Deep neural networks use deeper and broader structures to achieve better performance and consequently, use increasingly more GPU memory as well.However, limited GPU memory restricts many potential designs of neural networks.In this paper, we propose a reinforcement learning based variable swapping and recomputation algorithm to reduce the memory cost, without sacrificing the accuracy of models.Variable swapping can transfer variables between CPU and GPU memory to reduce variables stored in GPU memory.Recomputation can trade time for space by removing some feature maps during forward propagation.Forward functions are executed once again to get the feature maps before reuse.However, how to automatically decide which variables to be swapped or recomputed remains a challenging problem.To address this issue, we propose to use a deep Q-network(DQN) to make plans.By combining variable swapping and recomputation, our results outperform several well-known benchmarks.

Proponiamo un algoritmo di reinforcement learning basato sullo swapping e sulla ricomputazione delle variabili per ridurre il costo della memoria.


In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability.Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL).This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing (ITD) using fixed random feedback weight alignment (FBA).Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures.We don't claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning.

La differenziazione temporale iterativa con fixed random feedback alignment supporta la spike-time dependent plasticity nella backpropagation standard per deep learning.


Inspired by the recent successes of deep generative models for Text-To-Speech (TTS) such as WaveNet (van den Oord et al., 2016) and Tacotron (Wang et al., 2017), this article proposes the use of a deep generative model tailored for Automatic Speech Recognition (ASR) as the primary acoustic model (AM) for an overall recognition system with a separate language model (LM).Two dimensions of depth are considered: (1) the use of mixture density networks, both autoregressive and non-autoregressive, to generate density functions capable of modeling acoustic input sequences with much more powerful conditioning than the first-generation generative models for ASR, Gaussian Mixture Models / Hidden Markov Models (GMM/HMMs), and (2) the use of standard LSTMs, in the spirit of the original tandem approach, to produce discriminative feature vectors for generative modeling.Combining mixture density networks and deep discriminative features leads to a novel dual-stack LSTM architecture directly related to the RNN Transducer (Graves, 2012), but with the explicit functional form of a density, and combining naturally with a separate language model, using Bayes rule.The generative models discussed here are compared experimentally in terms of log-likelihoods and frame accuracies.

Questo articolo propone l'uso di un deep acoustic generative model per automatic speech recognition, combinandolo naturalmente con altri deep module per task sequence-to-sequence utilizzando la regola di Bayes.


Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples.Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penalizing perturbations.Different defense methods have also been explored to defend against such adversarial attacks. While the effectiveness of L_p distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works.Perturbations generated through spatial transformation could result in large L_p distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems.This potentially provides a new direction in adversarial example generation and the design of corresponding defenses.We visualize the spatial transformation based perturbation for different examples and show that our techniquecan produce realistic adversarial examples with smooth image deformation.Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.

Proponiamo un nuovo approccio per la generazione di adversarial example basato sulla trasformazione spaziale, che produce esempi percettivamente realistici rispetto agli attacchi esistenti.


Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are eitherdiscrete or continuous space.Motivated by the project of design Game AI for King of Glory(KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuoushybrid action space.To directly apply existing DLR frameworks, existing approacheseither approximate the hybrid space by a discrete set or relaxing it into a continuous set, which isusually less efficient and robust.In this paper, we propose a parametrized deep Q-network (P-DQN)for the hybrid action space without approximation or relaxation.Our algorithm combines DQN andDDPG and can be viewed as an extension of the DQN to hybrid actions.The empirical study on thegame KOG validates the efficiency and effectiveness of our method.

Un algoritmo ibrido DQN e DDPG è proposto per affrontare lo spazio d'azione ibrido discreto-continuo.


Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs.More specifically, latent distance approaches model the relationships among entities via a distance between latent representations.Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero.They also lose information when learning relations with reflexive patterns since they become symmetric and transitive.We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework which enables collaborative combinations of latent distance-based terms (MDE).Our solution is based on two principles:1) using limit-based loss instead of margin ranking loss and2) by learning independent embedding vectors for each of terms we can collectively train and predict using contradicting distance terms.We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns.We propose MDE as a neural network model which allows us to map non-linear relations between the embedding vectors and the expected output of the score function.Our empirical results show that MDE outperforms the state-of-the-art embedding models on several benchmark datasets.

Un nuovo metodo di modellazione dei Knowledge Graph basato su Distance Embedding e reti neurali


Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning.However, it suffers from the problem of unstable training.In this paper, we found that the instability is mostly due to the vanishing gradients on the generator.To remedy this issue, we propose a new method to use collaborative training to improve the stability of semi-supervised GAN with the combination of Wasserstein GAN.The experiments have shown that our proposed method is more stable than the original Improved GAN and achieves comparable classification accuracy on different data sets.

Migliorare la stabilità del training delle reti generative semi-supervisionate con il training collaborativo


It has long been known that a single-layer fully-connected neural network with an i.i.d.prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.  This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of evaluating the corresponding GP.Recently, kernel functions which mimic multi-layer random neural networks have been developed, but only outside of a Bayesian framework.As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network.In this work, we derive the exact equivalence between infinitely wide, deep, networks and GPs with a particular covariance function.We further develop a computationally efficient pipeline to compute this covariance function.We then use the resulting GP to perform Bayesian inference for deep neural networks on MNIST and CIFAR-10.  We observe that the trained neural network accuracy approaches that of the corresponding GP with increasing layer width, and that the GP uncertainty is strongly correlated with trained network prediction error.We further find that test performance increases as finite-width trained networks are made wider and  more  similar  to  a  GP,  and  that  the  GP-based  predictions  typically  outperform  those  of  finite-width  networks.Finally  we  connect  the  prior  distribution over weights and variances in our GP formulation to the recent development of signal propagation in random neural networks.

Mostriamo come fare predizioni usando deep network, senza addestrare deep network.


Search space is a key consideration for neural architecture search.Recently, Xie et al. (2019a) found that randomly generated networks from the same distribution perform similarly, which suggest we should search for random graph distributions instead of graphs.We propose graphon as a new search space.A graphon is the limit of Cauchy sequence of graphs and a scale-free probabilistic distribution, from which graphs of different number of vertices can be drawn.This property enables us to perform NAS using fast, low-capacity models and scale the found models up when necessary.We develop an algorithm for NAS in the space of graphons and empirically demonstrate that it can find stage-wise graphs that outperform DenseNet and other baselines on ImageNet.

Graphon è un buon spazio di ricerca per la ricerca di architetture neurali e produce reti buone dal punto di vista empirico.


Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.Despite its success as a defense mechanism, adversarial training fails to generalize well to unperturbed test set.We hypothesize that this poor generalization is a consequence of adversarial training with uniform perturbation radius around every training sample.Samples close to decision boundary can be morphed into a different class under a small perturbation budget, and enforcing large margins around these samples produce poor decision boundaries that generalize poorly.Motivated by this hypothesis, we propose instance adaptive adversarial training -- a technique that enforces sample-specific perturbation margins around every training sample.We show that using our approach, test accuracy on unperturbed samples improve with a marginal drop in robustness.Extensive experiments on CIFAR-10, CIFAR-100 and Imagenet datasets demonstrate the effectiveness of our proposed approach.

