Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history of observations and maximizes its expected long-term reward with respect to this belief distribution. Our algorithm, Bayesian Policy Optimization, builds on recent policy optimization algorithms to learn a universal policy that navigates the exploration-exploitation trade-off to maximize the Bayesian value function. To address challenges from discretizing the continuous latent parameter space, we propose a new policy network architecture that encodes the belief distribution independently from the observable state. Our method significantly outperforms algorithms that address model uncertainty without explicitly reasoning about belief distributions and is competitive with state-of-the-art Partially Observable Markov Decision Process solvers.

Formuliamo l'incertezza del modello nel Reinforcement Learning come un processo decisionale continuo di Bayes-Adaptive Markov e presentiamo un metodo per l'ottimizzazione pratica e scalabile della policy bayesiana.

Usando un approccio bayesiano, c'è un migliore compromesso tra exploration e exploitation nel RL


For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.  We clarify a necessary condition for an evaluation metric not to behave this way: estimating the function must require a large sample from the model. In search of such a metric, we turn to neural network divergences (NNDs), which are defined in terms of a neural network trained to distinguish between distributions. The resulting benchmarks cannot be ``won'' by training set memorization, while still being perceptually correlated and computable only from samples. We survey past work on using NNDs for evaluation, implement an example black-box metric based on these ideas, and validate experimentally that it can measure a notion of generalization.

Noi sosteniamo che i benchmark GAN devono richiedere un grande sample dal modello per penalizzare la memorizzazione e indagare se le divergenze delle reti neurali hanno questa proprietà.

Gli autori propongono un criterio per valutare la qualità dei sample prodotti da una Generative Adversarial Network.


Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data. In this work, we make the first step to open the black box by introducing dialogue acts into open domain dialogue generation. The dialogue acts are generally designed and reveal how people engage in social chat. Inspired by analysis on real data, we propose jointly modeling dialogue act selection and response generation, and perform learning with human-human conversations tagged with a dialogue act classifier and a reinforcement approach to further optimizing the model for long-term conversation. With the dialogue acts, we not only achieve significant improvement over state-of-the-art methods on response quality for given contexts and long-term conversation in both machine-machine simulation and human-machine conversation, but also are capable of explaining why such achievements can be made.

Generazione di dialogo open-domain con atti di dialogo

Gli autori usano una tecnica di distant supervision per aggiungere tag di atti di dialogo come fattore condizionante per generare risposte nei dialoghi open-domain

L'articolo descrive una tecnica per incorporare atti di dialogo in agenti di conversazione neurali


We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics. Due to the lack of paired samples and without any definition of the semantic information, the problem might seem ill-posed. Specifically, in typical cases, it seems possible to build infinitely many alternative mappings  from every target mapping. This apparent ambiguity stands in sharp contrast to the recent empirical success in solving this problem.   We identify the abstract notion of aligning two domains in a semantic way with concrete terms of minimal relative complexity. A theoretical framework for measuring the complexity of compositions of functions is developed in order to show that it is reasonable to expect the minimal complexity mapping to be unique. The measured complexity used is directly related to the depth of the neural networks being learned and a semantically aligned mapping could then be captured simply by learning using architectures that are not much bigger than the minimal architecture.   Various predictions are made based on the hypothesis that semantic alignment can be captured by the minimal mapping. These are verified extensively. In addition, a new mapping algorithm is proposed and shown to lead to better mapping results.

La nostra ipotesi è che, dati due domini, la mappatura di minor complessità che ha una bassa discrepanza approssima la mappatura di destinazione.

L'articolo affronta il problema dell'apprendimento di mappature tra diversi domini senza alcuna supervisione, affermando tre congetture.

Dimostra che nell'apprendimento unsupervised su dati non allineati è possibile imparare la mappatura tra domini usando solo GAN senza una loss di ricostruzione.


We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.

Raffiniamo i risultati di sovra-approssimazione dei verificatori incompleti usando solutori MILP per dimostrare proprietà di robustezza maggiori rispetto allo stato dell'arte.

Introduce un verificatore che ottiene un miglioramento della precisione dei verificatori incompleti e della scalabilità dei verificatori completi usando la sovra-parametrizzazione, la programmazione lineare intera mista e il rilassamento della programmazione lineare.

