazione.


Most existing neural networks for learning graphs deal with the issue of permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors.We argue that this imposes a limitation on their representation power, and instead propose a new general architecture for representing objects consisting of a hierarchy of parts, which we call Covariant Compositional Networks (CCNs).Here covariance means that the activation of each neuron must transform in a specific way under permutations, similarly to steerability in CNNs.We achieve covariance by making each activation transform according to a tensor representation of the permutation group, and derive the corresponding tensor aggregation rules that each neuron must implement.Experiments show that CCNs can outperform competing methods on some standard graph learning benchmarks.

Un framework generale per la creazione di graph neural network covarianti


In recent years, three-dimensional convolutional neural network (3D CNN) are intensively applied in the video analysis and action recognition and receives good performance.However, 3D CNN leads to massive computation and storage consumption, which hinders its deployment on mobile and embedded devices.In this paper, we propose a three-dimensional regularization-based pruning method to assign different regularization parameters to different weight groups based on their importance to the network.Our experiments show that the proposed method outperforms other popular methods in this area.

In questo articolo, proponiamo un metodo di pruning basato sulla regolarizzazione tridimensionale per accelerare la 3D-CNN.


In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development — through the adoption and widespread use of data statements, the field can begin to address critical scientific and ethical issues that result from the use of data from certain populations in the development of technology for other populations.We present a form that data statements can take and explore the implications of adopting them as part of regular practice.We argue that data statements will help alleviate issues related to exclusion and bias in language technology; lead to better precision in claims about how NLP research can generalize and thus better engineering results; protect companies from public embarrassment; and ultimately lead to language technology that meets its users in their own preferred linguistic style and furthermore does not mis- represent them to others.** To appear in TACL **

Una proposta pratica per una tecnologia NLP più etica e reattiva, operando la trasparenza dei dati di test e di training


We introduce CGNN, a framework to learn functional causal models as generative neural networks.These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data.Unlike previous approaches, CGNN leverages both conditional independences and distributional asymmetries to seamlessly discover bivariate and multivariate  causal structures, with or without hidden variables.CGNN does not only estimate the causal structure, but a full and differentiable generative model of the data.Throughout an extensive variety of experiments, we illustrate the competitive  esults of CGNN w.r.t state-of-the-art alternatives in observational causal discovery on both simulated and real data, in the tasks of cause-effect inference, v-structure identification, and multivariate causal discovery.

Scoprire la struttura dei modelli causali funzionali con reti neurali generative


Network pruning is widely used for reducing the heavy computational cost of deep models.A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning.In this work, we make a rather surprising observation: fine-tuning a pruned model only gives comparable or even worse performance than training that model with randomly initialized weights.Our results have several implications:1) training a large, over-parameterized model is not necessary to obtain an efficient final model,2) learned "important" weights of the large model are not necessarily useful for the small pruned model,3) the pruned architecture itself, rather than a set of inherited weights, is what leads to the efficiency benefit in the final model, which suggests that some pruning algorithms could be seen as performing network architecture search.

Nel pruning della rete, il fine-tuning di un modello pruned dà solo prestazioni paragonabili o peggiori del training da zero. Questo richiede un ripensamento degli algoritmi di pruning esistenti.


Clustering is the central task in unsupervised learning and data mining.k-means is one of the most widely used clustering algorithms.Unfortunately, it is generally non-trivial to extend k-means to cluster data points beyond Gaussian distribution, particularly, the clusters with non-convex shapes (Beliakov & King, 2006).To this end, we, for the first time, introduce Extreme Value Theory (EVT) to improve the clustering ability of k-means.Particularly, the Euclidean space was transformed into a novel probability space denoted as extreme value space by EVT.We thus propose a novel algorithm called Extreme Value k-means (EV k-means), including GEV k-means and GPD k-means.In addition, we also introduce the tricks to accelerate Euclidean distance computation in improving the computational efficiency of classical k-means.Furthermore, our EV k-means is extended to an online version, i.e., online Extreme Value k-means, in utilizing the Mini Batch k-means to cluster streaming data.Extensive experiments are conducted to validate our EV k-means and online EV k-means on synthetic datasets and real datasets.Experimental results show that our algorithms significantly outperform competitors in most cases.

Questo articolo introduce la teoria dei valori estremi in k-means per misurare la somiglianza e propone un nuovo algoritmo chiamato Extreme Value k-means per il clustering.


Deep reinforcement learning algorithms have proven successful in a variety of domains.However, tasks with sparse rewards remain challenging when the state space is large.Goal-oriented tasks are among the most typical problems in this domain, where a reward can only be received when the final goal is accomplished.In this work, we propose a potential solution to such problems with the introduction of an experience-based tendency reward mechanism, which provides the agent with additional hints based on a discriminative learning on past experiences during an automated reverse curriculum.This mechanism not only provides dense additional learning signals on what states lead to success, but also allows the agent to retain only this tendency reward instead of the whole histories of experience during multi-phase curriculum learning.We extensively study the advantages of our method on the standard sparse reward domains like Maze and Super Mario Bros and show that our method performs more efficiently and robustly than prior approaches in tasks with long time horizons and large state space.In addition, we demonstrate that using an optional keyframe scheme with very small quantity of key states, our approach can solve difficult robot manipulation challenges directly from perception and sparse rewards.

Proponiamo Tendency RL per risolvere in modo efficiente i task goal-oriented con un grande spazio di stato utilizzando automated curriculum learning e la reward discriminante di shaping, che ha il potenziale per affrontare i task di manipolazione dei robot con la percezione.


Human scene perception goes beyond recognizing a collection of objects and their pairwise relations.We understand higher-level, abstract regularities within the scene such as symmetry and repetition.Current vision recognition modules and scene representations fall short in this dimension.In this paper, we present scene programs, representing a scene via a symbolic program for its objects, attributes, and their relations.We also propose a model that infers such scene programs by exploiting a hierarchical, object-based scene representation.Experiments demonstrate that our model works well on synthetic data and transfers to real images with such compositional structure.The use of scene programs has enabled a number of applications, such as complex visual analogy-making and scene extrapolation.

