Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance.We show that a network can learn complicated sequences with a reward-modulated Hebbian learning rule if the network of reservoir neurons is combined with a second network that serves as a dynamic working memory and provides a spatio-temporal backbone signal to the reservoir.In combination with the working memory, reward-modulated Hebbian learning of the readout neurons performs as well as FORCE learning, but with the advantage of a biologically plausible interpretation of both the learning rule and the learning paradigm.

Mostriamo che un input di working memory a una reservoir network fa sì che una regola di Hebbian modulata a reward locale funzioni altrettanto bene di quella dei minimi quadrati ricorsivi (aka FORCE)


Convolutional architectures have recently been shown to be competitive on manysequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs) while providing computational and modelling advantages due to inherent parallelism.However, currently, there remains a performancegap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables.In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces.In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales.The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers.We show that the proposed architecture achieves state of the art log-likelihoods across several tasks.Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modelling of handwritten text.

Combiniamo i vantaggi computazionali delle architetture di convoluzione temporale con l'espressività delle variabili latenti stocastiche.


The weak contraction mapping is a self mapping that the range is always a subset of the domain, which admits a unique fixed-point.The iteration of weak contraction mapping is a Cauchy sequence that yields the unique fixed-point.A gradient-free optimization method as an application of weak contraction mapping is proposed to achieve global minimum convergence.The optimization method is robust to local minima and initial point position.

Un metodo senza gradiente è proposto per un problema di ottimizzazione non convesso


Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.Model-based control algorithms, such as model-predictive control (MPC) and trajectory optimization, peer into the gradients of underlying system dynamics in order to solve control tasks with high sample efficiency.  However, like all gradient-based numerical optimization methods,model-based control methods are sensitive to intializations and are prone to becoming trapped in local minima.Deep reinforcement learning (DRL), on the other hand, can somewhat alleviate these issues by exploring the solution space through sampling — at the expense of computational cost.In this paper, we present a hybrid method that combines the best aspects of gradient-based methods and DRL.We base our algorithm on the deep deterministic policy gradients (DDPG) algorithm and propose a simple modification that uses true gradients from a differentiable physical simulator to increase the convergence rate of both the actor and the critic.  We demonstrate our algorithm on seven 2D robot control tasks, with the most complex one being a differentiable half cheetah with hard contact constraints.Empirical results show that our method boosts the performance of DDPGwithout sacrificing its robustness to local minima.

Proponiamo un nuovo metodo che sfrutta i gradienti dei simulatori differenziabili per migliorare le prestazioni della RL per il controllo della robotica


We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network).We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling.In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t).  In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.

Proponiamo hypernetwork bayesiane: un framework per l'inferenza bayesiana approssimativa nelle reti neurali.


Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels.This paper presents a method called Deep Innovation Protection (DIP) that allows training  complex world models end-to-end for such 3D environments.The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in a world model, allowing other components to adapt.We investigate the emergent representations of these evolved networks, which learn a model of the world without the need for a specific forward-prediction loss.

Deep Innovation Protection permette l'evoluzione di world model complessi end-to-end per task 3D.


Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly.In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses.Recent work shows that randomized smoothing can be used to provide certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER).The attack-free characteristic makes MACER faster to train and easier to optimize.In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN.For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.

Proponiamo MACER: un algoritmo di difesa dimostrabile che allena modelli robusti massimizzando il raggio certificato. Non utilizza l'adversarial training ma si comporta meglio di tutte le difese dimostrabili l2 esistenti.


Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic.However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter.In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC).GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning.Our main theoretical contributions are two folds.First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic.Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored.Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.

Questo articolo propone un nuovo metodo actor-critic che usa le Hessiane di un critic per aggiornare un actor.


Deep Infomax~(DIM) is an unsupervised representation learning framework by maximizing the mutual information between the inputs and the outputs of an encoder, while probabilistic constraints are imposed on the outputs.In this paper, we propose Supervised Deep InfoMax~(SDIM), which introduces supervised probabilistic constraints to the encoder outputs.The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated.Unlike other works building generative classifiers with conditional generative models, SDIMs scale on complex datasets, and can achieve comparable performance with discriminative counterparts.  With SDIM, we could perform \emph{classification with rejection}.Instead of always reporting a class label, SDIM only makes predictions when test samples' largest logits surpass some pre-chosen thresholds, otherwise they will be deemed as out of the data distributions, and be rejected.  Our experiments show that SDIM with rejection policy can effectively reject illegal inputs including out-of-distribution samples and adversarial examples.

classificatori generativi in scala su dataset complessi, e valutare la loro efficacia per rifiutare input illegali, compresi i sample fuori distribuzione e gli adversarial example.


Abstract In this work, we describe a set of rules for the design and initialization of well-conditioned neural networks, guided by the goal of naturally balancing the diagonal blocks of the Hessian at the start of training.We show how our measure of conditioning of a block relates to another natural measure of conditioning, the ratio of weight gradients to the weights.We prove that for a ReLU-based deep multilayer perceptron, a simple initialization scheme using the geometric mean of the fan-in and fan-out satisfies our scaling rule.For more sophisticated architectures, we show how our scaling principle can be used to guide design choices to produce well-conditioned neural networks, reducing guess-work.

Una teoria per l'inizializzazione e lo scaling dei layer della rete neurale ReLU


We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model.Assuming that both the adversary and victim model fine-tune a large pretrained language model such as BERT (Devlin et al., 2019), we show that the adversary does not need any real training data to successfully mount the attack.In fact, the attacker need not even use grammatical or semantically meaningful queries: we show that random sequences of words coupled with task-specific heuristics form effective queries for model extraction on a diverse set of NLP tasks including natural language inference and question answering.Our work thus highlights an exploit only made feasible by the shift towards transfer learning methods within the NLP community: for a query budget of a few hundred dollars, an attacker can extract a model that performs only slightly worse than the victim model.Finally, we study two defense strategies against model extraction—membership classification and API watermarking—which while successful against some adversaries can also be circumvented by more clever ones.

Gli output delle moderne API NLP su testi senza senso forniscono forti segnali sulle strutture interne del modello, permettendo agli avversari di rubare le API.


We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction.RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE).Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses.Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance.Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error.We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction.Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes.This allows us to validate the benefits of our approach on a machine translation task.

Introduciamo SeaRNN, un nuovo algoritmo per il training RNN, ispirato all'approccio di learning to search per la structured prediction, al fine di evitare le limitazioni del training MLE.


Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems,including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policiesfor multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine \expert policies,as evaluated in the domain of simulated bipedal locomotion across different classes of terrain.We also introduce an input injection method for augmenting an existing policy network to exploit new input features.Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills.The combination of these methods allows a policy to be incrementally augmented with new skills.We compare our progressive learning and integration via distillation (PLAID) methodagainst three alternative baselines.

Un metodo di continual learning che usa la distillation per combinare expert policy e transfer learning per accelerare l'apprendimento di nuove competenze.


Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go.However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings.Viper, a recently proposed technique, constructs a decision tree policy by mimicking the DRL agent.Decision trees are interpretable as each action made can be traced back to the decision rule path that lead to it.However, one global decision tree approximating the DRL policy has significant limitations with respect to the geometry of decision boundaries.We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.We propose a training procedure to support non-differentiable decision tree experts and integrate it into imitation learning procedure of Viper.We evaluate our algorithm on four OpenAI gym environments, and show that the policy constructed in such a way is more performant and better mimics the DRL agent by lowering mispredictions and increasing the reward.We also show that MoET policies are amenable for verification using off-the-shelf automated theorem provers such as Z3.