Adversarial learning adattivo all'istanza per migliorare il compromesso robustezza-accuratezza


Machine learning models including traditional models and neural networks can be easily fooled by adversarial examples which are generated from the natural examples with small perturbations.  This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection.  Unfortunately, even state-of-the-art defense approaches such as adversarial training and defensive distillation still suffer from major limitations and can be circumvented.  From a unique angle, we propose to investigate two important research questions in this paper: Are adversarial examples distinguishable from natural examples?  Are adversarial examples generated by different methods distinguishable from each other?  These two questions concern the distinguishability of adversarial examples.  Answering them will potentially lead to a simple yet effective approach, termed as defensive distinction in this paper under the formulation of multi-label classification, for protecting against adversarial examples.  We design and perform experiments using the MNIST dataset to investigate these two questions, and obtain highly positive results demonstrating the strong distinguishability of adversarial examples.  We recommend that this unique defensive distinction approach should be seriously considered to complement other defense approaches.

Proponiamo un approccio difensivo di protezione della distinzione e dimostriamo la forte distinguibilità degli adversarial example.


For a long time, designing neural architectures that exhibit high performance was considered a dark art that required expert hand-tuning.One of the few well-known guidelines for architecture design is the avoidance of exploding or vanishing gradients.However, even this guideline has remained relatively vague and circumstantial, because there exists no well-defined, gradient-based metric that can be computed {\it before} training begins and can robustly predict the performance of the network {\it after} training is complete.We introduce what is, to the best of our knowledge, the first such metric: the nonlinearity coefficient (NLC).Via an extensive empirical study, we show that the NLC, computed in the network's randomly initialized state, is a powerful predictor of test error and that attaining a right-sized NLC is essential for attaining an optimal test error, at least in fully-connected feedforward networks.The NLC is also conceptually simple, cheap to compute, and is robust to a range of confounders and architectural design choices that comparable metrics are not necessarily robust to.Hence, we argue the NLC is an important tool for architecture search and design, as it can robustly predict poor training outcomes before training even begins.

Introduciamo la NLC, una metrica che è economica da calcolare nello stato di inizializzazione casuale delle reti ed è altamente predittiva della generalizzazione, almeno nelle reti completamente connesse.


This paper gives a rigorous analysis of trained Generalized Hamming Networks (GHN) proposed by Fan (2017) and discloses an interesting finding about GHNs, i.e. stacked convolution layers in a GHN is equivalent to a single yet wide convolution layer.The revealed equivalence, on the theoretical side, can be regarded as a constructive manifestation of the universal approximation theorem Cybenko (1989); Hornik (1991).In practice, it has profound and multi-fold implications.For network visualization, the constructed deep epitomes at each layer provide a visualization of network internal representation that does not rely on the input data.Moreover, deep epitomes allows the direct extraction of features in just one step, without resorting to regularized optimizations used in existing visualization tools.


There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations.  The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level.  The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause misclassification, also known as the margin of an input feature.  While the former paradigm is well-resolved, the latter is not.  Existing zero-confidence attacks either introduce significant approximation errors, or are too time-consuming.  We therefore propose MarginAttack, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency.  Our experiments show that MarginAttack is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation attacks.  In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.

Questo articolo introduce MarginAttack, un attacco adversarial a confidenza zero più forte e veloce.


Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field.Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly.The degree of variation and diversity of inputs makes this a difficult task.Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data.We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity.We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input.Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.

L'ottimizzazione non supervisionata durante l'inferenza fornisce un feedback top-down per regolare iterativamente la predizione feedforward della variazione di scala per un riconoscimento più equivariante.


The Deep Image Prior (DIP, Ulyanov et al., 2017) is a fascinating recent approach for recovering images which appear natural, yet is not fully understood.This work aims at shedding some further light on this approach by investigating the properties of the early outputs of the DIP.First, we show that these early iterations demonstrate invariance to adversarial perturbations by classifying progressive DIP outputs and using a novel saliency map approach.Next we explore using DIP as a defence against adversaries, showing good potential.Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features.By comparing classification confidence values we show some evidence confirming this hypothesis.

Indaghiamo le proprietà del Prior dell'Immagine Profonda recentemente introdotto (Ulyanov et al, 2017)


In this paper, we address the challenge of limited labeled data and class imbalance problem for machine learning-based rumor detection on social media.We present an offline data augmentation method based on semantic relatedness for rumor detection.To this end, unlabeled social media data is exploited to augment limited labeled data.A context-aware neural language model and a large credibility-focused Twitter corpus are employed to learn effective representations of rumor tweets for semantic relatedness measurement.A language model fine-tuned with the a large domain-specific corpus shows a dramatic improvement on training data augmentation for rumor detection over pretrained language models.We conduct experiments on six different real-world events based on five publicly available data sets and one augmented data set.Our experiments show that the proposed method allows us to generate a larger training data with reasonable quality via weak supervision.We present preliminary results achieved using a state-of-the-art neural network model with augmented data for rumor detection.

Proponiamo una metodologia per aumentare i dati pubblicamente disponibili per gli studi sul rumor basati sulla correlazione semantica tra dati limitati annotati e non annotati.


Self-attention is a useful mechanism to build generative models for language and images.It determines the importance of context elements by comparing each element to the current time step.In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results.Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention.We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic.Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models.On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.

Le convoluzioni dinamiche leggere sono competitive all'auto-attention nei task linguistici.


Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards efficient GAN training depends crucially on moving beyond this classic framework.To make piecemeal progress along these lines, we analyze the behavior of mirror descent (MD) in a class of non-monotone problems whose solutions coincide with those of a naturally associated variational inequality – a property which we call coherence.We first show that ordinary, “vanilla” MD converges under a strict version of this condition, but not otherwise; in particular, it may fail to converge even in bilinear models with a unique solution.We then show that this deficiency is mitigated by optimism: by taking an “extra-gradient” step, optimistic mirror descent (OMD) converges in all coherent problems.Our analysis generalizes and extends the results of Daskalakis et al. [2018] for optimistic gradient descent (OGD) in bilinear problems, and makes concrete headway for provable convergence beyond convex-concave games.We also provide stochastic analogues of these results, and we validate our analysis by numerical experiments in a wide array of GAN models (including Gaussian mixture models, and the CelebA and CIFAR-10 datasets).

Mostriamo come l'inclusione di un step extra-gradiente nei metodi di training GAN del primo ordine può migliorare la stabilità e portare a risultati di convergenza migliori.