Presentiamo i scene program, una rappresentazione strutturata della scena che cattura sia l'aspetto low-level degli oggetti che le regolarità high-level nella scena.


Modern neural networks often require deep compositions of high-dimensional nonlinear functions (wide architecture) to achieve high test accuracy, and thus can have overwhelming number of parameters.Repeated high cost in prediction at test-time makes neural networks ill-suited for devices with constrained memory or computational power.We introduce an efficient mechanism, reshaped tensor decomposition, to compress neural networks by exploiting three types of invariant structures: periodicity, modulation and low rank.Our reshaped tensor decomposition method exploits such invariance structures using a technique called tensorization (reshaping the layers into higher-order tensors) combined with higher order tensor decompositions on top of the tensorized layers.Our compression method improves low rank approximation methods and can be incorporated to (is complementary to) most of the existing compression methods for neural networks to achieve better compression.Experiments on LeNet-5 (MNIST), ResNet-32 (CI- FAR10) and ResNet-50 (ImageNet) demonstrate that our reshaped tensor decomposition outperforms (5% test accuracy improvement universally on CIFAR10) the state-of-the-art low-rank approximation techniques under same compression rate, besides achieving orders of magnitude faster convergence rates.

Compressione delle reti neurali che migliora lo stato dell'arte delle tecniche di approssimazione di basso rango ed è complementare alla maggior parte delle altre tecniche di compressione.


With the rise in employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before.Although many different directions regarding interpretability have been explored for visual modalities, time-series data has been neglected with only a handful of methods tested due to their poor intelligibility.We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder with a sparsity-inducing norm on its output to the classifier and fine-tune it based on the gradients from the classifier and a reconstruction penalty.The auto-encoder learns to preserve features that are important for the prediction by the classifier and suppresses the ones that are irrelevant i.e. serves as a feature attribution method to boost interpretability.In other words, we ask the network to only reconstruct parts which are useful for the classifier i.e. are correlated or causal for the prediction.In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations.We evaluated TSInsight along with other commonly used attribution methods on a range of different time-series datasets to validate its efficacy.Furthermore, we analyzed the set of properties that TSInsight achieves out of the box including adversarial robustness and output space contraction.The obtained results advocate that TSInsight can be an effective tool for the interpretability of deep time-series models.

Presentiamo una tecnica di attribuzione che sfrutta le norme che inducono la sparsità per ottenere l'interpretabilità.


Variance reduction methods which use a mixture of large and small batch gradients, such as SVRG (Johnson & Zhang, 2013) and SpiderBoost (Wang et al., 2018), require significantly more computational resources per update than SGD (Robbins & Monro, 1951).We reduce the computational cost per update of variance reduction methods by introducing a sparse gradient operator blending the top-K operator (Stich et al., 2018; Aji & Heafield, 2017) and the randomized coordinate descent operator.While the computational cost of computing the derivative of a model parameter is constant, we make the observation that the gains in variance reduction are proportional to the magnitude of the derivative.In this paper, we show that a sparse gradient based on the magnitude of past gradients reduces the computational cost of model updates without a significant loss in variance reduction.Theoretically, our algorithm is at least as good as the best available algorithm (e.g. SpiderBoost) under appropriate settings of parameters and can be much more efficient if our algorithm succeeds in capturing the sparsity of the gradients.Empirically, our algorithm consistently outperforms SpiderBoost using various models to solve various image classification tasks.We also provide empirical evidence to support the intuition behind our algorithm via a simple gradient entropy computation, which serves to quantify gradient sparsity at every iteration.

Usiamo la sparsità per migliorare la complessità computazionale dei metodi di riduzione della varianza.


Despite promising progress on unimodal data imputation (e.g. image inpainting), models for multimodal data imputation are far from satisfactory.In this work, we propose variational selective autoencoder (VSAE) for this task.Learning only from partially-observed data, VSAE can model the joint distribution of observed/unobserved modalities and the imputation mask, resulting in a unified model for various down-stream tasks including data generation and imputation.Evaluation on synthetic high-dimensional and challenging low-dimensional multimodal datasets shows significant improvement over state-of-the-art imputation models.

Proponiamo un nuovo framework basato su VAE che impara dai dati parzialmente osservati per l'imputazione e la generazione.


In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity.However, there are strong-guarantees of privacy which prevent broad sampling and  labeling of personal text data to learn or evaluate models of interest.Fortunately, in some cases like enterprise email, manual annotation is possible on certain public datasets.The hope is that models trained on these public datasets would perform well on the target private datasets of interest.In this paper, we study the challenges of transferring information from one email dataset to another, for predicting user intent.In particular, we present approaches to characterizing the transfer gap in text corpora from both an intrinsic and extrinsic point-of-view, and evaluate several proposed methods in the literature for bridging this gap.We conclude with raising issues for further discussion in this arena.

Approfondimenti sulla sfida della domain adaptation, quando si predice l'intento dell'utente nelle e-mail aziendali.


Hierarchical Reinforcement Learning is a promising approach to long-horizon decision-making problems with sparse rewards.Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task.Treating the skills as fixed can lead to significant sub-optimality in the transfer setting.In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task.Our main contributions are two-fold.First, we derive a new hierarchical policy gradient, as well as an unbiased latent-dependent baseline.We introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy simultaneously.Second, we propose a method of training time-abstractions that improves the robustness of the obtained skills to environment changes.Code and results are available at sites.google.com/view/hippo-rl.

Proponiamo HiPPO, un algoritmo stabile di Hierarchical Reinforcement Learning che può addestrare diversi livelli della gerarchia simultaneamente, dando buone prestazioni sia nella scoperta delle abilità che nell'adattamento.