Modello di reinforcement learning spiegabile usando una nuova combinazione di mistura di esperti con esperti di alberi decisionali non differenziabili.


We consider the problem of unconstrained minimization of a smooth objectivefunction in $\mathbb{R}^d$ in setting where only function evaluations are possible.We propose and analyze stochastic zeroth-order method with heavy ball momentum.In particular, we propose, SMTP, a momentum version of the stochastic three-point method (STP) Bergou et al. (2019).We show new complexity results for non-convex, convex and strongly convex functions.We test our method on a collection of learning to continuous control tasks on several MuJoCo Todorov et al. (2012) environments with varying difficulty and compare against STP, other state-of-the-art derivative-free optimization algorithms and against policy gradient methods.SMTP significantly outperforms STP and all other methods that we considered in our numerical experiments.Our second contribution is SMTP with importance sampling which we call SMTP_IS.We provide convergence analysis of this method for non-convex, convex and strongly convex objectives.

Sviluppiamo e analizziamo un nuovo algoritmo di ottimizzazione senza derivate con momentum e importance sampling con applicazioni al controllo continuo.


Using class labels to represent class similarity is a typical approach to training deep hashing systems for retrieval; samples from the same or different classes take binary 1 or 0 similarity values.This similarity does not model the full rich knowledge of semantic relations that may be present between data points.In this work we build upon the idea of using semantic hierarchies to form distance metrics between all available sample labels; for example cat to dog has a smaller distance than cat to guitar.We combine this type of semantic distance into a loss function to promote similar distances between the deep neural network embeddings.We also introduce an empirical Kullback-Leibler divergence loss term to promote binarization and uniformity of the embeddings.We test the resulting SHREWD method and demonstrate improvements in hierarchical retrieval scores using compact, binary hash codes instead of real valued ones, and show that in a weakly supervised hashing setting we are able to learn competitively without explicitly relying on class labels, but instead on similarities between labels.

Proponiamo un nuovo metodo per addestrare il deep hashing per image retrieval utilizzando solo una metrica di distanza relazionale tra i sample


In this paper, we propose a novel approach to improve a given surface mapping through local refinement.The approachreceives an established mapping between two surfaces and follows four phases:(i) inspection of the mapping and creation of a sparseset of landmarks in mismatching regions;(ii) segmentation with a low-distortion region-growing process based on flattening thesegmented parts;(iii) optimization of the deformation of segmented parts to align the landmarks in the planar parameterization domain;and(iv) aggregation of the mappings from segments to update the surface mapping.In addition, we propose a new method to deform themesh in order to meet constraints (in our case, the landmark alignment of phase(iii)).We incrementally adjust the cotangent weights forthe constraints and apply the deformation in a fashion that guarantees that the deformed mesh will be free of flipped faces and will havelow conformal distortion.Our new deformation approach, Iterative Least Squares Conformal Mapping (ILSCM), outperforms otherlow-distortion deformation methods.The approach is general, and we tested it by improving the mappings from different existing surfacemapping methods.We also tested its effectiveness by editing the mappings for a variety of 3D objects.

Proponiamo un nuovo approccio per migliorare una data cross-surface mapping attraverso il raffinamento locale con un nuovo metodo iterativo per deformare la mesh al fine di soddisfare i vincoli dell'utente.


Understanding the groundbreaking performance of Deep Neural Networks is oneof the greatest challenges to the scientific community today.In this work, weintroduce an information theoretic viewpoint on the behavior of deep networksoptimization processes and their generalization abilities.By studying the InformationPlane, the plane of the mutual information between the input variable andthe desired label, for each hidden layer.Specifically, we show that the training ofthe network is characterized by a rapid increase in the mutual information (MI)between the layers and the target label, followed by a longer decrease in the MIbetween the layers and the input variable.Further, we explicitly show that thesetwo fundamental information-theoretic quantities correspond to the generalizationerror of the network, as a result of introducing a new generalization bound that isexponential in the representation compression.The analysis focuses on typicalpatterns of large-scale problems.For this purpose, we introduce a novel analyticbound on the mutual information between consecutive layers in the network.An important consequence of our analysis is a super-linear boost in training timewith the number of non-degenerate hidden layers, demonstrating the computationalbenefit of the hidden layers.

Introdurre un punto di vista dalla teoria dell'informazione sul comportamento dei processi di ottimizzazione delle deep network e le loro capacità di generalizzazione


We propose and study a method for learning interpretable representations for the task of regression.Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions.Differentiable features are trained via gradient descent, and the performance of features in a linear model is used to weight the rate of change among subcomponents of each representation.The search process maintains an archive of representations with accuracy-complexity trade-offs to assist in generalization and interpretation.We compare several stochastic optimization approaches within this framework.We benchmark these variants on 100 open-source regression problems in comparison to state-of-the-art machine learning approaches.Our main finding is that this approach produces the highest average test scores across problems while producing representations that are orders of magnitude smaller than the next best performing method (gradient boosting).We also report a negative result in which attempts to directly optimize the disentanglement of the representation result in more highly correlated features.

Rappresentare l'architettura della rete come un insieme di alberi sintattici e ottimizzare la loro struttura porta a modelli di regressione accurati e concisi.


Most distributed machine learning (ML) systems store a copy of the model parameters locally on each machine to minimize network communication.In practice, in order to reduce synchronization waiting time, these copies of the model are not necessarily updated in lock-step, and can become stale.Despite much development in large-scale ML, the effect of staleness on the learning efficiency is inconclusive, mainly because it is challenging to control or monitor the staleness in complex distributed environments.In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates.Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature.The empirical findings also inspire a new convergence analysis of SGD in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\sqrt{T}).

Studio empirico e teorico degli effetti della staleness in esecuzione non sincrona su algoritmi di apprendimento automatico.


System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs.It is a key step for model-based control, estimator design, and output prediction.This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable.The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters.We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches.We also use SISL to identify the dynamics of an aerobatic helicopter.By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.

Questo lavoro presenta un algoritmo scalabile per l'identificazione non lineare del sistema offline da osservazioni parziali.


Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models.Sign-based methods, such as signSGD (Bernstein et al., 2018), have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM.In this paper, we perform a general analysis of sign-based methods for non-convex optimization.Our analysis is built on intuitive bounds on success probabilities and does not rely on special noise distributions nor on the boundedness of the variance of stochastic gradients.Extending the theory to distributed setting within a parameter server framework, we assure exponentially fast variance reduction with respect to number of nodes, maintaining 1-bit compression in both directions and using small mini-batch sizes.We validate our theoretical findings experimentally.

Analisi generale dei metodi basati sui segni (per esempio signSGD) per l'ottimizzazione non convessa, costruita su limiti intuitivi delle probabilità di successo.