Una strategia mista per ottenere una migliore precisione nelle verifiche di robustezza delle reti neurali feed-forward con funzioni di attivazione lineari piecewise, ottenendo una migliore precisione rispetto ai verificatori incompleti e una maggiore scalabilità rispetto ai verificatori completi.


A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data. In addition, it has been noted that the backward computation of the Baum-Welch algorithm for HMMs is a special case of the back-propagation algorithm used for neural networks (Eisner (2016)). Do these observations suggest that, despite their many apparent differences, HMMs are a special case of RNNs? In this paper, we show that that is indeed the case, and investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization. In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmaxes, and the use of non-linearities—in order to pin down their empirical effects. We present a comprehensive empirical study to provide insights into the interplay between expressivity and interpretability in this model family with respect to language modeling and parts-of-speech induction.

Gli HMM sono un caso speciale di RNN? Indaghiamo una serie di trasformazioni dell'architettura tra HMM e RNN, sia attraverso derivazioni teoriche che ibridazioni empiriche e forniamo nuove intuizioni.

Questo articolo esplora se gli HMM sono un caso speciale di RNN utilizzando il language modeling e il POS tagging


Deep neural networks have been tremendously successful in a number of tasks.  One of the main reasons for this is their capability to automatically  learn representations of data in levels of abstraction,  increasingly disentangling the data as the internal transformations are applied.  In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement.  This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation.  The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren't needed.  Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model.

Proponiamo un nuovo metodo di regolarizzazione che penalizza la covarianza tra le dimensioni degli hidden layer in una rete.

Questo articolo presenta un meccanismo di regolarizzazione che penalizza la covarianza tra tutte le dimensioni nella rappresentazione latente di una rete neurale al fine di dissociare la rappresentazione latente


This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature filters, each of which discerns objects belonging to one of the classes only. Classification of an input can be inferred from the maximum response of the filters. A multiple check with multiple versions of filters can diminish fluctuation and yields better performance. This scheme of discerning, maximum response and multiple check is a method of general viability to improve performance of feedforward networks, and the filter training itself is a promising feature abstraction procedure. In contrast to the direct sorting, the scheme mimics the classification process mediated by a series of one component picking.

Lo schema proposto imita il processo di classificazione mediato da una serie di prelievi di un componente.

Un metodo per aumentare l'accuratezza delle deep network su task di classificazione multiclasse tramite una riduzione della classificazione multiclasse a quella binaria.

Una nuova procedura di classificazione per il discernimento, la risposta massima e il controllo multiplo per migliorare la precisione delle reti mediocri e migliorare le reti feedforward.


A long-held conventional wisdom states that larger models train more slowly when using gradient descent. This work challenges this widely-held belief, showing that larger models can potentially train faster despite the increasing computational requirements of each training step. In particular, we study the effect of network structure (depth and width) on halting time and show that larger models---wider models in particular---take fewer training steps to converge.   We design simple experiments to quantitatively characterize the effect of overparametrization on weight space traversal. Results show that halting time improves when growing model's width for three different applications, and the improvement comes from each factor: The distance from initialized weights to converged weights shrinks with a power-law-like relationship, the average step size grows with a power-law-like relationship, and gradient vectors become more aligned with each other during traversal.

Empiricamente dimostra che i modelli più grandi si addestrano in meno step di allenamento, perché tutti i fattori di attraversamento dello spazio dei pesi migliorano.

Questo articolo mostra che RNN più larghe migliorano la velocità di convergenza quando sono applicate a problemi di NLP, e per estensione mostra anche l'effetto di aumentare le larghezze nelle deep neural network sulla convergenza dell'ottimizzazione

Questo articolo caratterizza l'impatto della sovra-parametrizzazione nel numero di iterazioni necessarie a un algoritmo per convergere, e presenta ulteriori osservazioni empiriche sugli effetti della sovra-parametrizzazione nel training delle reti neurali.


Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called variable-misuse bugs. The state-of-the-art solution for variable misuse enumerates potential fixes for all possible bug locations in a program, before selecting the best prediction. We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs. We present multi-headed pointer networks for this purpose, with one head each for localization and repair. The experimental results show that the joint model significantly outperforms an enumerative solution that uses a pointer based model for repair alone.

Multi-headed Pointer Network per apprendere congiuntamente a localizzare e a riparare i bug di abuso di variabili

Propone un modello basato su LSTM con puntatori per suddividere il problema del VarMisuse in più step.