Learning can be framed as trying to encode the mutual information between input and output while discarding other information in the input.Since the distribution between input and output is unknown, also the true mutual information is.To quantify how difficult it is to learn a task, we calculate a observed mutual information score by dividing the estimated mutual information by the entropy of the input.We substantiate this score analytically by showing that the estimated mutual information has an error that increases with the entropy of the data.Intriguingly depending on how the data is represented the observed entropy and mutual information can vary wildly.There needs to be a match between how data is represented and how a model encodes it.Experimentally we analyze image-based input data representations and demonstrate that performance outcomes of extensive network architectures searches are well aligned to the calculated score.Therefore to ensure better learning outcomes, representations may need to be tailored to both task and model to align with the implicit distribution of the model.

Facciamo un step avanti verso la misurazione della difficoltà del task di apprendimento e dimostriamo che in pratica la performance dipende fortemente dalla corrispondenza tra la rappresentazione dell'informazione e il modello che la interpreta.


Sepsis is a life-threatening complication from infection and a leading cause of mortality in hospitals.  While early detection of sepsis improves patient outcomes, there is little consensus on exact treatment guidelines, and treating septic patients remains an open  problem.  In this work we present a new deep reinforcement learning method that we use to learn optimal personalized treatment policies for septic patients.We model patient continuous-valued physiological time series using multi-output Gaussian processes, a probabilistic model that easily handles missing values and irregularly spaced observation times while maintaining estimates of uncertainty.The Gaussian process is directly tied to a deep recurrent Q-network that learns clinically interpretable treatment policies, and both models are learned together end-to-end.  We evaluate our approach on a heterogeneous dataset of septic spanning 15 months from our university health system, and find that our learned policy could reduce patient mortality by as much as 8.2\% from an overall baseline mortality rate of 13.3\%.  Our algorithm could be used to make treatment recommendations to physicians as part of a decision support tool, and the framework readily applies to other reinforcement learning problems that rely on sparsely sampled and frequently missing multivariate time series data.

Combiniamo processi gaussiani multi-output con deep recurrent Q-network per imparare trattamenti ottimali per la sepsi e mostriamo prestazioni migliori rispetto ai metodi standard di deep reinforcement learning.


Unsupervised and semi-supervised learning are important problems that are especially challenging with complex data like natural images.Progress on these problems would accelerate if we had access to appropriate generative models under which to pose the associated inference tasks.Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Neural Rendering Model (NRM), a new hierarchical probabilistic generative model whose inference calculations correspond to those in a CNN.The NRM introduces a small set of latent variables at each level of the model and enforces dependencies among all the latent variables via a conjugate prior distribution.The conjugate prior yields a new regularizer for learning based on the paths rendered in the generative model for training CNNs–the Rendering Path Normalization (RPN).We demonstrate that this regularizer improves generalization both in theory and in practice.Likelihood estimation in the NRM yields the new Max-Min cross entropy training loss, which suggests a new deep network architecture–the Max- Min network–which exceeds or matches the state-of-art for semi-supervised and supervised learning on SVHN, CIFAR10, and CIFAR100.

Sviluppiamo un nuovo deep generative model per l'apprendimento semi-supervised e proponiamo una nuova Max-Min cross-entropy per il training delle CNN.


Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images.In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations.It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments.Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization.We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.

Proponiamo una semplice tecnica di randomizzazione per migliorare la generalizzazione nel deep reinforcement learning attraverso task con vari pattern visivi non visti.


Touch interactions with current mobile devices have limited expressiveness.Augmenting devices with additional degrees of freedom can add power to the interaction, and several augmentations have been proposed and tested.However, there is still little known about the effects of learning multiple sets of augmented interactions that are mapped to different applications.To better understand whether multiple command mappings can interfere with one another, or affect transfer and retention, we developed a prototype with three pushbuttons on a smartphone case that can be used to provide augmented input to the system.The buttons can be chorded to provide seven possible shortcuts or transient mode switches.We mapped these buttons to three different sets of actions, and carried out a study to see if multiple mappings affect learning and performance, transfer, and retention.Our results show that all of the mappings were quickly learned and there was no reduction in performance with multiple mappings.Transfer to a more realistic task was successful, although with a slight reduction in accuracy.Retention after one week was initially poor, but expert performance was quickly restored.Our work provides new information about the design and use of augmented input in mobile interactions.

Descrive uno studio che indaga l'interferenza, il transfer e la ritenzione di mappature multiple con lo stesso set di pulsanti accordati


Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text.This paper aims to tackle under-sensitivity in the context of natural language inference by ensuring that models do not become more confident in their predictions as arbitrary subsets of words from the input text are deleted.We develop a novel technique for formal verification of this specification for models based on the popular decomposable attention mechanism by employing the efficient yet effective interval bound propagation (IBP) approach.Using this method we can efficiently prove, given a model, whether a particular sample is free from the under-sensitivity problem.We compare different training methods to address under-sensitivity, and compare metrics to measure it.In our experiments on the SNLI and MNLI datasets, we observe that IBP training leads to a significantly improved verified accuracy.On the SNLI test set, we can verify 18.4% of samples, a substantial improvement over only 2.8% using standard training.

Verifica formale di una specifica sulla sottosensibilità della predizione di un modello usando la propagazione degli interval bound


Training with larger number of parameters while keeping fast iterations is an increasinglyadopted strategy and trend for developing better performing Deep NeuralNetwork (DNN) models.This necessitates increased memory footprint andcomputational requirements for training.Here we introduce a novel methodologyfor training deep neural networks using 8-bit floating point (FP8) numbers.Reduced bit precision allows for a larger effective memory and increased computationalspeed.We name this method Shifted and Squeezed FP8 (S2FP8).Weshow that, unlike previous 8-bit precision training methods, the proposed methodworks out of the box for representative models: ResNet50, Transformer and NCF.The method can maintain model accuracy without requiring fine-tuning loss scalingparameters or keeping certain layers in single precision.We introduce twolearnable statistics of the DNN tensors - shifted and squeezed factors that are usedto optimally adjust the range of the tensors in 8-bits, thus minimizing the loss ininformation due to quantization.