Batch normalization (BN) is often used in an attempt to stabilize and accelerate training in deep neural networks.In many cases it indeed decreases the number of parameter updates required to achieve low training error.However, it also reduces robustness to small adversarial input perturbations and common corruptions by double-digit percentages, as we show on five standard datasets.Furthermore, we find that substituting weight decay for BN is sufficient to nullify a relationship between adversarial vulnerability and the input dimension.A recent mean-field analysis found that BN induces gradient explosion when used on multiple layers, but this cannot fully explain the vulnerability we observe, given that it occurs already for a single BN layer.We argue that the actual cause is the tilting of the decision boundary with respect to the nearest-centroid classifier along input dimensions of low variance.As a result, the constant introduced for numerical stability in the BN step acts as an important hyperparameter that can be tuned to recover some robustness at the cost of standard test accuracy.We explain this mechanism explicitly on a linear ``toy model and show in experiments that it still holds for nonlinear ``real-world models.

La batch normalization riduce la robustezza in fase di test alle corruzioni comuni e agli adversarial example.


Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning.In this paper, we explore learning an optimization algorithm for training shallow neural nets.Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms.We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture.More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.

Impariamo un algoritmo di ottimizzazione che generalizza ai task non visti


The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks.Nevertheless, the functional form of this dependency remains elusive.In this work, we present a functional form which approximates well the generalization error in practice.Capitalizing on the successful concept of model scaling (e.g., width, depth), we are able to simultaneously construct such a form and specify the exact models which can attain it across model/data scales.Our construction follows insights obtained from observations conducted over a range of model/data scales, in various model types and datasets, in vision and language tasks.We show that the form both fits the observations well across scales, and provides accurate predictions from small- to large-scale models and data.

Prevediamo l'errore di generalizzazione e specifichiamo il modello che lo raggiunge attraverso le scale modello/dati.


Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions.Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state.This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations.We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.

Metodo di reinforcement learning unsupervised per l'apprendimento di una policy per raggiungere in modo robusto obiettivi specificati percettivamente.


State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process.In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence.Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity.On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.

Modello di sequenza che regola dinamicamente la quantità di calcolo per ogni input.


Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming a distribution p(z) over latent space, and an inference model that infers likely latent codes for each data point (Kingma and Welling, 2013).Recent work shows that traditional training methods tend to yield solutions that violate modeling desiderata: (1) the learned generative model captures the observed data distribution but does so while ignoring the latent codes, resulting in codes that do not represent the data (e.g. van den Oord et al. (2017); Kim et al. (2018)); (2) the aggregate of the learned latent codes does not match the prior p(z).This mismatch means that the learned generative model will be unable to generate realistic data with samples from p(z)(e.g. Makhzani et al. (2015); Tomczak and Welling (2017)).In this paper, we demonstrate that both issues stem from the fact that the global optima of the VAE training objective often correspond to undesirable solutions.Our analysis builds on two observations: (1) the generative model is unidentifiable – there exist many generative models that explain the data equally well, each with different (and potentially unwanted) properties and (2) bias in the VAE objective – the VAE objective may prefer generative models that explain the data poorly but have posteriors that are easy to approximate.We present a novel inference method, LiBI, mitigating the problems identified in our analysis.On synthetic datasets, we show that LiBI can learn generative models that capture the data distribution and inference models that better satisfy modeling assumptions when traditional methods struggle to do so.

Caratterizziamo gli ottimi globali problematici dell'obiettivo VAE e presentiamo un nuovo metodo di inferenza per evitare tali ottimi.


Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering.Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from unlabeled text.  Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy.  This paper introduces {\it distributional inclusion vector embedding (DIVE)}, a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts.In experimental evaluations more comprehensive than any previous literature of which we are aware---evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions---we find that our method provides up to double the precision of previous unsupervised methods, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results.In addition, the meaning of each dimension in DIVE is interpretable, which leads to a novel approach on word sense disambiguation as another promising application of DIVE.

Proponiamo un nuovo tipo di word embedding unsupervised che conserva la proprietà di inclusione nella distribuzione del contesto e raggiungiamo risultati allo stato dell'arte nel rilevamento unsupervised dell'ipernimia


Continual learning aims to learn new tasks without forgetting previously learned ones.This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity.Current regularization-based continual learning algorithms  need an external representation and extra computation to measure the parameters' \textit{importance}.In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in  networks.Uncertainty is a natural way to identify \textit{what to remember} and \textit{what to change} as we continually learn,  and thus mitigate catastrophic forgetting.We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks.We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches.Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.

Un approccio basato sulla regolarizzazione per continual learning utilizzando reti neurali bayesiane per prevedere l'importanza dei parametri


Humans have a natural curiosity to imagine what it feels like to exist as someone or something else.This curiosity becomes even stronger for the pets we care for.Humans cannot truly know what it is like to be our pets, but we can deepen our understanding of what it is like to perceive and explore the world like them.We investigate how wearables can offer people animal perspective-taking opportunities to experience the world through animal senses that differ from those biologically natural to us.To assess the potential of wearables in animal perspective-taking, we developed a sensory-augmenting wearable that gives wearers cat-like whiskers.We then created a maze exploration experience where blindfolded participants utilized the whiskers to navigate the maze.We draw on animal behavioral research to evaluate how the whisker activity supported authentically cat-like experiences, and discuss the implications of this work for future learning experiences.

Questo articolo esplora l'uso di una tecnologia di aumento sensoriale indossabile per facilitare la presa di prospettiva in prima persona di cosa voglia dire avere i baffi da gatto.


Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data.Proving tight generalization error bounds is a central question in statistical learning  theory.   In  this  paper,  we  obtain  generalization  error  bounds  for  learning general  non-convex  objectives,  which  has  attracted  significant  attention  in  recent years.   We develop a new framework,  termed Bayes-Stability,  for proving algorithm-dependent generalization error bounds.  The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability.  Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD).Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018) and improves upon the results in Pensia et al. (2018).  Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a).We also study the setting where the total loss is the sum of a bounded loss and an additiona l`2 regularization term.We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time.Our new bounds are more desirable when the noise level of the processis not very small, and do not become vacuous even when T tends to infinity.

Diamo alcuni bound sull'errore di generalizzazione dei metodi a gradiente rumoroso come SGLD, le dinamiche di Langevin, noisy momentum e così via.


In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models.In the proposed method, the mixture of multiple dynamics models is used to approximate the true unknown transition probability, and the intrinsic reward is designed as the minimum of the surprise seen from each dynamics model to the mixture of the dynamics models.In order to show the effectiveness of the proposed intrinsic reward generation method, a working algorithm is constructed by combining the proposed intrinsic reward generation method with the proximal policy optimization (PPO) algorithm.Numerical results show that for representative locomotion tasks, the proposed model-ensemble-based intrinsic reward generation method outperforms the previous methods based on a single dynamics model.

Per lo sparse-reward reinforcement learning, l'ensemble di modelli dinamici multipli è usato per generare la reward intrinseca progettata come il minimo della sorpresa.