Off-policy learning, the task of evaluating and improving policies using historic data collected from a logging policy, is important because on-policy evaluation is usually expensive and has adverse impacts.One of the major challenge of off-policy learning is to derive counterfactual estimators that also has low variance and thus low generalization error. In this work, inspired by learning bounds for importance sampling problems, we present a new counterfactual learning principle for off-policy learning with bandit feedbacks.Our method regularizes the generalization error by minimizing the distribution divergence between the logging policy and the new policy, and removes the need for iterating through all training samples to compute sample variance regularization in prior work.With neural network policies, our end-to-end training algorithms using variational divergence minimization showed significant improvement over conventional baseline algorithms and is also consistent with our theoretical results.

Per l'apprendimento off-policy con bandit feedback, proponiamo un nuovo algoritmo di counterfactual learning regolarizzato dalla varianza, che ha sia basi teoriche che prestazioni empiriche superiori.


We outline new approaches to incorporate ideas from deep learning into wave-based least-squares imaging.The aim, and main contribution of this work, is the combination of handcrafted constraints with deep convolutional neural networks, as a way to harness their remarkable ease of generating natural images.The mathematical basis underlying our method is the expectation-maximization framework, where data are divided in batches and coupled to additional "latent" unknowns.These unknowns are pairs of elements from the original unknown space (but now coupled to a specific data batch) and network inputs.In this setting, the neural network controls the similarity between these additional parameters, acting as a "center" variable.The resulting problem amounts to a maximum-likelihood estimation of the network parameters when the augmented data model is marginalized over the latent variables.

Combiniamo hard constraint fatti a mano con un  debole deep constraint a priori per eseguire l'imaging sismico e raccogliere informazioni sulla distribuzione "posteriore" sfruttando la molteplicità nei dati.


When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas.  The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.  We present a unified framework, based on the relation-aware self-attention mechanism,to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.On the challenging Spider dataset this framework boosts the exact match accuracy to 53.7%, compared to 47.4% for the previous state-of-the-art model unaugmented with BERT embeddings.In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment.

Stato dell'arte nel parsing complesso text-to-SQL combinando il ragionamento relazionale hard e soft nella codifica di schema/question.


As our experience shows, humans can learn and deploy a myriad of different skills to tackle the situations they encounter daily.Neural networks, in contrast, have a fixed memory capacity that prevents them from learning more than a few sets of skills before starting to forget them. In this work, we make a step to bridge neural networks with human-like learning capabilities.For this, we propose a model with a growing and open-bounded memory capacity that can be accessed based on the model’s current demands.To test this system, we introduce a continual learning task based on language modelling where the model is exposed to multiple languages and domains in sequence, without providing any explicit signal on the type of input it is currently dealing with.The proposed system exhibits improved adaptation skills in that it can recover faster than comparable baselines after a switch in the input language or domain.

Introduciamo una configurazione di continual learning basata sulla language modeling in cui non viene dato alcun segnale esplicito di segmentazione del task e proponiamo un modello di rete neurale con memoria a lungo termine crescente per affrontarlo.


We propose to tackle a time series regression problem by computing temporal evolution of a probability density function to provide a probabilistic forecast.A Recurrent Neural Network (RNN) based model is employed to learn a nonlinear operator for temporal evolution of a probability density function.We use a softmax layer for a numerical discretization of a smooth probability density functions, which transforms a function approximation problem to a classification task.Explicit and implicit regularization strategies are introduced to impose a smoothness condition on the estimated probability distribution.A Monte Carlo procedure to compute the temporal evolution of the distribution for a multiple-step forecast is presented.The evaluation of the proposed algorithm on three synthetic and two real data sets shows advantage over the compared baselines.

Algoritmo proposto basato su RNN per stimare la distribuzione predittiva nelle previsioni a uno e più step nei problemi di predizione delle serie temporali


In cognitive systems, the role of a working memory is crucial for visual reasoning and decision making.Tremendous progress has been made in understanding the mechanisms of the human/animal working memory, as well as in formulating different frameworks of artificial neural networks.  In the case of humans, the visual working memory (VWM) task is a standard one in which the subjects are presented with a sequence of images, each of which needs to be identified as to whether it was already seen or not. Our work is a study of multiple ways to learn a working memory model using recurrent neural networks that learn to remember input images across timesteps.We train these neural networks to solve the working memory task by training them with a sequence of images in supervised and reinforcement learning settings.The supervised setting uses image sequences with their corresponding labels.The reinforcement learning setting is inspired by the popular view in neuroscience that the working memory in the prefrontal cortex is modulated by a dopaminergic mechanism.We consider the VWM task as an environment that rewards the agent when it remembers past information and penalizes it for forgetting.  We quantitatively estimate the performance of these models on sequences of images from a standard image dataset (CIFAR-100).Further, we evaluate their ability to remember and recall as they are increasingly trained over episodes.Based on our analysis, we establish that a gated recurrent neural network model with long short-term memory units trained using reinforcement learning is powerful and more efficient in temporally consolidating the input spatial information. This work is an initial analysis as a part of our ultimate goal to use artificial neural networks to model the behavior and information processing of the working memory of the brain and to use brain imaging data captured from human subjects during the VWM cognitive task to understand various memory mechanisms of the brain.

Le LSTM possono modellare più efficacemente la working memory se sono apprese utilizzando reinforcement learning, in molto simile al sistema di dopamina che modula la memoria nella corteccia prefrontale


Nonlinearity is crucial to the performance of a deep (neural) network (DN).To date there has been little progress understanding the menagerie of available  nonlinearities, but recently progress has been made on understanding the r\^{o}le played by piecewise affine and convex nonlinearities like the ReLU and absolute value activation functions and max-pooling.In particular, DN layers constructed from these operations can be interpreted as {\em max-affine spline operators} (MASOs) that have an elegant link to vector quantization (VQ) and $K$-means.While this is good theoretical progress, the entire MASO approach is predicated on the requirement that the nonlinearities be piecewise affine and convex, which precludes important activation functions like the sigmoid, hyperbolic tangent, and softmax.{\em This paper extends the MASO framework to these and an infinitely large class of new nonlinearities by linking deterministic MASOs with probabilistic Gaussian Mixture Models (GMMs).}We show that, under a GMM, piecewise affine, convex nonlinearities like ReLU, absolute value, and max-pooling can be interpreted as solutions to certain natural ``hard'' VQ inference problems, while sigmoid, hyperbolic tangent, and softmax can be interpreted as solutions to corresponding ``soft'' VQ inference problems.We further extend the framework by hybridizing the hard and soft VQ optimizations to create a $\beta$-VQ inference that interpolates between hard, soft, and linear VQ inference.A prime example of a $\beta$-VQ DN nonlinearity is the {\em swish} nonlinearity, which offers state-of-the-art performance in a range of computer vision tasks but was developed ad hoc by experimentation.Finally, we validate with experiments an important assertion of our theory, namely that DN performance can be significantly improved by enforcing orthogonality in its linear filters.

Riformulare le nonlinearità delle deep network da un ambito di quantizzazione vettoriale e collegare la maggior parte delle nonlinearità conosciute.


Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice.A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, and the task of finding a viable design is often referred to as the inverse protein folding problem.We develop generative models for protein sequences conditioned on a graph-structured specification of the design target.Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space.Our framework significantly improves upon prior parametric models of protein sequences given structure, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

Impariamo a generare condizionatamente sequenze di proteine date strutture con un modello che cattura le dipendenze sparse e a lungo raggio.