Proponiamo un nuovo formato a 8 bit che elimina la necessità del loss scaling, dell'arrotondamento stocastico e di altre tecniche a bassa precisione


Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences.From practitioners point of view, we are usually interested in solving problems where tasks are learned sequentially, in a way that avoids revisiting all previous data at each stage.We address this problem by introducing a conceptually simple and scalable end-to-end approach of incorporating past knowledge by learning prior directly from the data.  We consider scalable boosting-like approximation for intractable theoretical optimal prior.We provide empirical studies on two commonly used benchmarks, namely  MNIST and Fashion MNIST on disjoint sequential image generation tasks.For each dataset proposed method delivers the best results among comparable approaches,  avoiding catastrophic forgetting in a fully automatic way with a fixed model architecture.

Nuovo algoritmo per l'apprendimento incrementale di VAE con architettura fissa


The field of few-shot learning has recently seen substantial advancements.Most of these advancements came from casting few-shot learning as a meta-learning problem.Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning.MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times.In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.

MAML è ottimo, ma ha molti problemi, noi risolviamo molti di questi problemi e come risultato impariamo la maggior parte degli iperparametri end-to-end, velocizziamo il training e l'inferenza e stabiliamo un nuovo SOTA in few-shot learning.


Community detection in graphs is of central importance in graph mining, machine learning and network science. Detecting overlapping communities is especially challenging, and remains an open problem.  Motivated by the success of graph-based  deep  learning  in  other  graph-related  tasks,  we  study  the  applicability  of this framework for overlapping community detection. We propose a probabilistic model for overlapping community detection based on the graph neural network architecture.  Despite its simplicity, our model outperforms the existing approaches in the community recovery task by a large margin.  Moreover, due to the inductive formulation, the proposed model is able to perform out-of-sample community detection for nodes that were not present at training time

Rilevamento di comunità sovrapposte nei grafi utilizzando graph neural network


Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks.Existing techniques rely on two stages: searching over the architecture space and validating the best architecture.NAS algorithms are currently compared solely based on their results on the downstream task.While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies.In this paper, we propose to evaluate the NAS search phase.To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection.We find that:(i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy;(ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process.We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones.

Smentiamo empiricamente un'ipotesi fondamentale della strategia di condivisione dei pesi ampiamente adottata nella ricerca dell'architettura neurale e spieghiamo perché lo stato dell'arte degli algoritmi NAS si comporta in modo simile alla ricerca casuale.


In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder.We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified.In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution.We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation.We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.

In questo articolo usiamo la distanza sliced di Wasserstein per modellare la distribuzione latente di un auto-encoder in qualsiasi distribuzione a priori campionabile.


The Hamiltonian formalism plays a central role in classical and quantum physics.Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time.These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks.In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions.Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time, and even speed up or slow down the learned dynamics.We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities.Hence, we hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to machine learning.More results and video evaluations are available at: http://tiny.cc/hgn

Introduciamo una classe di modelli generativi che imparano in modo affidabile la dinamica hamiltoniana da osservazioni ad alta dimensionalità. L'hamiltoniana appresa può essere applicata alla modellazione di sequenze o come normalising flow.


Cloud Migration transforms customer’s data, application and services from original IT platform to one or more cloud en- vironment, with the goal of improving the performance of the IT system while reducing the IT management cost.The enterprise level Cloud Migration projects are generally com- plex, involves dynamically planning and replanning various types of transformations for up to 10k endpoints.Currently the planning and replanning in Cloud Migration are generally done manually or semi-manually with heavy dependency on the migration expert’s domain knowledge, which takes days to even weeks for each round of planning or replanning.As a result, automated planning engine that is capable of gener- ating high quality migration plan in a short time is particu- larly desirable for the migration industry.In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the challenges the requires attention from the planning and scheduling society.

In questo short paper, introduciamo brevemente i vantaggi dell'uso della pianificazione AI in Cloud Migration, un prototipo preliminare, così come le sfide che richiedono attenzione da parte della società di pianificazione e programmazione.


Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain.One popular approach to this problem is to learn a domain-invariant representation for both domains.In this work, we study, theoretically and empirically, the explicit effect of the embedding on generalization to the target domain.In particular, the complexity of the class of embeddings affects an upper bound on the target domain's risk.This is reflected in our experiments, too.

Un upper bound generale sul rischio del dominio target che riflette il ruolo dell'embedding-complexity.


The domain of time-series forecasting has been extensively studied because it is of fundamental importance in many real-life applications.Weather prediction, traffic flow forecasting or sales are compelling examples of sequential phenomena.Predictive models generally make use of the relations between past and future values.However, in the case of stationary time-series, observed values also drastically depend on a number of exogenous features that can be used to improve forecasting quality.In this work, we propose a change of paradigm which consists in learning such features in embeddings vectors within recurrent neural networks.We apply our framework to forecast smart cards tap-in logs in the Parisian subway network.Results show that context-embedded models perform quantitatively better in one-step ahead and multi-step ahead forecasting.

Per prevedere serie temporali stazionarie multivariate impariamo embedding contenenti feature contestuali all'interno di una RNN; applichiamo il framework su dati del trasporto pubblico


The ability of an agent to {\em discover} its own learning objectives has long been considered a key ingredient for artificial general intelligence.Breakthroughs in autonomous decision making and reinforcement learning have primarily been in domains where the agent's goal is outlined and clear: such as playing a game to win, or driving safely.Several studies have demonstrated that learning extramural sub-tasks and auxiliary predictions can improve (1) single human-specified task learning, (2) transfer of learning, (3) and the agent's learned representation of the world.In all these examples, the agent was instructed what to learn about.We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent's representation of the world.Specifically, our system maintains a large collection of predictions, continually pruning and replacing predictions.We highlight the importance of considering stability rather than convergence for such a system, and develop an adaptive, regularized algorithm towards that aim.We provide several experiments in computational micro-worlds demonstrating that this simple approach can be effective for discovering useful predictions autonomously.