Given the fast development of analysis techniques for NLP and speechprocessing systems, few systematic studies have been conducted tocompare the strengths and weaknesses of each method. As a step inthis direction we study the case of representations of phonology inneural network models of spoken language.We use two commonly appliedanalytical techniques, diagnostic classifiers and representationalsimilarity analysis, to quantify to what extent neural activationpatterns encode phonemes and phoneme sequences.We manipulate twofactors that can affect the outcome of analysis.First, we investigatethe role of learning by comparing neural activations extracted fromtrained versus randomly-initialized models.Second, we examine thetemporal scope of the activations by probing both local activationscorresponding to a few milliseconds of the speech signal, and globalactivations pooled over the whole utterance.We conclude thatreporting analysis results with randomly initialized models iscrucial, and that global-scope methods tend to yield more consistentand interpretable results and we recommend their use as a complementto local-scope diagnostic methods.

Studiamo le rappresentazioni della fonologia nei modelli di rete neurale del linguaggio parlato con diverse varianti di tecniche analitiche.


Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.Different from traditional SR models, this study concentrates on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) image, which widely exists on the Internet.In general, C-JPG can release storage space while keeping considerable quality in visual.However, further image processing operations, e.g., SR, will suffer from enlarging inner artificial details and result in unacceptable outputs.To address this problem, we propose a novel SR structure with two specifically designed components, as well as a cycle loss.In short, there are mainly three contributions to this paper.First, our research can generate high-qualified SR images for prevalent C-JPG images.Second, we propose a functional sub-model to recover information for C-JPG images, instead of the perspective of noise elimination in traditional SR approaches.Third, we further integrate cycle loss into SR solver to build a hybrid loss function for better SR generation.Experiments show that our approach achieves outstanding performance among state-of-the-art methods.

Risolviamo il problema specifico di SR delle immagini JPG di bassa qualità tramite sottomodelli funzionali.


Keyword spotting—or wakeword detection—is an essential feature for hands-free operation of modern voice-controlled devices.With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword.In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection.The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording.Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system.DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.

Proponiamo un modello interpretabile per rilevare le wakewords scelte dall'utente che impara dagli esempi dell'utente.


To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.One way of constructing such representations is by focusing on the important events in a sequence.In this paper, we propose a model that learns both to discover such key events (or keyframes) as well as to represent the sequence in terms of them.  We do so using a hierarchical Keyframe-Inpainter (KeyIn) model that first generates keyframes and their temporal placement and then inpaints the sequences between keyframes.We propose a fully differentiable formulation for efficiently learning the keyframe placement.We show that KeyIn finds informative keyframes in several datasets with diverse dynamics.When evaluated on a planning task, KeyIn outperforms other recent proposals for learning hierarchical representations.

Proponiamo un modello che impara a scoprire frame informativi in una sequenza video futura e a rappresentare il video attraverso i suoi keyframe.


This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder.Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks.We further control characteristics of the representation by matching to a prior distribution adversarially.Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures.DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.

Impariamo la deep representation massimizzando l'informazione reciproca, sfruttando la struttura nell'obiettivo, e siamo in grado di calcolare con classificatori completamente supervisionati aventi architetture comparabili


Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and material engineering.The most common way to estimate the atomic importance is to compute the electronic structure using density-functional theory (DFT), and then to interpret it using domain knowledge of human experts.However, this conventional approach is impractical to the large molecular database because DFT calculation requires huge computation, specifically, O(n^4) time complexity w.r.t. the number of electrons in a molecule.Furthermore, the calculation results should be interpreted by the human experts to estimate the atomic importance in terms of the target molecular property.To tackle this problem, we first exploit machine learning-based approach for the atomic importance estimation.To this end, we propose reverse self-attention on graph neural networks and integrate it with graph-based molecular description.Our method provides an efficiently-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics.

Proponiamo innanzitutto uno stimatore d'importanza atomica completamente automatizzato e target-directed, basato sulle graph neural network e su un nuovo concetto di self-attention inversa.


Spectral clustering is a leading and popular technique in unsupervised data analysis.  Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension).In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcomings.Our network, which we call SpectralNet, learns a map that embeds input data points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them.We train SpectralNet using a procedure that involves constrained stochastic optimization.Stochastic optimization allows it to scale to large datasets, while the constraints, which are implemented using a special purpose output layer, allow us to keep the network output orthogonal.Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points.To further improve the quality of the clustering, we replace the standard pairwise Gaussian affinities with affinities leaned from unlabeled data using a Siamese network.  Additional improvement can be achieved by applying the network to code representations produced, e.g., by standard autoencoders.Our end-to-end learning procedure is fully unsupervised.In addition, we apply VC dimension theory to derive a lower bound on the size of  SpectralNet.  State-of-the-art clustering results are reported for both the MNIST and Reuters datasets.

Clustering spettrale unsupervised utilizzando deep neural network


Meta-learning methods, most notably Model-Agnostic Meta-Learning (Finn et al, 2017) or MAML, have achieved great success in adapting to new tasks quickly, after having been trained on similar tasks.The mechanism behind their success, however, is poorly understood.We begin this work with an experimental analysis of MAML, finding that deep models are crucial for its success, even given sets of simple tasks where a linear model would suffice on any individual task.Furthermore, on image-recognition tasks, we find that the early layers of MAML-trained models learn task-invariant features, while later layers are used for adaptation, providing further evidence that these models require greater capacity than is strictly necessary for their individual tasks.Following our findings, we propose a method which enables better use of model capacity at inference time by separating the adaptation aspect of meta-learning into parameters that are only used for adaptation but are not part of the forward model.We find that our approach enables more effective meta-learning in smaller models, which are suitably sized for the individual tasks.

Troviamo che i deep model sono cruciali per il funzionamento di MAML e proponiamo un metodo che permetta un meta-learning efficace nei modelli più piccoli.


Convolutional Neural Networks continuously advance the progress of 2D and 3D image and object classification.The steadfast usage of this algorithm requires constant evaluation and upgrading of foundational concepts to maintain progress.Network regularization techniques typically focus on convolutional layer operations, while leaving pooling layer operations without suitable options.We introduce Wavelet Pooling as another alternative to traditional neighborhood pooling.This method decomposes features into a second level decomposition, and discards the first-level subbands to reduce feature dimensions.This method addresses the overfitting problem encountered by max pooling, while reducing features in a more structurally compact manner than pooling via neighborhood regions.Experimental results on four benchmark classification datasets demonstrate our proposed method outperforms or performs comparatively with methods like max, mean, mixed, and stochastic pooling.

Il pooling si ottiene usando le wavelets invece dei tradizionali approcci di neighborhood (massimo, medio, ecc.).