We provide a novel perspective on the forward pass through a block of layers in a deep network.In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex objective with a single iteration of a $\tau$-nice Proximal Stochastic Gradient method.We further show that replacing standard Bernoulli dropout with additive dropout is equivalent to optimizing the same convex objective with a variance-reduced proximal method.By expressing both fully-connected and convolutional layers as special cases of a high-order tensor product, we unify the underlying convex optimization problem in the tensor setting and derive a formula for the Lipschitz constant $L$ used to determine the optimal step size of the above proximal methods.We conduct experiments with standard convolutional networks applied to the CIFAR-10 and CIFAR-100 datasets and show that replacing a block of layers with multiple iterations of the corresponding solver, with step size set via $L$, consistently improves classification accuracy.

Un framework che collega i layer più profondi della rete agli algoritmi di ottimizzazione stocastica; può essere usato per migliorare l'accuratezza del modello e informare la progettazione della rete.


Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases.Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy.Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured.Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters.This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.

Un metodo per imparare la configurazione di quantizzazione per le reti a bassa precisione che raggiunge lo stato dell'arte delle prestazioni per le reti quantizzate.


There have been several studies recently showing that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models which fail to generalize to out-of-domain datasets, and are likely to perform poorly in real-world scenarios.We propose several learning strategies to train neural models which are more robust to such biases and transfer better to out-of-domain datasets.We introduce an additional lightweight bias-only model which learns dataset biases and uses its prediction to adjust the loss of the base model to reduce the biases.In other words, our methods down-weight the importance of the biased examples, and focus training on hard examples, i.e. examples that cannot be correctly classified by only relying on biases.Our approaches are model agnostic and simple to implement.  We experiment on large-scale natural language inference and fact verification datasets and their out-of-domain datasets and show that our debiased models significantly improve the robustness in all settings, including gaining 9.76 points on the FEVER symmetric evaluation dataset, 5.45 on the HANS dataset and 4.78 points on the SNLI hard set.  These datasets are specifically designed to assess the robustness of models in the out-of-domain setting where typical biases in the training data do not exist in the evaluation set.

Proponiamo diverse strategie generali di debiasing per affrontare i bias comuni visti in diversi dataset e ottenere un sostanziale miglioramento delle prestazioni fuori dal dominio in tutti i setting.


Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem.It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT.Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low.This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction.The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image.The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction.We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset.

Presentiamo un algoritmo di ricostruzione basato sull'inferenza CNN per affrontare CT con pochissime view.


In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date.The most popular sequence to sequence models typically “generate and hope” generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context.Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear  grounding.To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia.  We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses.Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.

Costruiamo agenti di conversazione consapevoli condizionando su Wikipedia + un nuovo task supervised.


We formulate a new problem at the intersection of semi-supervised learning and contextual bandits, motivated by several applications including clinical trials and dialog systems.We demonstrate how contextual bandit and graph convolutional networks can be adjusted to the new problem formulation.We then take the best of both approaches to develop multi-GCN embedded contextual bandit.Our algorithms are verified on several real world datasets.

Sintesi degli algoritmi GCN e LINUCB per l'apprendimento online con feedback mancanti


A collection of scientific papers is often accompanied by tags:  keywords, topics, concepts etc., associated with each paper.  Sometimes these tags are human-generated, sometimes they are  machine-generated.  We propose a simple measure of the consistency  of the tagging of scientific papers: whether these tags are  predictive for the citation graph links.  Since the authors tend to  cite papers about the topics close to those of their publications, a  consistent tagging system could predict citations.  We present an  algorithm to calculate consistency, and experiments with human- and  machine-generated tags.  We show that augmentation, i.e. the combination  of the manual tags with the machine-generated ones, can enhance the  consistency of the tags.  We further introduce cross-consistency,  the ability to predict citation links between papers tagged by  different taggers, e.g. manually and by a machine.  Cross-consistency can be used to evaluate the tagging quality when  the amount of labeled data is limited.

Un buon tagger dà tag simili a un dato articolo e agli articoli che cita


Recent research has intensively revealed the vulnerability of deep neural networks, especially for convolutional neural networks (CNNs) on the task of image recognition, through creating adversarial samples which `"slightly" differ from legitimate samples.This vulnerability indicates that these powerful models are sensitive to specific perturbations and cannot filter out these adversarial perturbations.In this work, we propose a quantization-based method which enables a CNN to filter out adversarial perturbations effectively.Notably, different from prior work on input quantization, we apply the quantization in the intermediate layers of a CNN.Our approach is naturally aligned with the clustering of the coarse-grained semantic information learned by a CNN.Furthermore, to compensate for the loss of information which is inevitably caused by the quantization, we propose the multi-head quantization, where we project data points to different sub-spaces and perform quantization within each sub-space.We enclose our design in a quantization layer named as the Q-Layer.The results obtained on MNIST and Fashion-MNSIT datasets demonstrate that only adding one Q-Layer into a CNN could significantly improve its robustness against both white-box and black-box attacks.

Proponiamo un metodo basato sulla quantizzazione che regolarizza le rappresentazioni apprese da una CNN per essere automaticamente allineate con la matrice concettuale allenabile, filtrando quindi efficacemente le perturbazioni adversarial.


Invariant and equivariant networks have been successfully used for learning images, sets, point clouds, and graphs.A basic challenge in developing such networks is finding the maximal collection of invariant and equivariant \emph{linear} layers.Although this question is answered for the first three examples (for popular transformations, at-least), a full characterization of invariant and equivariant linear layers for graphs is not known. In this paper we provide a characterization of all permutation invariant and equivariant linear layers for (hyper-)graph data, and show that their dimension, in case of edge-value graph data, is $2$ and $15$, respectively.More generally, for graph data defined on $k$-tuples of nodes, the dimension is the $k$-th and $2k$-th Bell numbers.Orthogonal bases for the layers are computed, including generalization to multi-graph data.The constant number of basis elements and their characteristics allow successfully applying the networks to different size graphs.From the theoretical point of view, our results generalize and unify recent advancement in equivariant deep learning.In particular, we show that our model is capable of approximating any message passing neural network.Applying these new linear layers in a simple deep neural network framework is shown to achieve comparable results to state-of-the-art and to have better expressivity than previous invariant and equivariant bases.

L'articolo fornisce una caratterizzazione completa dei layer lineari invarianti o equivarianti per la permutazione per i dati del grafo.


In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images).For this reason, previous works have considered partial models, which model only part of the observation.In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning.To address this, we introduce a general family of partial models that are provably causally correct, but avoid the need to fully model future observations.

I modelli parziali causalmente corretti non devono generare l'intera osservazione per rimanere causalmente corretti in ambienti stocastici.


In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task.In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost.Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation.Second, we introduce a new metric measuring how quickly a learner acquires a new skill.Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods.Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration.Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency

Un efficiente algoritmo di lifelong learning che fornisce un migliore compromesso tra accuratezza e complessità di tempo/memoria rispetto ad altri algoritmi.