Studiamo un framework per la scoperta: curare una grande collezione di predizioni, che sono utilizzate per costruire la rappresentazione dell'agente in domini parzialmente osservabili.


Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion relies heavily on contextual information, and is not as simple as identifying  a single object in the image.Thus any methods which attempt to do so must somehow account for these complexities, and no single model to date is completely capable of addressing all challenges.This work contributes to the state of research in image geolocation inferencing by introducing a novel global meshing strategy, outlining a variety of training procedures to overcome the considerable data limitations when training these models, and demonstrating how incorporating additional information can be used to improve the overall performance of a geolocation inference model.In this work, it is shown that Delaunay triangles are an effective type of mesh for geolocation in relatively low volume scenarios when compared to results from state of the art models which use quad trees and an order of magnitude more training data.  In addition, the time of posting, learned user albuming, and other meta data are easily incorporated to improve geolocation by up to 11% for country-level (750 km) locality accuracy to 3% for city-level (25 km) localities.

Una strategia globale di inferenza di geolocalizzazione con una nuova strategia di meshing, mostrando che incorporare informazioni aggiuntive può essere utile per migliorare le prestazioni complessive di un modello di inferenza di geolocalizzazione.


Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables.To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases.Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood.With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers.The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.

Tecnica per l'apprendimento di deep generative model con variabili latenti condivise, applicata a Omniglot con un decoder PixelCNN.


Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora.We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models.A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs.Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach.Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.

In questo articolo presentiamo un'architettura di lettura task-agnostica per l'integrazione dinamica della conoscenza esplicita del background nei modelli NLU neurali.


Mixed-precision arithmetic combining both single- and half-precision operands in the same operation have been successfully applied to train deep neural networks.Despite the advantages of mixed-precision arithmetic in terms of reducing the need for key resources like memory bandwidth or register file size, it has a limited capacity for diminishing computing costs and requires 32 bits to represent its output operands.This paper proposes two approaches to replace mixed-precision for half-precision arithmetic during a large portion of the training.The first approach achieves accuracy ratios slightly slower than the state-of-the-art by using half-precision arithmetic during more than 99% of training.The second approach reaches the same accuracy as the state-of-the-art by dynamically switching between half- and mixed-precision arithmetic during training.It uses half-precision during more than 94% of the training process.This paper is the first in demonstrating that half-precision can be used for a very large portion of DNNs training and still reach state-of-the-art accuracy.

Tecnica di precisione dinamica per addestrare deep neural network


We introduce the “inverse square root linear unit” (ISRLU) to speed up learning in deep neural networks.ISRLU has better performance than ELU but has many of the same benefits.ISRLU and ELU have similar curves and characteristics.Both have negative values, allowing them to push mean unit activation closer to zero, and bring the normal gradient closer to the unit natural gradient, ensuring a noise- robust deactivation state, lessening the over fitting risk.The significant performance advantage of ISRLU on traditional CPUs also carry over to more efficient HW implementations on HW/SW codesign for CNNs/RNNs.In experiments with TensorFlow, ISRLU leads to faster learning and better generalization than ReLU on CNNs.This work also suggests a computationally efficient variant called the “inverse square root unit” (ISRU) which can be used for RNNs.Many RNNs use either long short-term memory (LSTM) and gated recurrent units (GRU) which are implemented with tanh and sigmoid activation functions.ISRU has less computational complexity but still has a similar curve to tanh and sigmoid.

Introduciamo la funzione di attivazione ISRLU che è continuamente differenziabile e più veloce di ELU. La relativa ISRU sostituisce tanh e sigmoide.


A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage  the compositional structure of the task distribution.This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems.We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon.As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems.We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not.

Esploriamo il problema della generalizzazione compositiva e proponiamo un mezzo per dotare le architetture delle reti neurali della capacità di comporsi per risolvere questi problemi.


The information bottleneck method provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for predicting the label, while minimizing the amount of other, superfluous information in the representation.The original formulation, however, requires labeled data in order to identify which information is superfluous.  In this work, we extend this ability to the multi-view unsupervised setting, in which two views of the same underlying entity are provided but the label is unknown.This enables us to identify superfluous information as that which is not shared by both views.A theoretical analysis leads to the definition of a new multi-view model that produces state-of-the-art results on the Sketchy dataset and on label-limited versions of the MIR-Flickr dataset.  We also extend our theory to the single-view setting by taking advantage of standard data augmentation techniques, empirically showing better generalization capabilities when compared to traditional unsupervised approaches for representation learning.

Estendiamo il metodo dell'information bottleneck al setting multiview non supervisionato e mostriamo risultati allo stato dell'arte su dataset standard


The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections.We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues.In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism.Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function.As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function,but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights.We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.

Descriviamo un algoritmo di apprendimento biologicamente plausibile per reti ricorrenti a punto fisso senza pesi vincolati


In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way.For example, we do not use any ground truth 3D or 2D annotations, stereo video, and ego-motion during the training.Our approach follows the general strategy of Generative Adversarial Networks, where an image generator network learns to create image samples that are realistic enough to fool a discriminator network into believing that they are natural images.In contrast, in our approach the image gen- eration is split into 2 stages.In the first stage a generator network outputs 3D ob- jects.In the second, a differentiable renderer produces an image of the 3D object from a random viewpoint.The key observation is that a realistic 3D object should yield a realistic rendering from any plausible viewpoint.Thus, by randomizing the choice of the viewpoint our proposed training forces the generator network to learn an interpretable 3D representation disentangled from the viewpoint.In this work, a 3D representation consists of a triangle mesh and a texture map that is used to color the triangle surface by using the UV-mapping technique.We provide analysis of our learning approach, expose its ambiguities and show how to over- come them.Experimentally, we demonstrate that our method can learn realistic 3D shapes of faces by using only the natural images of the FFHQ dataset.