Dynamic ridesharing services (DRS) play a major role in improving the efficiency of urban transportation.User satisfaction in dynamic ridesharing is determined by multiple factors such as travel time, cost, and social compatibility with co-passengers.Existing DRS optimize profit by maximizing the operational value for service providers or minimize travel time for users but they neglect the social experience of riders, which significantly influences the total value of the service to users.We propose DROPS, a dynamic ridesharing framework that factors the riders' social preferences in the matching process so as to improve the quality of the trips formed.Scheduling trips for users is a multi-objective optimization that aims to maximize the operational value for the service provider, while simultaneously maximizing the value of the trip for the users.The user value is estimated based on compatibility between co-passengers and the ride time.We then present a real-time matching algorithm for trip formation.Finally, we evaluate our approach empirically using real-world taxi trips data, and a population model including social preferences based on user surveys.The results demonstrate improvement in riders' social compatibility, without significantly affecting the vehicle miles for the service provider and travel time for users.

Proponiamo un nuovo framework dinamico di ridesharing per formare viaggi che ottimizza sia il valore operativo per il fornitore di servizi che il valore utente per i passeggeri, inserendo le preferenze sociali degli utenti nel processo decisionale.


Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction.To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subspaces') in which adversarial examples lie.We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID).LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors.We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the distinction of adversarial examples generated using state-of-the-art attacks.As a proof-of-concept, we show that a potential application of LID is to distinguish adversarial examples, and the preliminary results show that it can outperform several state-of-the-art detection measures by large margins for five attack strategies considered in this paper across three benchmark datasets.Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.

Caratterizziamo le proprietà dimensionali dei sottospazi avversari nel neighborhood degli adversarial example attraverso l'uso della dimensionalità intrinseca locale (LID).


In this paper, we design and analyze a new zeroth-order (ZO) stochastic optimization algorithm, ZO-signSGD, which enjoys dual advantages of gradient-free operations and signSGD.The latter requires only the sign information of  gradient estimates but is able to achieve a comparable  or even better convergence speed than SGD-type algorithms.Our study  shows that ZO signSGD requires $\sqrt{d}$ times more iterations than signSGD, leading to a convergence rate of  $O(\sqrt{d}/\sqrt{T})$ under mild conditions, where $d$ is the number of optimization variables, and $T$ is the number of iterations.In addition, we analyze the effects of different types of gradient estimators on the convergence of ZO-signSGD, and propose two variants of ZO-signSGD that  at least  achieve $O(\sqrt{d}/\sqrt{T})$ convergence rate.On the application side we explore the connection between ZO-signSGD and  black-box adversarial attacks in robust deep learning.  Our empirical evaluations on image classification datasets MNIST and CIFAR-10 demonstrate the superior performance of ZO-signSGD on the generation of   adversarial examples from black-box neural networks.

Progettiamo e analizziamo un nuovo algoritmo di ottimizzazione stocastica di ordine zero, ZO-signSGD, e dimostriamo la sua connessione e applicazione agli attacchi avversari black-box nel deep learning robusto


The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors.This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and resulting in increased cost of operation.This research paper proposes a unified architecture for multi-time-horizon solar forecasting for short and long-term predictions using Recurrent Neural Networks (RNN).The paper describes an end-to-end pipeline to implement the architecture along with methods to test and validate the performance of the prediction model.The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best performing methods which use one model for each time-horizon.The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.

Questo articolo propone un'architettura di rete neurale ricorrente unificata per la predizione solare a breve termine con orizzonte temporale multiplo e convalida i guadagni di prestazione della predizione rispetto ai metodi precedentemente riportati


The ResNet and the batch-normalization (BN) achieved high performance even when only a few labeled data are available.However, the reasons for its high performance are unclear.To clear the reasons, we analyzed the effect of the skip-connection in ResNet and the BN on the data separation ability, which is an important ability for the classification problem.Our results show that, in the multilayer perceptron with randomly initialized weights, the angle between two input vectors converges to zero in an exponential order of its depth, that the skip-connection makes this exponential decrease into a sub-exponential decrease, and that the BN relaxes this sub-exponential decrease into a reciprocal decrease.Moreover, our analysis shows that the preservation of the angle at initialization encourages trained neural networks to separate points from different classes.These imply that the skip-connection and the BN improve the data separation ability and achieve high performance even when only a few labeled data are available.

La Skip-connection in ResNet e la batch normalization migliorano la capacità di separazione dei dati e aiutano ad addestrare una deep neural network.


Learning representations of data is an important issue in machine learning.Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead.GAN tends to produce the data simply without any information about the manifold of the data, which hinders from controlling desired features to generate.Moreover, most of GAN’s have a large size of manifold, resulting in poor scalability.In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data to generate and learns a representation of the data efficiently.Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution.As the larger scale of latent space caused by deploying various distributions in one latent space makes training unstable while maintaining the dimension of latent space, we need to separate the process of defining the distributions explicitly and operation of generation.We prove that a VAE is proper for the former and modify a loss function of VAE to map the data into the pre-defined latent space so as to locate the reconstructed data as close to the input data according to its characteristics.Moreover, we add the KL divergence to the loss function of LSC-GAN to include this process.The decoder of VAE, which generates the data with the corresponding features from the pre-defined latent space, is used as the generator of the LSC-GAN.Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method to generate desired data stably and efficiently, achieving a high compression ratio that can hold about 24 pixels of information in each dimension of latent space.Besides, our model learns the reverse of features such as not laughing (rather frowning) only with data of ordinary and smiling facial expression.

Proponiamo un modello generativo che non solo produce dati con le feature desiderate dallo spazio latente predefinito, ma comprende anche completamente le feature dei dati per creare feature che non sono nel dataset.


With the ever increasing demand and the resultant reduced quality of services, the focus has shifted towards easing network congestion to enable more efficient flow in systems like traffic, supply chains and electrical grids.A step in this direction is to re-imagine the traditional heuristics based training of systems as this approach is incapable of modelling the involved dynamics.While one can apply Multi-Agent Reinforcement Learning (MARL) to such problems by considering each vertex in the network as an agent, most MARL-based models assume the agents to be independent.In many real-world tasks, agents need to behave as a group, rather than as a collection of individuals.In this paper, we propose a framework that induces cooperation and coordination amongst agents, connected via an underlying network, using emergent communication in a MARL-based setup.We formulate the problem in a general network setting and demonstrate the utility of communication in networks with the help of a case study on traffic systems.Furthermore, we study the emergent communication protocol and show the formation of distinct communities with grounded vocabulary.To the best of our knowledge, this is the only work that studies emergent language in a networked MARL setting.

Un framework per studiare la comunicazione emergente in una configurazione di reinforcement learning multi-agente in rete.


We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images.The model embeds data sets into an infinite-dimensional function space, as opposed to finite-dimensional vector spaces.To formalize this notion, we extend the theory of neural representations of sets to include functional representations, and demonstrate that any translation-equivariant embedding can be represented using a convolutional deep-set.We evaluate ConvCNPs in several settings, demonstrating that they achieve state-of-the-art performance compared to existing NPs.We demonstrate that building in translation equivariance enables zero-shot generalization to challenging, out-of-domain tasks.