Reduced precision computation is one of the key areas addressing the widening’compute gap’, driven by an exponential growth in deep learning applications.In recent years, deep neural network training has largely migrated to 16-bit precision,with significant gains in performance and energy efficiency.However, attempts to train DNNs at 8-bit precision have met with significant challenges, because of the higher precision and dynamic range requirements of back-propagation.   In this paper,  we  propose  a  method  to  train  deep  neural  networks  using  8-bit  floating point representation for weights, activations, errors, and gradients.  We demonstrate state-of-the-art accuracy across multiple data sets (imagenet-1K, WMT16)and a broader set of workloads (Resnet-18/34/50, GNMT, and Transformer) than previously reported.   We propose an enhanced loss scaling method to augment the reduced subnormal range of 8-bit floating point, to improve error propagation.We also examine the impact of quantization noise on generalization, and propose a stochastic rounding technique to address gradient noise.As a result of applying all these techniques,  we report slightly higher validation accuracy compared to full precision baseline.

Abbiamo dimostrato risultati di training allo stato dell'arte utilizzando la rappresentazione in virgola mobile a 8 bit, attraverso Resnet, GNMT, Transformer.


Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed.Some supervise the learning process by pairwise or tripletwise similarity constraints while others take the advantage of structured similarity information among multiple data points.In this work, we approach deep metric learning from a novel perspective.We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one.ICE has three main appealing properties.Firstly, similar to categorical cross entropy (CCE), ICE has clear probabilistic interpretation and exploits structured semantic similarity information for learning supervision.Secondly, ICE is scalable to infinite training data as it learns on mini-batches iteratively and is independent of the training set size.Thirdly, motivated by our relative weight analysis, seamless sample reweighting is incorporated.It rescales samples’ gradients to control the differentiation degree over training examples instead of truncating them by sample mining.In addition to its simplicity and intuitiveness, extensive experiments on three real-world benchmarks demonstrate the superiority of ICE.

Proponiamo la cross entropy di istanza (ICE) che misura la differenza tra una matching distribution stimata a livello di istanza e la sua distribuzione ground-truth.


In model-based reinforcement learning, the agent interleaves between model learning and planning.  These two components are  inextricably intertwined.If the model is not able to provide sensible long-term prediction, the executed planer would exploit model flaws, which can yield catastrophic failures.This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration.To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference.We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions.Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid.An exploration strategy can be devised by searching for unlikely trajectories under the model.Our methods achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

L'inclusione, nel modello, di variabili latenti che codificano il contenuto futuro migliora l'accuratezza della predizione a lungo termine, che è fondamentale per una migliore pianificazione nel RL basato sul modello.


Detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems.Although there have been several studies in detecting anomalies based on rule-based or machine learning-based approaches for satellite systems, a tensor-based decomposition method has not been extensively explored for anomaly detection.In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system.Because of the high risk and cost, detecting anomalies in a satellite system is crucial.We construct 3rd-order tensors with telemetry data collected from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies.Our result shows that our tensor-based approach can be effective in achieving higher accuracy and reducing false positives in detecting anomalies as compared to other existing approaches.

Struttura di rilevamento delle anomalie basata su tensori integrativi per un sistema satellitare.


Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time.In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.We start from the KL regularized expected reward objective which introduces an additional component, a default policy.Instead of relying on a fixed default policy, we learn it from data.But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster.We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm.We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning.Please watch the video demonstrating learned experts and default policies on several continuous control tasks ( https://youtu.be/U2qA3llzus8 ).

Limitare le informazioni di stato per la policy predefinita può migliorare le prestazioni, in un framework RL regolarizzato da KL in cui sia l'agente che la policy predefinita sono ottimizzati insieme


When an image classifier makes a prediction, which parts of the image are relevant and why?We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?Producing an answer requires marginalizing over images that could have been seen but weren't.We can sample plausible image in-fills by conditioning a generative model on the rest of the image.We then optimize to find the image regions that most change the classifier's decision after in-fill.Our approach contrasts with ad-hoc in-filling approaches, such as blurring or injecting noise, which generate inputs far from the data distribution, and ignore informative relationships between different parts of the image.Our method produces more compact and relevant saliency maps, with fewer artifacts compared to previous methods.

Calcoliamo la saliency utilizzando un modello generativo forte per marginalizzare in modo efficiente su input alternativi plausibili, rivelando aree di pixel concentrate che conservano le informazioni della label.


This paper presents the Variation Network (VarNet), a  generative model providing means to manipulate the high-level attributes of a given input.The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself.  These two settings can be easily combined  which makes VarNet applicable for a wide variety of tasks.Further, VarNet has a sound probabilistic interpretation which grants us with  a novel way to navigate in the latent spaces as well as means to control how the  attributes are learned.We demonstrate  experimentally that this model is capable of performing interesting input manipulation  and that the learned attributes are relevant and interpretable.

Il Variation Network è un modello generativo in grado di apprendere attributi di alto livello senza supervisione che possono poi essere utilizzati per la manipolazione controllata degli input.


Despite alarm over the reliance of machine learning systems on so-called spurious patterns in training data, the term lacks coherent meaning in standard statistical frameworks.However, the language of causality offers clarity: spurious associations are those due to a common cause (confounding) vs direct or indirect effects.In this paper, we focus on NLP, introducing methods and resources for training models insensitive to spurious patterns.Given documents and their initial labels, we task humans with revise each document to accord with a counterfactual target label, asking that the revised documents be internally coherent while avoiding any gratuitous changes.Interestingly, on sentiment analysis and natural language inference tasks, classifiers trained on original data fail on their counterfactually-revised counterparts and vice versa.Classifiers trained on combined datasets perform remarkably well, just shy of those specialized to either domain.While classifiers trained on either original or manipulated data alone  are sensitive to spurious features (e.g., mentions of genre), models trained on the combined data are insensitive to this signal.We will publicly release both datasets.

Gli human in the loop rivedono i documenti per accordarsi sulle label controfattuali, la risorsa risultante aiuta a ridurre la dipendenza dalle associazioni spurie.


Among multiple ways of interpreting a machine learning model, measuring the importance of a set of features tied to a prediction is probably one of the most intuitive way to explain a model.In this paper, we establish the link between a set of features to a prediction with a new evaluation criteria, robustness analysis, which measures the minimum tolerance of adversarial perturbation.By measuring the tolerance level for an adversarial attack, we can extract a set of features that provides most robust support for a current prediction, and also can extract a set of features that contrasts the current prediction to a target class by setting a targeted adversarial attack.By applying this methodology to various prediction tasks across multiple domains, we observed the derived explanations are indeed capturing the significant feature set qualitatively and quantitatively.

Proponiamo una nuova misura oggettiva per valutare le spiegazioni basata sulla nozione di adversarial robustness. Il criterio di valutazione ci permette inoltre di derivare nuove spiegazioni che catturano le feature pertinenti qualitativamente e quantitativamente.


Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.However, typical GANs require fully-observed data during training.In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data.The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution.We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer.We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption.

Questo articolo presenta un framework basato su GAN per imparare la distribuzione da dati incompleti ad alta dimensionalità.