Questo articolo presenta un modello basato su LSTM per il rilevamento e la riparazione dei bug di VarMisuse, e dimostra miglioramenti significativi rispetto agli approcci precedenti su diversi dataset.


Classification and clustering have been studied separately in machine learning and computer vision. Inspired by the recent success of deep learning models in solving various vision problems (e.g., object recognition, semantic segmentation) and the fact that humans serve as the gold standard in assessing clustering algorithms, here, we advocate for a unified treatment of the two problems and suggest that hierarchical frameworks that progressively build complex patterns on top of the simpler ones (e.g., convolutional neural networks) offer a promising solution. We do not dwell much on the learning mechanisms in these frameworks as they are still a matter of debate, with respect to biological constraints. Instead, we emphasize on the compositionality of the real world structures and objects. In particular, we show that CNNs, trained end to end using back propagation with noisy labels, are able to cluster data points belonging to several overlapping shapes, and do so much better than the state of the art algorithms. The main takeaway lesson from our study is that mechanisms of human vision, particularly the hierarchal organization of the visual ventral stream should be taken into account in clustering algorithms (e.g., for learning representations in an unsupervised manner or with minimum supervision) to reach human level clustering performance. This, by no means, suggests that other methods do not hold merits. For example, methods relying on pairwise affinities (e.g., spectral clustering) have been very successful in many cases but still fail in some cases (e.g., overlapping clusters).

L'articolo convalida l'idea che le reti neurali convoluzionali profonde potrebbero imparare a raggruppare i dati di input meglio di altri metodi di clustering notando la loro capacità di interpretare il contesto di ogni punto di input grazie a un ampio campo visivo.

Questo lavoro combina il deep learning per la rappresentazione delle feature con il task di clustering unsupervised simile a quello umano.


Instancewise feature scoring is a method for model interpretation, which yields, for each test instance, a vector of importance scores associated with features. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions, but incur an exponential complexity in the number of features.   This combinatorial explosion arises from the definition of Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization.   In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring on black-box models.   We establish the relationship of our methods to the Shapley value and a closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods using both quantitative metrics and human evaluation.

Sviluppiamo due algoritmi di complessità lineare per l'interpretazione di modelli model-agnostic basata sul valore di Shapley, nel setting in cui il contributo delle feature all'obiettivo è ben approssimato da una fattorizzazione strutturata a grafo.

L'articolo propone due approssimazioni al valore di Shapley usato per generare i punteggi delle feature per l'interpretabilità.

Questo articolo propone due metodi per la valutazione dell'importanza delle feature in base all'istanza utilizzando i valori di Shapely, e fornisce due metodi efficienti per calcolare i valori di Shapely approssimati quando c'è una struttura nota che mette in relazione le feature.


According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand.

Sono stati trovati codici locali nelle reti neurali feed-forward

Un metodo per determinare fino a che punto i singoli neuroni in un hidden layer di una MLP codifichino un codice localista, che viene studiato per diverse rappresentazioni di input.

Studia lo sviluppo di rappresentazioni localistiche negli hidden layer delle reti neurali feed-forward.


Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches however primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in relational databases, such as text, images, and numerical values. In our approach, we propose a multimodal embedding using different neural encoders for this variety of data, and combine with existing models to learn embeddings of the entities. We extend existing datasets to create two novel benchmarks, YAGO-10-plus and MovieLens-100k-plus, that contain additional relations such as textual descriptions and images of the original entities. We demonstrate that our model utilizes the additional information effectively to provide further gains in accuracy. Moreover, we test our learned multimodal embeddings by using them to predict missing multimodal attributes.

Estensione della modellazione relazionale per supportare i dati multimodali usando codificatori neurali.

Questo articolo propone di eseguire la predizione dei link nelle knowledge base integrando le entità originali con informazioni multimodali, e presenta un modello in grado di codificare tutti i tipi di informazioni quando assegna un punteggio alle triple.