Estendiamo i deep set agli embedding funzionali e ai processi neurali per includere membri equivarianti rispetto alla traslazione


Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately.Recent work shows that convolutional neural networks (CNNs) can be trained to predict V1 activity more accurately, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance.Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations.We present a framework for identifying common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations.We fit this rotation-equivariant CNN to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging.We show that our rotation-equivariant network outperforms a regular CNN with the same number of feature maps and reveals a number of common features, which are shared by many V1 neurons and are pooled sparsely to predict neural activity.Our findings are a first step towards a powerful new tool to study the nonlinear functional organization of visual cortex.

Un modello CNN rotazionale-equivariante di V1 che supera i modelli precedenti e suggerisce raggruppamenti funzionali di neuroni V1.


In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional.  Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective.  This generalized functional encompasses most modern inference procedures and suggests novel ones.

Rideriva un'ampia classe di procedure di inferenza da un obiettivo di global information bottleneck.


In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant.The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target.In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input.This is typically the case when we have a standard conditioning input, such as a state observation, and a ``privileged'' input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent.In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication).Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access.In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not.We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.

Il training di agenti con calcolo adattivo basato sul global information bottleneck può promuovere la generalizzazione.


Breathing exercises are an accessible way to manage stress and many mental illness symptoms.Traditionally, learning breathing exercises involved in-person guidance or audio recordings.The shift to mobile devices has led to a new way of learning and engaging in breathing exercises as seen in the rise of multiple mobile applications with different breathing representations.However, limited work has been done to investigate the effectiveness of these visual representations in supporting breathing pace as measured by synchronization.We utilized a within-subjects study to evaluate four common breathing visuals to understand which is most effective in providing breathing exercise guidance.Through controlled lab studies and interviews, we identified two representations with clear advantages over the others.In addition, we found that auditory guidance was not preferred by all users.We identify potential usability issues with the representations and suggest design guidelines for future development of app-supported breathing training.

Abbiamo utilizzato uno studio intra-soggetto per valutare quattro visual di respirazione ritmata comuni nelle applicazioni mobile per capire qual è più efficace nel fornire una guida all'esercizio della respirazione.


Current work on neural code synthesis consists of increasingly sophisticated architectures being trained on highly simplified domain-specific languages, using uniform sampling across program space of those languages for training.By comparison, program space for a C-like language is vast, and extremely sparsely populated in terms of `useful' functionalities; this requires a far more intelligent approach to corpus generation for effective training.We use a genetic programming approach using an iteratively retrained discriminator to produce a population suitable as labelled training data for a neural code synthesis architecture.We demonstrate that use of a discriminator-based training corpus generator, trained using only unlabelled problem specifications in classic Programming-by-Example format, greatly improves network performance compared to current uniform sampling techniques.

Un modo per generare training corpora per la neural code synthesis usando un discriminatore addestrato su dati non annotati


Neural reading comprehension models have recently achieved impressive gener- alisation results, yet still perform poorly when given adversarially selected input.Most prior work has studied semantically invariant text perturbations which cause a model’s prediction to change when it should not.In this work we focus on the complementary problem: excessive prediction undersensitivity where input text is meaningfully changed, and the model’s prediction does not change when it should.We formulate a noisy adversarial attack which searches among semantic variations of comprehension questions for which a model still erroneously pro- duces the same answer as the original question – and with an even higher prob- ability.We show that – despite comprising unanswerable questions – SQuAD2.0 and NewsQA models are vulnerable to this attack and commit a substantial frac- tion of errors on adversarially generated questions.This indicates that current models—even where they can correctly predict the answer—rely on spurious sur- face patterns and are not necessarily aware of all information provided in a given comprehension question.Developing this further, we experiment with both data augmentation and adversarial training as defence strategies: both are able to sub- stantially decrease a model’s vulnerability to undersensitivity attacks on held out evaluation data.Finally, we demonstrate that adversarially robust models gener- alise better in a biased data setting with a train/evaluation distribution mismatch; they are less prone to overly rely on predictive cues only present in the training set and outperform a conventional model in the biased data setting by up to 11% F1.

Dimostriamo la vulnerabilità agli attacchi undersensitivity nei modelli di neural reading comprehension SQuAD2.0 e NewsQA, dove il modello predice la stessa risposta con maggiore confidenza a domande scelte in modo adversarial, confrontiamo inoltre le strategie di difesa.


This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters.This representation is language-independent with no need of pretraining and produces an encoding with no information loss.It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset.Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries.As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level.We apply two variants of CNN coupled with it.Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.

L'utilizzo di tecniche di compressione per l'encoding delle parole è una possibilità per un training più veloce delle CNN e la riduzione della dimensionalità della rappresentazione


Sequence prediction models can be learned from example sequences with a variety of training algorithms.Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time. Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.A rich set of other algorithms, such as data noising, RAML, and softmax policy gradient, have also been developed from different perspectives. In this paper, we present a formalism of entropy regularized policy optimization, and show that the apparently distinct algorithms, including MLE, can be reformulated as special instances of the formulation.The difference between them is characterized by the reward function and two weight hyperparameters.The unifying interpretation enables us to systematically compare the algorithms side-by-side, and gain new insights into the trade-offs of the algorithm design.The new perspective also leads to an improved approach that dynamically interpolates among the family of algorithms, and learns the model in a scheduled way.Experiments on machine translation, text summarization, and game imitation learning demonstrate superiority of the proposed approach.

Un formalismo di ottimizzazione della policy regolarizzata dall'entropia sussume un insieme di algoritmi di apprendimento per sequence prediction. Un nuovo algoritmo di interpolazione con risultati migliori su text generation e su imitation learning nei giochi.


Origin-Destination (OD) flow data is an important instrument in transportation studies.Precise prediction of customer demands from each original location to a destination given a series of previous snapshots helps  ride-sharing platforms to better understand their market mechanism.However, most existing prediction methods ignore the network structure of OD flow data and fail to utilize the topological dependencies among related OD pairs.In this paper, we propose a latent spatial-temporal origin-destination (LSTOD) model, with a novel convolutional neural network (CNN) filter to learn the spatial features of OD pairs from a graph perspective and an attention structure to capture their long-term periodicity.Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrate the advantage of LSTOD in achieving at least 6.5% improvement in prediction accuracy over the second best model.

Proponiamo un modello CNN puramente convoluzionale con meccanismo di attention per prevedere i flussi spazio-temporali origin-destination.


Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples.However, adversarially trained models often lack adversarially robust generalization on unseen testing data.Recent works show that adversarially trained models are more biased towards global structure features.Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation.To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples.We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples.To demonstrate the generality of our argument, we implement RLFAT in currently state-of-the-art adversarial training frameworks.Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training.Additionally, we demonstrate that our models capture more local features of the object on the images, aligning better with human perception.