Due to a resource-constrained environment, network compression has become an important part of deep neural networks research.In this paper, we propose a new compression method, Inter-Layer Weight Prediction (ILWP) and quantization method which quantize the predicted residuals between the weights in all convolution layers based on an inter-frame prediction method in conventional video coding schemes.Furthermore, we found a phenomenon Smoothly Varying Weight Hypothesis (SVWH) which is that the weights in adjacent convolution layers share strong similarity in shapes and values, i.e., the weights tend to vary smoothly along with the layers.Based on SVWH, we propose a second ILWP and quantization method which quantize the predicted residuals between the weights in adjacent convolution layers.Since the predicted weight residuals tend to follow Laplace distributions with very low variance, the weight quantization can more effectively be applied, thus producing more zero weights and enhancing the weight compression ratio.In addition, we propose a new inter-layer loss for eliminating non-texture bits, which enabled us to more effectively store only texture bits.That is, the proposed loss regularizes the weights such that the collocated weights between the adjacent two layers have the same values.Finally, we propose an ILWP with an inter-layer loss and quantization method.Our comprehensive experiments show that the proposed method achieves a much higher weight compression rate at the same accuracy level compared with the previous quantization-based compression methods in deep neural networks.

Proponiamo un nuovo metodo di compressione, Inter-Layer Weight Prediction (ILWP) e un metodo di quantizzazione che quantizza i residui predetti tra i pesi nei layer di convoluzione.


There exists a plethora of techniques for inducing structured sparsity in parametric models during the optimization process, with the final goal of resource-efficient inference.However, to the best of our knowledge, none target a specific number of floating-point operations (FLOPs) as part of a single end-to-end optimization objective, despite reporting FLOPs as part of the results.Furthermore, a one-size-fits-all approach ignores realistic system constraints, which differ significantly between, say, a GPU and a mobile phone -- FLOPs on the former incur less latency than on the latter; thus, it is important for practitioners to be able to specify a target number of FLOPs during model compression.In this work, we extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective and show that, given a desired FLOPs requirement, different neural networks can be successfully trained for image classification.

Estendiamo una tecnica allo stato dell'arte per incorporare direttamente le FLOP come parte dell'obiettivo di ottimizzazione, e mostriamo che, dato un requisito FLOP desiderato, diverse reti neurali sono addestrate con successo.


Unpaired image-to-image translation among category domains has achieved remarkable success in past decades.Recent studies mainly focus on two challenges.For one thing, such translation is inherently multimodal due to variations of domain-specific information (e.g., the domain of house cat has multiple fine-grained subcategories).For another, existing multimodal approaches have limitations in handling more than two domains, i.e. they have to independently build one model for every pair of domains.To address these problems, we propose the Hierarchical Image-to-image Translation (HIT) method which jointly formulates the multimodal and multi-domain problem in a semantic hierarchy structure, and can further control the uncertainty of multimodal.Specifically, we regard the domain-specific variations as the result of the multi-granularity property of domains, and one can control the granularity of the multimodal translation by dividing a domain with large variations into multiple subdomains which capture local and fine-grained variations.With the assumption of Gaussian prior, variations of domains are modeled in a common space such that translations can further be done among multiple domains within one model.To learn such complicated space, we propose to leverage the inclusion relation among domains to constrain distributions of parent and children to be nested.Experiments on several datasets validate the promising results and competitive performance against state-of-the-arts.

Metodo di traduzione image-to-image multidominio e multimodale con controllo della granularità


Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data.However, this observed efficiency is not yet entirely explained by theory.It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency --- a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN.Such networks, however, are not very often applied to real life tasks.In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency.Our theoretical results are verified by a series of extensive computational experiments.

Analisi dell'espressività e della generalità delle reti neurali ricorrenti con nonlinearità ReLu utilizzando la decomposizione Tensor-Train.


While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood.In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image.In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step.We demonstrate our results on MNIST with convolutional neural networks and on ImageNet with Inception-v3 and ResNet-101.

Proponiamo un nuovo ed efficiente algoritmo per costruire adversarial example per mezzo di deformazioni, piuttosto che perturbazioni additive.


Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable.Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients.In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck.By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms.Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running.We show that our method can learn such skills directly from raw video demonstrations, substantially outperforming prior adversarial imitation learning methods.The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings.Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods.

Regolarizzazione dell'adversarial learning con un information bottleneck, applicato all'imitation learning, al reinforcement learning inverso e alle generative adversarial network.


Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions.While architectural advances have led to improved accuracy, building robust models remains challenging, involving major changes in training procedure and datasets.  Prior work has argued that there is an inherent trade-off between robustness and accuracy, as exemplified by standard data augmentation techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy.We introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image.  Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNet Common Corruptions benchmarks while also maintaining accuracy on clean data.We find that this augmentation leads to reduced sensitivity to high frequency noise (similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout).We show it can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment.  Finally, we find that the idea of restricting perturbations to patches can also be useful in the context of adversarial learning, yielding models without the loss in accuracy that is found with unconstrained adversarial training.

Il semplice metodo di augmentation supera il trade-off robustezza/precisione osservato in letteratura e apre domande sull'effetto della distribuzione di training sulla generalizzazione fuori dalla distribuzione.


Offset regression is a standard method for spatial localization in many vision tasks, including human pose estimation, object detection, and instance segmentation.However, if high localization accuracy is crucial for a task, convolutional neural networks will offset regressionusually struggle to deliver.  This can be attributed to the locality of the convolution operation, exacerbated by variance in scale, clutter, and viewpoint.An even more fundamental issue is the multi-modality of real-world images.As a consequence, they cannot be approximated adequately using a single mode model.  Instead, we propose to use mixture density networks (MDN) for offset regression, allowing the model to manage various modes efficiently and learning to predict full conditional density of the outputs given the input.On 2D human pose estimation in the wild, which requires accurate localisation of body keypoints, we show that this yields significant improvement in localization accuracy.In particular, our experiments reveal viewpoint variation as  the dominant  multi-modal factor.Further, by carefully initializing MDN parameters, we do not face any instabilities in training, which is known to be a big obstacle for widespread deployment of MDN.The method can be readily applied to any task with a spatial regression component.Our findings  highlight the multi-modal nature of real-world vision, and the significance of explicitly accounting for viewpoint variation, at least when spatial localization is concerned.

Usiamo reti di densità mista per fare una stima di densità condizionale completa per la regressione di offset spaziale e la applichiamo al task di human pose estimation.


Like language, music can be represented as a sequence of discrete symbols that form a hierarchical syntax, with notes being roughly like characters and motifs of notes like words.  Unlike text however, music relies heavily on repetition on multiple timescales to build structure and meaning.The Music Transformer has shown compelling results in generating music with structure (Huang et al., 2018).  In this paper, we introduce a tool for visualizing self-attention on polyphonic music with an interactive pianoroll.  We use music transformer as both a descriptive tool and a generative model.  For the former, we use it to analyze existing music to see if the resulting self-attention structure corroborates with the musical structure known from music theory.  For the latter, we inspect the model's self-attention during generation, in order to understand how past notes affect future ones.We also compare and contrast the attention structure of regular attention to that of relative attention (Shaw et al., 2018, Huang et al., 2018), and examine its impact on the resulting generated music.  For example, for the JSB Chorales dataset, a model trained with relative attention is more consistent in attending to all the voices in the preceding timestep and the chords before, and at cadences to the beginning of a phrase, allowing it to create an arc.  We hope that our analyses will offer more evidence for relative self-attention as a powerful inductive bias for modeling music.  We invite the reader to explore our video animations of music attention and to interact with the visualizations at https://storage.googleapis.com/nips-workshop-visualization/index.html.

Visualizzazione delle differenze tra attention regolare e relativa per Music Transformer.