Addestriamo un modello generativo 3D di forme da immagini naturali in modo completamente unsupervised.


We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.We use Bayesian optimization (BO) to specificallycater to scenarios involving low query budgets to develop query efficient adversarial attacks.We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques.Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count.In particular, in low query budget regimes, our proposed method reduces the query count up to 80% with respect to the state of the art methods.

Mostriamo che uno schema di black-box adversarial attack relativamente semplice che utilizza l'ottimizzazione bayesiana e l'upsampling delle dimensioni è preferibile ai metodi esistenti quando il numero di query disponibili è molto basso.


State-of-the-art deep neural networks (DNNs) typically have tens of millions of parameters, which might not fit into the upper levels of the memory hierarchy, thus increasing the inference time and energy consumption significantly, and prohibiting their use on edge devices such as mobile phones.The compression of DNN models has therefore become an active area of research recently, with \emph{connection pruning} emerging as one of the most successful strategies.A very natural approach is to prune connections of DNNs via $\ell_1$ regularization, but recent empirical investigations have suggested that this does not work as well in the context of DNN compression.In this work, we revisit this simple strategy and analyze it rigorously, to show that:(a) any \emph{stationary point} of an $\ell_1$-regularized layerwise-pruning objective has its number of non-zero elements bounded by the number of penalized prediction logits, regardless of the strength of the regularization;(b) successful pruning highly relies on an accurate optimization solver, and there is a trade-off between compression speed and distortion of prediction accuracy, controlled by the strength of regularization.Our theoretical results thus suggest that $\ell_1$ pruning could be successful provided we use an accurate optimization solver.We corroborate this in our experiments, where we show that simple $\ell_1$ regularization with an Adamax-L1(cumulative) solver gives pruning ratio competitive to the state-of-the-art.

Rivisitiamo la semplice idea di fare pruning delle connessioni delle DNN attraverso la regolarizzazione $\ell_1$ ottenendo risultati allo stato dell'arte su più dataset con garanzie teoriche.


Autonomy and adaptation of machines requires that they be able to measure their own errors.We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task.How can a machine measure the error of regression sub-components when it does not have the ground truth for the correct predictions?A compressed sensing approach applied to the error signal of the regressors can recover their precision error without any ground truth.It allows for some regressors to be strongly correlated as long as not too many are so related.Its solutions, however, are not unique - a property of ground truth inference solutions.Adding  $\ell_1$--minimizationas a condition can recover the correct solution in settings where error correction is possible.We briefly discuss the similarity of the mathematics of ground truth inference for regressors to that for classifiers.

Un metodo non parametrico per misurare i momenti di errore dei regressori senza ground truth può essere usato con regressori biased


We propose a new representation, one-pixel signature, that can be used to reveal the characteristics of the convolution neural networks (CNNs).Here, each CNN classifier is associated with a signature that is created by generating, pixel-by-pixel, an adversarial value that is the result of the largest change to the class prediction.The one-pixel signature is agnostic to the design choices of CNN architectures such as type, depth, activation function, and how they were trained.It can be computed efficiently for a black-box classifier without accessing the network parameters.Classic networks such as LetNet, VGG, AlexNet, and ResNet demonstrate different characteristics in their signature images.For application, we focus on the classifier backdoor detection problem where a CNN classifier has been maliciously inserted with an unknown Trojan.We show the effectiveness of the one-pixel signature in detecting backdoored CNN.Our proposed one-pixel signature representation is general and it can be applied in problems where discriminative classifiers, particularly neural network based, are to be characterized.

Caratterizzazione delle reti neurali convoluzionali per classifier detection e understanding backdoored.


We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints.Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories.Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving a mixed integer program.Additionally, by leveraging a known parameterization of the constraint, we modify our method to learn parametric constraints in high dimensions.We show that our method can learn a six-dimensional pose constraint for a 7-DOF robot arm.

Possiamo imparare i vincoli ad alta dimensionalità dalle dimostrazioni campionando le traiettorie non sicure e sfruttando una parametrizzazione nota dei vincoli.


We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.We employ Siamese networks to solve the problem of least squares multidimensional scaling for generating mappings that preserve geodesic distances on the manifold.In contrast to previous parametric manifold learning methods we show a substantial reduction in training effort enabled by the computation of geodesic distances in a farthest point sampling strategy.Additionally, the use of a network to model the distance-preserving map reduces the complexity of the multidimensional scaling problem and leads to an improved non-local generalization of the manifold compared to analogous non-parametric counterparts.We demonstrate our claims on point-cloud data and on image manifolds and show a numerical analysis of our technique to facilitate a greater understanding of the representational power of neural networks in modeling manifold data.

Apprendimento parametrico di manifold con reti neurali in un framework geometrico


Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection.Exploration, however, can be more efficient if directed toward gaining new world knowledge.Visit-counters have been proven useful both in practice and in theory for directed exploration.However, a major limitation of counters is their locality.While there are a few model-based solutions to this shortcoming, a model-free approach is still missing.We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories.We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters.We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs.We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.