Proponiamo un nuovo approccio di adversarial training chiamato Robust Local Features for Adversarial Training (RLFAT) che migliora significativamente sia la generalizzazione adversarially robust che la generalizzazione standard.


The verification of planning domain models is crucial to ensure the safety, integrity and correctness of planning-based automated systems.This task is usually performed using model checking techniques.  However, directly applying model checkers to verify planning domain models can result in false positives, i.e. counterexamples that are unreachable by a sound planner when using the domain under verification during a planning task.In this paper, we discuss the downside of unconstrained planning domain model verification.We then propose a fail-safe practice for designing planning domain models that can inherently guarantee the safety of the produced plans in case of undetected errors in domain models.  In addition, we demonstrate how model checkers, as well as state trajectory constraints planning techniques, should be used to verify planning domain models so that unreachable counterexamples are not returned.

Perché e come vincolare la verifica del modello di planning domain con obiettivi di pianificazione per evitare controesempi irraggiungibili (risultati di verifica falsi positivi).


We've seen tremendous success of image generating models these years.Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes.To imitate human drawing, interactions between the environment and the agent is required to allow trials.However, the environment is usually non-differentiable, leading to slow convergence and massive computation.In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation.We present  StrokeNet, a novel model where the agent is trained upon a well-crafted neural approximation of the painting environment.With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner.Our primary contribution is the neural simulation of a real-world environment.Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.

StrokeNet è una nuova architettura in cui l'agente è addestrato a disegnare a colpi su una simulazione differenziabile dell'ambiente, che potrebbe sfruttare efficacemente la potenza della back-propagation.


We demonstrate how machine learning is able to model experiments in quantum physics.Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography.Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels.Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it.To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states.In this work, we show that machine learning models can provide significant improvement over random search.We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves.This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Dimostriamo come il machine learning è in grado di modellare gli esperimenti di fisica quantistica.


In this paper, we propose a nonlinear unsupervised metric learning framework to boost of the performance of clustering algorithms.Under our framework, nonlinear distance metric learning and manifold embedding are integrated and conducted simultaneously to increase the natural separations among data samples.The metric learning component is implemented through feature space transformations, regulated by a nonlinear deformable model called Coherent Point Drifting (CPD).Driven by CPD, data points can get to a higher level of linear separability, which is subsequently picked up by the manifold embedding component to generate well-separable sample projections for clustering.Experimental results on synthetic and benchmark datasets show the effectiveness of our proposed approach over the state-of-the-art solutions in unsupervised metric learning.

Un framework di metric learning non lineare unsupervised per migliorare le prestazioni degli algoritmi di clustering.


As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can.As a result, machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program.At the same time, data owners would like to trade their data for its value, without having to first give away the data itself be- fore receiving compensation.It is difficult for data providers and model providers to agree on a fair price without first revealing the data or the trained model to the other side.Escrow systems only complicate this further, adding an additional layer of trust required of both parties.Currently, data owners and model owners don’t have a fair pricing system that eliminates the need to trust a third party and training the model on the data, which1) takes a long time to complete,2) does not guarantee that useful data is paid valuably and that useless data isn’t, without trusting in the third party with both the model and the data.Existing improve- ments to secure the transaction focus heavily on encrypting or approximating the data, such as training on encrypted data, and variants of federated learning.As powerful as the methods appear to be, we show them to be impractical in our use case with real world assumptions for preserving privacy for the data owners when facing black-box models.Thus, a fair pricing scheme that does not rely on secure data encryption and obfuscation is needed before the exchange of data.This pa- per proposes a novel method for fair pricing using data-model efficacy techniques such as influence functions, model extraction, and model compression methods, thus enabling secure data transactions.We successfully show that without running the data through the model, one can approximate the value of the data; that is, if the data turns out redundant, the pricing is minimal, and if the data leads to proper improvement, its value is properly assessed, without placing strong assumptions on the nature of the model.Future work will be focused on establishing a system with stronger transactional security against adversarial attacks that will reveal details about the model or the data to the other party.

Di fronte a modelli complessi, black-box, criptare i dati non è utilizzabile tanto quanto approssimare il modello e usarlo per dare un prezzo ad una potenziale transazione.


We present Line-Storm, an interactive computer system for creative performance.The context we investigated was writing on paper using Line-Storm.We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm.Line-Storm consisted of a writing stylus and writing pad, augmented with electronics.The writing pad was connected to a contact microphone, and the writing stylus had a small micro-controller board and peripherals attached to it.The signals from these electronic augmentations were fed into the audio-synthesis environment Max/MSP to produce an interactive soundscape.We attempted to discover whether Line-Storm enhanced a self-reported sense of being present and engaged during a writing task, and we compared Line-Storm to a non-interactive control condition.After performing statistical analysis in SPSS, we were unable to support our research hypothesis, that presence and engagement were enhanced by Line-Storm.Participants reported they were, on average, no more present and engaged during the experimental condition than during the control condition.As creativity is subtle, and varies with person, time, context,  space and so many other factors, this result was somewhat expected by us.A statistically significant result of our study is that some participants responded to Line-Storm more positively than others.These Preservers of Line-Storm were a group, distinct from other participants, who reported greater presence and engagement and who wrote more words with Line-Storm and during the control condition.We discuss the results of our research and place Line-Storm in an artistic-technological context, drawing upon writings by Martin Heidegger when considering the nature of Line-Storm.Future work includes modifying interactive components, improving aesthetics and using more miniaturized electronics, experimenting with a drawing task instead of a writing task, and collaborating with a composer of electronic music to make a more interesting, immersive, and engaging interactive soundscape for writing or drawing performance.

Sistema di scrittura interattivo capace di incorporare il suono basato su uno stilo


Language style transfer is the problem of migrating the content of a source sentence to a target style.In many applications, parallel training data are not available and source sentences to be transferred may have arbitrary and unknown styles.In this paper, we present an encoder-decoder framework under this problem setting.Each sentence is encoded into its content and style latent representations.By recombining the content with the target style, we can decode a sentence aligned in the target domain.To adequately constrain the encoding and decoding functions, we couple them with two loss functions.The first is a style discrepancy loss, enforcing that the style representation accurately encodes the style information guided by the discrepancy between the sentence style and the target style.The second is a cycle consistency loss, which ensures that the transferred sentence should preserve the content of the original sentence disentangled from its style.We validate the effectiveness of our proposed model on two tasks: sentiment modification of restaurant reviews, and dialog response revision with a romantic style.

Presentiamo un framework encoder-decoder per il transfer dello stile linguistico, che permette l'uso di dati non paralleli e di dati sorgente con vari stili linguistici sconosciuti.