We study the statistical properties of the endpoint of stochastic gradient descent (SGD).We approximate SGD as a stochastic differential equation (SDE) and consider its Boltzmann Gibbs equilibrium distribution under the assumption of isotropic variance in loss gradients..Through this analysis, we find that three factors – learning rate, batch size and the variance of the loss gradients – control the trade-off between the depth and width of the minima found by SGD, with wider minima favoured by a higher ratio of learning rate to batch size. In the equilibrium distribution only the ratio of learning rate to batch size appears, implying that it’s invariant under a simultaneous rescaling of each by the same amount. We experimentally show how learning rate and batch size affect SGD from two perspectives: the endpoint of SGD and the dynamics that lead up to it. For the endpoint, the experiments suggest the endpoint of SGD is similar under simultaneous rescaling of batch size and learning rate, and also that a higher ratio leads to flatter minima, both findings are consistent with our theoretical analysis. We note experimentally that the dynamics also seem to be similar under the same rescaling of learning rate and batch size, which we explore showing that one can exchange batch size and learning rate in a cyclical learning rate schedule. Next, we illustrate how noise affects memorization, showing that high noise levels lead to better generalization. Finally, we find experimentally that the similarity under simultaneous rescaling of learning rate and batch size breaks down if the learning rate gets too large or the batch size gets too small.

Tre fattori (batch size, learning rate, rumore del gradiente) cambiano in modo prevedibile le proprietà (per esempio la nitidezza) dei minimi trovati da SGD.


Although word analogy problems have become a standard tool for evaluating word vectors, little is known about why word vectors are so good at solving these problems.In this paper, I attempt to further our understanding of the subject, by developing a simple, but highly accurate generative approach to solve the word analogy problem for the case when all terms involved in the problem are nouns.My results demonstrate the ambiguities associated with learning the relationship between a word pair, and the role of the training dataset in determining the relationship which gets most highlighted.Furthermore, my results show that the ability of a model to accurately solve the word analogy problem may not be indicative of a model’s ability to learn the relationship between a word pair the way a human does.

Semplice approccio generativo per risolvere il problema dell'analogia delle parole che produce intuizioni sulle relazioni tra le parole e i problemi con la loro stima


Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks.Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyper-parameters and keeping them fixed to values normally used for training from scratch.This paper re-examines several common practices of setting hyper-parameters for fine-tuning.Our findings are based on extensive empirical evaluation for fine-tuning on various transfer learning benchmarks.(1) While prior works have thoroughly investigated learning rate and batch size, momentum for fine-tuning is a relatively unexplored parameter.We find that picking the right value for momentum is critical for fine-tuning performance and connect it with previous theoretical findings.(2) Optimal hyper-parameters for fine-tuning in particular the effective learning rate are not only dataset dependent but also sensitive to the similarity between the source domain and target domain.This is in contrast to hyper-parameters for training from scratch.(3) Reference-based regularization that keeps models close to the initial model does not necessarily apply for "dissimilar" datasets.Our findings challenge common practices of fine- tuning and encourages deep learning practitioners to rethink the hyper-parameters for fine-tuning.

Questo articolo riesamina diverse pratiche comuni dei setting degli iper-parametri per fine-tuning.


Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task.To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied.In this paper, we propose a novel regularized transfer learning framework DELTA, namely DEep Learning Transfer using Feature Map with Attention.Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network.Specifically, in addition to minimizing the empirical loss, DELTA intends to align the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in an supervised learning manner.We evaluate DELTA with the state-of-the-art algorithms, including L2 and L2-SP.The experiment results show that our proposed method outperforms these baselines with higher accuracy for new tasks.

Migliorare il deep transfer learning con regolarizzazione usando feature map basate sull'attention


Neural models achieved considerable improvement for many natural language processing tasks, but they offer little transparency, and interpretability comes at a cost.In some domains, automated predictions without justifications have limited applicability.Recently, progress has been made regarding single-aspect sentiment analysis for reviews, where the ambiguity of a justification is minimal.In this context, a justification, or mask, consists of (long) word sequences from the input text, which suffice to make the prediction.Existing models cannot handle more than one aspect in one training and induce binary masks that might be ambiguous.In our work, we propose a neural model for predicting multi-aspect sentiments for reviews and generates a probabilistic multi-dimensional mask (one per aspect) simultaneously, in an unsupervised and multi-task learning manner.Our evaluation shows that on three datasets, in the beer and hotel domain, our model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable.

Modello neurale che predice sentimenti multiaspetto e genera simultaneamente una maschera probabilistica multidimensionale. Il modello supera le forti baseline e genera maschere che sono forti predittrici di feature e al tempo stesso significative e interpretabili.


Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL).However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency.We point out that it is difficult to resolve all of the shortcomings simultaneously because of a tradeoff between ML and RL.In order to counteract these problems, we propose an objective function for sequence generation using α-divergence, which leads to an ML-RL integrated method that exploits better parts of ML and RL.We demonstrate that the proposed objective function generalizes ML and RL objective functions because it includes both as its special cases (ML corresponds to α → 0 and RL to α → 1).We provide a proposition stating that the difference between the RL objective function and the proposed one monotonically decreases with increasing α.Experimental results on machine translation tasks show that minimizing the proposed objective function achieves better sequence generation performance than ML-based methods.

Proporre una nuova funzione obiettivo per la generazione di sequenze neurali che integra le funzioni obiettivo basate su ML e RL.


Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB.Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable.Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points.In doing so we introduce \textit{Siamese Capsule Networks}, a new variant that can be used for pairwise learning tasks.We find that the model improves over baselines in the few-shot learning setting, suggesting that capsule networks are efficient at learning discriminative representations when given few samples.  We find that \textit{Siamese Capsule Networks} perform well against strong baselines on both pairwise learning datasets when trained using a contrastive loss with $\ell_2$-normalized capsule encoded pose features, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

Una capsule network appresa in modo pairwise che si comporta bene in task di face verification nel setting con un numero limitato di dati annotati


In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al., 2016) and Categorical DQN (Bellemare et al., 2017), while giving better run-time performance than A3C (Mnih et al., 2016).Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting.The same approach can be used to convert several classes of multi-step policy evaluation algorithms designed for expected value evaluation into distributional ones.Next, we introduce the β-leaveone-out policy gradient algorithm which improves the trade-off between variance and bias by using action values as a baseline.Our final algorithmic contribution is a new prioritized replay algorithm for sequences, which exploits the temporal locality of neighboring observations for more efficient replay prioritization.Using the Atari 2600 benchmarks, we show that each of these innovations contribute to both the sample efficiency and final agent performance.Finally, we demonstrate that Reactor reaches state-of-the-art performance after 200 million frames and less than a day of training.

Reactor combina molteplici contributi algoritmici e architetturali per produrre un agente con una maggiore sample-efficiency rispetto al Prioritized Dueling DQN, dando al tempo stesso migliori prestazioni di run-time rispetto all'A3C.


Hierarchical planning, in particular, Hierarchical Task Networks, was proposed as a method to describe plans by decomposition of tasks to sub-tasks until primitive tasks, actions, are obtained.Plan verification assumes a complete plan as input, and the objective is finding a task that decomposes to this plan.In plan recognition, a prefix of the plan is given and the objective is finding a task that decomposes to the (shortest) plan with the given prefix.This paper describes how to verify and recognize plans using a common method known from formal grammars, by parsing.

L'articolo descrive i metodi per verificare e riconoscere i piani HTN attraverso il parsing delle grammatiche degli attributi.


Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones.However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup.In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension.From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English.We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines.In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset.

Esploriamo la neural architecture search per task linguistici. La recurrent cell search è impegnativa per la NMT, ma la ricerca del meccanismo di attention funziona. Il risultato della ricerca dell'attention sulla traduzione è trasferibile alla reading comprehension.


Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code.In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints.In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data.We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting.We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.

Proponiamo un regolarizzatore che migliora l'interpolazione e gli autoencoder e mostriamo che migliora anche la rappresentazione appresa per i downstream task.


We consider the problem of generating plausible and diverse video sequences, when we are only given a start and an end frame.This task is also known as inbetweening, and it belongs to the broader area of stochastic video generation, which is generally approached by means of recurrent neural networks (RNN).In this paper, we propose instead a fully convolutional model to generate video sequences directly in the pixel domain.We first obtain a latent video representation using a stochastic fusion mechanism that learns how to incorporate information from the start and end frames.Our model learns to produce such latent representation by progressively increasing the temporal resolution, and then decode in the spatiotemporal domain using 3D convolutions.The model is trained end-to-end by minimizing an adversarial loss.Experiments on several widely-used benchmark datasets show that it is able to generate meaningful and diverse in-between video sequences, according to both quantitative and qualitative evaluations.

Questo articolo presenta un metodo per la generazione stocastica di frame video intermedi a partire da key frame dati, utilizzando convoluzioni 3D dirette.


Aligning knowledge graphs from different sources or languages, which aims to align both the entity and relation, is critical to a variety of applications such as knowledge graph construction and question answering.Existing methods of knowledge graph alignment usually rely on a large number of aligned knowledge triplets to train effective models.However, these aligned triplets may not be available or are expensive to obtain for many domains.Therefore, in this paper we study how to design fully-unsupervised methods or weakly-supervised methods, i.e., to align knowledge graphs without or with only a few aligned triplets.We propose an unsupervised framework based on adversarial training, which is able to map the entities and relations in a source knowledge graph to those in a target knowledge graph.This framework can be further seamlessly integrated with existing supervised methods, where only a limited number of aligned triplets are utilized as guidance.Experiments on real-world datasets prove the effectiveness of our proposed approach in both the weakly-supervised and unsupervised settings.

Questo articolo studia l'allineamento weakly-supervised dei knowledge graph con framework di adversarial training.


Multi-view learning can provide self-supervision when different views are available of the same data.Distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora.Motivated by the asymmetry in the two hemispheres of the human brain as well as the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we present two multi-view frameworks for learning sentence representations in an unsupervised fashion.One framework uses a generative objective and the other a discriminative one.In both frameworks, the final representation is an ensemble of two views, in which, one view encodes the input sentence with a Recurrent Neural Network (RNN), and the other view encodes it with a simple linear model.We show that, after learning, the vectors produced by our multi-view frameworks provide improved representations over their single-view learnt counterparts, and the combination of different views gives representational improvement over each view and demonstrates solid transferability on standard downstream tasks.

L'apprendimento multi view migliora l'apprendimento unsupervised di sentence embedding


There are myriad kinds of segmentation, and ultimately the `"right" segmentation of a given scene is in the eye of the annotator.Standard approaches require large amounts of labeled data to learn just one particular kind of segmentation.As a first step towards relieving this annotation burden, we propose the problem of guided segmentation: given varying amounts of pixel-wise labels, segment unannotated pixels by propagating supervision locally (within an image) and non-locally (across images).We propose guided networks, which extract a latent task representation---guidance---from variable amounts and classes (categories, instances, etc.) of pixel supervision and optimize our architecture end-to-end for fast, accurate, and data-efficient segmentation by meta-learning.To span the few-shot and many-shot learning regimes, we examine guidance from as little as one pixel per concept to as much as 1000+ images, and compare to full gradient optimization at both extremes.To explore generalization, we analyze guidance as a bridge between different levels of supervision to segment classes as the union of instances.Our segmentor concentrates different amounts of supervision of different types of classes into an efficient latent representation, non-locally propagates this supervision across images, and can be updated quickly and cumulatively when given more supervision.

Proponiamo un approccio di meta-learning per guidare task di segmentazione visiva da quantità variabili di supervisione.


Training generative adversarial networks requires balancing of delicate adversarial dynamics.Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes.In this work, we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator.We develop supporting theoretical analysis from the perspectives of differentiable games and stochastic approximation.Our experiments demonstrate that latent optimisation can significantly improve GAN training, obtaining state-of-the-art performance for the ImageNet (128 x 128) dataset.Our model achieves an Inception Score (IS) of 148 and an Frechet Inception Distance (FID) of 3.4, an improvement of 17% and 32% in IS and FID respectively, compared with the baseline BigGAN-deep model with the same architecture and number of parameters.

L'ottimizzazione latente migliora le dinamiche di adversarial training. Presentiamo sia l'analisi teorica che lo stato dell'arte della generazione di immagini con ImageNet 128x128.


In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset.We look at this problem in the setting where the number of parameters is greater than the number of sampled points.We show that for a wide class of differentiable activation functions (this class involves most nonlinear functions and excludes piecewise linear functions), we have that arbitrary first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular.We essentially show that these non-singular hidden layer matrix satisfy a ``"good" property for these big class of activation functions.Techniques involved in proving this result inspire us to look at a new algorithmic, where in between two gradient step of hidden layer, we add a stochastic gradient descent (SGD) step of the output layer.In this new algorithmic framework, we extend our earlier result and show that for all finite iterations the hidden layer satisfies the``good" property mentioned earlier therefore partially explaining success of noisy gradient methods and addressing the issue of data independency of our earlier result.Both of these results are easily extended to hidden layers given by a flat matrix from that of a square matrix.Results are applicable even if network has more than one hidden layer provided all inner hidden layers are arbitrary, satisfy non-singularity, all activations are from the given class of differentiable functions and optimization is only with respect to the outermost hidden layer.Separately, we also study the smoothness properties of the objective function and show that it is actually Lipschitz smooth, i.e., its gradients do not change sharply.We use smoothness properties to guarantee asymptotic convergence of $O(1/\text{number of iterations})$ to a first-order optimal solution.

Questo articolo parla delle proprietà teoriche del punto ottimale del primo ordine di una rete neurale a due layer nel caso sovra-parametrizzato


We introduce the concept of channel aggregation in ConvNet architecture, a novel compact representation of CNN features useful for explicitly modeling the nonlinear channels encoding especially when the new unit is embedded inside of deep architectures for action recognition.The channel aggregation is based on multiple-channels features of ConvNet and aims to be at the spot finding the optical convergence path at fast speed.We name our proposed convolutional architecture “nonlinear channels aggregation networks (NCAN)” and its new layer “nonlinear channels aggregation layer (NCAL)”.We theoretically motivate channels aggregation functions and empirically study their effect on convergence speed and classification accuracy.Another contribution in this work is an efficient and effective implementation of the NCAL, speeding it up orders of magnitude.We evaluate its performance on standard benchmarks UCF101 and HMDB51, and experimental results demonstrate that this formulation not only obtains a fast convergence but stronger generalization capability without sacrificing performance.

Un'architettura permette alla CNN addestrata sulle sequenze video di convergere rapidamente