L'articolo riguarda l'embedding di informazioni di diversa natura negli approcci di link prediction


An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing.   In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks.  In the first stage, we run SG-MCMC with group sparse priors to draw an ensemble of samples from the posterior distribution of network parameters. In the second stage, we apply weight-pruning to each sampled network and then perform retraining over the remained connections.  In this way of learning SSEs with SG-MCMC and pruning, we not only achieve high prediction accuracy since SG-MCMC enhances exploration of the model-parameter space, but also reduce memory and computation cost significantly in both training and testing of NN ensembles.  This is thoroughly evaluated in the experiments of learning SSE ensembles of both FNNs and LSTMs.  For example, in LSTM based language modeling (LM), we obtain 21\% relative reduction in LM perplexity by learning a SSE of 4 large LSTM models, which has only 30\% of model parameters and 70\% of computations in total, as compared to the baseline large LSTM LM.  To the best of our knowledge, this work represents the first methodology and empirical study of integrating SG-MCMC, group sparse prior and network pruning together for learning NN ensembles.

Si propone un nuovo metodo integrando il sampling SG-MCMC, il prior sparso di gruppo e il pruning della rete per imparare Sparse Structured Ensemble (SSE) con prestazioni migliori e costi significativamente ridotti rispetto ai metodi tradizionali.

Gli autori propongono una procedura per generare un'ensemble di modelli strutturati sparsi

Un nuovo framework per il training di ensemble di reti neurali che utilizza metodi SG-MCMC all'interno del deep learning, e poi aumenta l'efficienza computazionale tramite sparsità+pruning di gruppo.

Questo articolo esplora l'uso di FNN e LSTM per rendere la media dei modelli bayesiani più fattibile da computare e per migliorare le prestazioni medie dei modelli.


This paper introduces a new framework for data efficient and versatile learning. Specifically:  1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods.   2) We introduce \Versa{}, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. \Versa{} substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training.  3) We evaluate \Versa{} on benchmark datasets where the method sets new state-of-the-art results, and can handle arbitrary number of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.

Nuovo framework per il meta-learning che unifica ed estende un'ampia classe di metodi esistenti per il few-shot learning. Raggiunge una forte performance sui benchmark di few-shot learning senza richiedere un'inferenza iterativa al test-time.

Questo lavoro affronta il few-shot learning da un punto di vista dell'inferenza probabilistica, raggiungendo lo stato dell'arte nonostante una configurazione più semplice di molti altri concorrenti


In recent years, softmax together with its fast approximations has become the de-facto loss function for deep neural networks with multiclass predictions. However, softmax is used in many problems that do not fully fit the multiclass framework and where the softmax assumption of mutually exclusive outcomes can lead to biased results. This is often the case for applications such as language modeling, next event prediction and matrix factorization, where many of the potential outcomes are not mutually exclusive, but are more likely to be independent conditionally on the state. To this end, for the set of problems with positive and unlabeled data, we propose a relaxation of the original softmax formulation, where, given the observed state, each of the outcomes are conditionally independent but share a common set of negatives. Since we operate in a regime where explicit negatives are missing, we create an adversarially-trained model of negatives and derive a new negative sampling and weighting scheme which we denote as Cooperative Importance Sampling (CIS). We show empirically the advantages of our newly introduced negative sampling scheme by pluging it in the Word2Vec algorithm and benching it extensively against other negative sampling schemes on both language modeling and matrix factorization tasks and show large lifts in performance.

Definizione di una loss softmax parzialmente mutualmente esclusiva per dati positivi e implementazione di uno schema di sampling basato sulla cooperazione

Questo articolo presenta il Cooperative Importance Sampling per risolvere il problema dell'assunzione reciprocamente esclusiva del softmax tradizionale che è biased quando i sample negativi non sono esplicitamente definiti

Questo articolo propone metodi PMES per rilassare l'ipotesi di risultato esclusivo nella loss softmax, dimostrandone il merito empirico nel migliorare i modelli di embedding di tipo word2vec.


Over the past few years, various tasks involving videos such as classification, description, summarization and question answering have received a lot of attention. Current models for these tasks compute an encoding of the video by treating it as a sequence of images and going over every image in the sequence, which becomes computationally expensive for longer videos. In this paper, we focus on the task of video classification and aim to reduce the computational cost by using the idea of distillation. Specifically, we propose a Teacher-Student network wherein the teacher looks at all the frames in the video but the student looks at only a small fraction of the frames in the video. The idea is to then train the student to minimize  (i)  the difference between the final representation computed by the student and the teacher and/or (ii) the difference between the distributions predicted by the teacher and the student. This smaller student network which involves fewer computations but still learns to mimic the teacher can then be employed at inference time for video classification. We experiment with the YouTube-8M dataset and show  that the proposed student network can reduce the inference time by upto 30% with a negligent drop in the performance.