In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs).Optimizing the standard L2 loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but fails to identify the exact eigenvectors.This downside originates from an invariance that cancels out in the global map.Here, we prove that our loss function eliminates this issue, i.e. the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix.For this new loss, we establish that all local minima are global optima and also show that computing the new loss (and also its gradients) has the same order of complexity as the classical loss.We report numerical results on both synthetic simulations, and a real-data PCA experiment on MNIST (i.e., a 60,000 x784 matrix), demonstrating our approach to be practically applicable and rectify previous LAEs' downsides.

Una nuova loss per PCA con autoencoder lineari che produce in modo dimostrabile autovettori ordinati esatti


Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations.However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (ER), can change, rendering the intended targets of user feedback unknown–a problem we term identity uncertainty.In this work, we present a framework for integrating user feedback into KBs in the presence of identity uncertainty.Our approach is based on having user feedback participate alongside mentions in ER.We propose a specific representation of user feedback as feedback mentions and introduce a new online algorithm for integrating these mentions into an existing KB.In experiments, we demonstrate that our proposed approach outperforms the baselines in 70% of experimental conditions.

Questo articolo sviluppa un framework per integrare il feedback degli utenti sotto l'incertezza dell'identità nelle knowledge base.


Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the algorithm's performance.Optimal poisoning attacks have already been proposed to evaluate worst-case scenarios, modelling attacks as a bi-level optimization problem.Solving these problems is computationally demanding and has limited applicability for some models such as deep networks.In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that degrade the classifier's accuracy when used for training.We propose a Generative Adversarial Net with three components: generator, discriminator, and the target classifier.This approach allows us to model naturally the detectability constrains that can be expected in realistic attacks and to identify the regions of the underlying data distribution that can be more vulnerable to data poisoning.Our experimental evaluation shows the effectiveness of our attack to compromise machine learning classifiers, including deep networks.

In questo articolo proponiamo un nuovo modello generativo per creare attacchi di poisoning sistematico con vincoli di rilevabilità contro i classificatori di apprendimento automatico, comprese le deep network.


The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning.It is often used as an efficient way to solve Maximum Likelihood (ML) and Maximum A Posteriori estimation problems, especially for models with latent variables.It is also the algorithm of choice to fit mixture models: generative models that represent unlabelled points originating from $k$ different processes, as samples from $k$ multivariate distributions.In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model.Given quantum access to a dataset of $n$ vectors of dimension $d$, our algorithm has convergence and precision guarantees similar to the classical algorithm, but the runtime is only polylogarithmic in the number of elements in the training set, and is polynomial in other parameters - as the dimension of the feature space, and the number of components in the mixture.We generalize further the algorithm by fitting any mixture model of base distributions in the exponential family.We discuss the performance of the algorithm on datasets that are expected to be classified successfully by those algorithms, arguing that on those cases we can give strong guarantees on the runtime.

È un algoritmo quantistico per Expectation Maximization. È veloce: il tempo di esecuzione dipende solo polilogaritmicamente dal numero di elementi nel dataset.


We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy.  The method has been tested for various data sets, and proven to be significantly more efficient than most existing compressing techniques in the deep learning literature.  For many popular data sets such as MNIST and CIFAR-10, more than 95% of the weights can be zeroed out without losing accuracy.In particular, we are able to make ResNet18 with 95% sparsity to have an accuracy that is comparable to that of a much larger model ResNet50 with the best 60% sparsity as reported in the literature.

Un algoritmo di ottimizzazione sparsa per deep CNN model.


Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics.Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation.Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon.Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training.We propose a simple alternative that still uses RL, but does not require learning a reward function.The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states.We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior.Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm.Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo.This paper is a proof of concept that illustrates how a simple imitation method based on RL with constant rewards can be as effective as more complex methods that use learned rewards.

Un'alternativa semplice ed efficace all'adversarial imitation learning: inizializzare il buffer di experience replay con le dimostrazioni, impostare la loro reward a +1, impostare la reward per tutti gli altri dati a 0, eseguire Q-learning o soft actor-critic per training.


Generating visualizations and interpretations from high-dimensional data is acommon problem in many fields.Two key approaches for tackling this problem are clustering and representation learning.There are very performant deepclustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps,on the other hand.However, current methods do not yet successfully combinethese two approaches.We present a new deep architecture for probabilistic clustering, VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM modules connected by LSTM cells.We show that they achieve superior clustering performance compared to current deep clustering methods on static MNIST/Fashion-MNIST data as well as medical time series, while inducing aninterpretable representation.Moreover, on the medical time series, VarTPSOMsuccessfully predicts future trajectories in the original data space.

Presentiamo una nuova architettura deep, VarPSOM, e la sua estensione ai dati delle serie temporali, VarTPSOM, che raggiungono prestazioni di clustering superiori rispetto agli attuali metodi di deep clustering su dati statici e temporali.


Many computer vision applications require solving multiple tasks in real-time.A neural network can be trained to solve multiple tasks simultaneously using 'multi-task learning'.This saves computation at inference time as only a single network needs to be evaluated.Unfortunately, this often leads to inferior overall performance as task objectives compete, which consequently poses the question: which tasks should and should not be learned together in one network when employing multi-task learning?We systematically study task cooperation and competition and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks.Our framework offers a time-accuracy trade-off and can produce better accuracy using less inference time than not only a single large multi-task neural network but also many single-task networks.

Analizziamo quali task è meglio imparare insieme in una rete e quali è meglio imparare separatamente.


Search engine has become a fundamental component in various web and mobile applications.Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries.In this paper, we explore a vector space search framework for document retrieval.Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding.Our model was trained based on BERT architecture.We deployed a fast k-nearest-neighbor index service for online serving.Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail queries

Una deep semantic framework per il document retrieval nei motori di ricerca testuali


Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training.SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful results on standard benchmark tasks such as image classification.In this work, we consider the general setting of  SSL problem where the labeled and unlabeled data  come from the same underlying probability distribution.We  propose a new approach that adopts  an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures to  provide pseudo-labels for the unlabeled data, which can then be used in conjunction with the initial labeled data to train the CNN model in an SSL manner.We have evaluated and compared our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our  SSL algorithm.

Proponiamo un nuovo algoritmo basato sul trasporto ottimale per addestrare una CNN in modo SSL.


Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks.In many cases it indeed decreases the number of parameter updates required to achieve low training error.However, it also reduces robustness to small adversarial input perturbations and noise by double-digit percentages, as we show on five standard datasets.Furthermore, substituting weight decay for batch norm is sufficient to nullify the relationship between adversarial vulnerability and the input dimension.Our work is consistent with a mean-field analysis that found that batch norm causes exploding gradients.

La batch normalization riduce l'adversarial robustness, così come la robustezza generale in molti casi, in particolare alle corruzioni da rumore.


This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nondeter- ministic domains.We are currently implementing the ideas expressed in this paper.


Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve.However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task.We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information.  In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks.  On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.

Un semplice meta-learner basato su RNN che raggiunge prestazioni SOTA su benchmark popolari