Proponiamo una generalizzazione dei contatori di visite che valutano il valore esplorativo che si propaga sulle traiettorie, permettendo un'esplorazione efficiente per RL model-free


Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search.The former is widely applicable and rather stable, but suffers from low sample efficiency.By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting.So far, these families of methods have mostly been compared as competing tools.However, an emerging approach consists in combining them so as to get the best of both worlds.Two previously existing combinations use either an ad hoc evolutionary algorithm or a goal exploration process together with the Deep Deterministic Policy Gradient (DDPG) algorithm, a sample efficient off-policy deep RL algorithm.In this paper, we propose a different combination scheme using the simple cross-entropymethod (CEM) and Twin Delayed Deep Deterministic policy gradient (TD3), another off-policy deep RL algorithm which improves over DDPG.We evaluate the resulting method, CEM-RL, on a set of benchmarks classically used in deep RL.We show that CEM-RL benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency.

Proponiamo una nuova combinazione di strategia di evoluzione e deep reinforcement learning che prende il meglio di entrambi i mondi


Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games.However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved.Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model.At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework.To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems.We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario.In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting.

Un confronto empirico di deep bayesian network per il sampling di Thompson


Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs).In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information.With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training.Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution.Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10.In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality.We thus propose a new metric, called AM Score, to provide more accurate estimation on the sample quality.Our proposed model also outperforms the baseline methods in the new metric.

Capire come le label di classe aiutino il training GAN. Proporre una nuova metrica di valutazione per i modelli generativi.


Modern neural networks are over-parametrized.In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and out- put weights, without changing the rest of the network.Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the l2 norm of the weights, equivalently the weight decay regularizer.It provably converges to a unique solution.Interleaving our algorithm with SGD during training improves the test accuracy.For small batches, our approach offers an alternative to batch- and group- normalization on CIFAR-10 and ImageNet with a ResNet-18.

Algoritmo iterativo veloce per bilanciare l'energia di una rete rimanendo nella stessa classe di equivalenza funzionale


Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings.But does the distribution over environment settings contain important biases, and do these lead to agents that fail in certain cases despite high average-case performance?In this work, we consider worst-case analysis of agents over environment settings in order to detect whether there are directions in which agents may have failed to generalize.Specifically, we consider a 3D first-person task where agents must navigate procedurally generated mazes, and where reinforcement learning agents have recently achieved human-level average-case performance.By optimizing over the structure of mazes, we find that agents can suffer from catastrophic failures, failing to find the goal even on surprisingly simple mazes, despite their impressive average-case performance.Additionally, we find that these failures transfer between different agents and even significantly different architectures.We believe our findings highlight an important role for worst-case analysis in identifying whether there are directions in which agents have failed to generalize.Our hope is that the ability to automatically identify failures of generalization will facilitate development of more general and robust agents.To this end, we report initial results on enriching training with settings causing failure.

Troviamo setting ambientali in cui gli agenti SOTA addestrati su task di navigazione mostrano gravi fallimenti che suggeriscono fallimenti nella generalizzazione.


In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates.To fix this issue, it has been suggested to replace the likelihood by a pseudo-likelihood, that is the exponential of a loss function enjoying suitable robustness properties.In this paper, we build a pseudo-likelihood based on the Maximum Mean Discrepancy, defined via an embedding of probability distributions into a reproducing kernel Hilbert space.We show that this MMD-Bayes posterior is consistent and robust to model misspecification.As the posterior obtained in this way might be intractable, we also prove that reasonable variational approximations of this posterior enjoy the same properties.We provide details on a stochastic gradient algorithm to compute these variational approximations.Numerical simulations indeed suggest that our estimator is more robust to misspecification than the ones based on the likelihood.

Stima bayesiana robusta attraverso la massima discrepanza media


The standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series.If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator.We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost.This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions.

Creiamo uno stimatore unbiased per la log probabilità di modelli di variabili latenti, estendendo tali modelli a un più ampio campo di applicazioni.


This paper makes two contributions towards understanding how the hyperparameters of stochastic gradient descent affect the final training loss and test accuracy of neural networks.First, we argue that stochastic gradient descent exhibits two regimes with different behaviours; a noise dominated regime which typically arises for small or moderate batch sizes, and a curvature dominated regime which typically arises when the batch size is large.In the noise dominated regime, the optimal learning rate increases as the batch size rises, and the training loss and test accuracy are independent of batch size under a constant epoch budget.In the curvature dominated regime, the optimal learning rate is independent of batch size, and the training loss and test accuracy degrade as the batch size rises.We support these claims with experiments on a range of architectures including ResNets, LSTMs and autoencoders.We always perform a grid search over learning rates at all batch sizes.Second, we demonstrate that small or moderately large batch sizes continue to outperform very large batches on the test set, even when both models are trained for the same number of steps and reach similar training losses.Furthermore, when training Wide-ResNets on CIFAR-10 with a constant batch size of 64, the optimal learning rate to maximize the test accuracy only decays by a factor of 2 when the epoch budget is increased by a factor of 128, while the optimal learning rate to minimize the training loss decays by a factor of 16.These results confirm that the noise in stochastic gradients can introduce beneficial implicit regularization.

Le batch size più piccole possono superare le batch size molto grandi sul test set sotto budget di step costanti e con schedule di learning rate adeguatamente sintonizzate.


Counterfactual Regret Minimization (CFR) is the most successful algorithm for finding approximate Nash equilibria in imperfect information games.However, CFR's reliance on full game-tree traversals limits its scalability and generality.Therefore, the game's state- and action-space is often abstracted (i.e. simplified) for CFR, and the resulting strategy is then mapped back to the full game.This requires extensive expert-knowledge, is not practical in many games outside of poker, and often converges to highly exploitable policies.A recently proposed method, Deep CFR, applies deep learning directly to CFR, allowing the agent to intrinsically abstract and generalize over the state-space from samples, without requiring expert knowledge.In this paper, we introduce Single Deep CFR (SD-CFR), a variant of Deep CFR that has a lower overall approximation error by avoiding the training of an average strategy network.We show that SD-CFR is more attractive from a theoretical perspective and empirically outperforms Deep CFR with respect to exploitability and one-on-one play in poker.

Migliore algoritmo di Deep Reinforcement Learning per approssimare la minimizzazione del counterfactual regret


While generative models have shown great success in generating high-dimensional samples conditional on low-dimensional descriptors (learning e.g. stroke thickness in MNIST, hair color in CelebA, or speaker identity in Wavenet), their generation out-of-sample poses fundamental problems.The conditional variational autoencoder (CVAE) as a simple conditional generative model does not explicitly relate conditions during training and, hence, has no incentive of learning a compact joint distribution across conditions.We overcome this limitation by matching their distributions using maximum mean discrepancy (MMD) in the decoder layer that follows the bottleneck.This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization.We refer to the architecture as transformer VAE (trVAE).Benchmarking trVAE on high-dimensional image and tabular data, we demonstrate higher robustness and higher accuracy than existing approaches.In particular, we show qualitatively improved predictions for cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data, by tackling previously problematic minority classes and multiple conditions.For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively.

Genera dati mai visti durante il training da una condizione desiderata


We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network by minimizing the L2 loss over whitened data.  Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the minimum of the input and output dimensions; (ii) weight matrices at initialization are approximately balanced; and (iii) the initial loss is smaller than the loss of any rank-deficient solution.  The assumptions on initialization (conditions (ii) and (iii)) are necessary, in the sense that violating any one of them may lead to convergence failure.  Moreover, in the important case of output dimension 1, i.e. scalar regression, they are met, and thus convergence to global optimum holds, with constant probability under a random initialization scheme.  Our results significantly extend previous analyses, e.g., of deep linear residual networks (Bartlett et al., 2018).

Analizziamo la gradient descent per le deep linear neural network, fornendo una garanzia di convergenza all'ottimo globale ad un tasso lineare.


One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data.Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data.However, learning non-parametric invariances directly from data remains an important open problem.In this paper, we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself.This layer can learn invariance to non-parametric transformations and interestingly, motivates and incorporates permanent random connectomes there by being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN).PRC-NPTN networks are initialized with random connections (not just weights) which are a small subset of the connections in a fully connected convolution layer.Importantly, these connections in PRC-NPTNs once initialized remain permanent throughout training and testing.  Random connectomes makes these architectures loosely more biologically plausible than many other mainstream network architectures which require highly ordered structures.We motivate randomly initialized connections as a simple method to learn invariance from data itself while invoking invariance towards multiple nuisance transformations simultaneously.We find that these randomly initialized permanent connections have positive effects on generalization, outperform much larger ConvNet baselines and the recently proposed Non-Parametric Transformation Network (NPTN) on benchmarks that enforce learning invariances from the data itself.

Un layer che modella connettomi casuali locali nella corteccia all'interno di deep network in grado di apprendere invarianze generali non parametriche dai dati stessi.


We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications.In contrast to existing conditionalgenerative models which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit (CTU), designed to learn the latent space transformations corresponding to specified target views.In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views, and an adaptive discriminator is introduced to improve the adversarial training process.The generality of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view reconstruction on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects.The proposed model is shown to exceed state-of-the-art results in each category while simultaneously achieving a reduction in the computational demand required for inference by 30% on average.

Introduciamo un framework efficace e generale per incorporare informazioni di condizionamento in modelli generativi basati sull'inferenza.


We describe three approaches to enabling an extremely computationally limited embedded scheduler to consider a small number of alternative activities based on resource availability.We consider the case where the scheduler is so computationally limited that it cannot backtrack search.The first two approaches precompile resource checks (called guards) that only enable selection of a preferred alternative activity if sufficient resources are estimated to be available to schedule the remaining activities.The final approach mimics backtracking by invoking the scheduler multiple times with the alternative activities.We present an evaluation of these techniques on mission scenarios (called sol types) from NASA's next planetary rover where these techniques are being evaluated for inclusion in an onboard scheduler.

Questo articolo descrive tre tecniche per permettere a uno scheduler non-backtracking, computazionalmente limitato, di considerare un piccolo numero di attività alternative basate sulla disponibilità delle risorse.


Inspired by the modularity and the life-cycle of biological neurons,we introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on the pruning of neurons of low activity.In this method, an L1 regulator is used to promote the presence of neurons of zero or low activity whose connections to previously active neurons is permanently severed at the end of training.Subsequent tasks are trained using these pruned neurons after reinitialization and cause zero deterioration to the performance of previous tasks.We show empirically that this biologically inspired method leads to state of the art results beating or matching current methods of higher computational complexity.

Usiamo modifiche semplici e biologicamente motivate di tecniche di apprendimento standard per ottenere prestazioni allo stato dell'arte su benchmark di catastrophic forgetting.


There has been an increasing use of neural networks for music information retrieval tasks.In this paper, we empirically investigate different ways of improving the performance of convolutional neural networks (CNNs) on spectral audio features.More specifically, we explore three aspects of CNN design: depth of the network, the use of residual blocks along with the use of grouped convolution, and global aggregation over time.The application context is singer classification and singing performance embedding and we believe the conclusions extend to other types of music analysis using convolutional neural networks.The results show that global time aggregation helps to improve the performance of CNNs the most.Another contribution of this paper is the release of a singing recording dataset that can be used for training and evaluation.

Utilizzo di tecniche di deep learning su task relativi alla voce cantata.


The Softmax function is used in the final layer of nearly all existing sequence-to-sequence models for language generation.However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint.We propose a general technique for replacing the softmax layer with a continuous embedding layer.Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax.We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation.We show that our models obtain upto 2.5x speed-up in training time while performing on par with the state-of-the-art models in terms of translation quality.These models are capable of handling very large vocabularies without compromising on translation quality.They also produce more meaningful errors than in the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations.

Language generation utilizzando modelli seq2seq che producono word embedding invece di una distribuzione basata su softmax sul vocabolario ad ogni step, consentendo un training molto più veloce pur mantenendo la qualità della generazione