Struttura teacher-student per una classificazione video efficiente utilizzando un minor numero di frame

L'articolo propone un'idea per distillare da un modello di classificazione video completo un piccolo modello che riceve solo un numero minore di frame.

Gli autori presentano una rete teacher-student per risolvere il problema della classificazione dei video, proponendo algoritmi di training seriali e paralleli volti a ridurre i costi computazionali.


Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.

Una visione statistica unificata dell'ampia classe di deep generative model

L'articolo sviluppa un framework che interpreta gli algoritmi GAN come l'esecuzione di una forma di inferenza variazionale su un modello generativo che ricostruisce una variabile indicatrice del fatto che un sample appartenga alla vera distribuzione di dati generativi.


Deep neural networks have demonstrated promising prediction and classification performance on many healthcare applications. However, the interpretability of those models are often lacking. On the other hand, classical interpretable models such as rule lists or decision trees do not lead to the same level of accuracy as deep neural networks and can often be too complex to interpret (due to the potentially large depth of rule lists). In this work, we present PEARL,  Prototype lEArning via Rule Lists, which iteratively uses rule lists to guide a neural network to learn representative data prototypes. The resulting prototype neural network provides  accurate prediction, and the prediction can be easily explained by  prototype and its guiding rule lists. Thanks to the prediction power of neural networks, the rule lists from				 prototypes are more concise and hence provide better interpretability. On two real-world electronic healthcare records (EHR) datasets, PEARL consistently outperforms all baselines across both datasets, especially achieving performance improvement over conventional rule learning by up to 28% and over prototype learning by up to 3%. Experimental results also show the resulting interpretation of PEARL is  simpler than the standard rule learning.

Un metodo che combina l'apprendimento di liste di regole e il prototype learning

Presenta un nuovo framework di predizione interpretabile, che combina l'apprendimento basato su regole, il prototype learning e le NN ed è particolarmente applicabile ai dati longitudinali.

Questo articolo mira ad affrontare la mancanza di interpretabilità dei modelli di deep learning, e propone Prototype Learning via Rule Lists (PEARL), che combina l'apprendimento delle regole e prototype learning per ottenere una classificazione più accurata e rendere il task di interpretabilità più semplice.


Generative Adversarial Networks (GANs) are powerful tools for realistic image generation. However, a major drawback of GANs is that they are especially hard to train, often requiring large amounts of data and long training time. In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images by enforcing structure on the latent generative space using similar approaches in \cite{deligan}. The structure of the latent space we consider in this paper is modeled as a mixture of Gaussians, whose parameters are learned in the training process. Furthermore, to improve stability and efficiency, we use the Fisher Integral Probability Metric as the divergence measure in our GAN model, instead of the Jensen-Shannon divergence. We show by experiments that the Deli-Fisher GAN performs better than DCGAN, WGAN, and the Fisher GAN as measured by inception score.

Questo articolo propone una nuova Generative Adversarial Network che è più stabile, più efficiente e produce immagini migliori di quelle dello status-quo

Questo paper combina Fisher-GAN e Deli-GAN

Questo paper combina Deli-GAN, che ha una distribuzione a priori mista nello spazio latente, e Fisher GAN, che usa Fisher IPM invece di JSD come obiettivo.


Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attentional mechanisms into neural networks increases the performance of the system substantially. We report on a new modular network architecture that applies an attentional mechanism not on temporal and spatial regions of the input, but on sensor selection for multi-sensor setups. This network called the sensor transformation attention network (STAN) is evaluated in scenarios which include the presence of natural noise or synthetic dynamic noise. We demonstrate how the attentional signal responds dynamically to changing noise levels and sensor-specific noise, leading to reduced word error rates (WERs) on both audio and visual tasks using TIDIGITS and GRID; and also on CHiME-3, a multi-microphone real-world noisy dataset. The improvement grows as more channels are corrupted as demonstrated on the CHiME-3 dataset. Moreover, the proposed STAN architecture naturally introduces a number of advantages including ease of removing sensors from existing architectures, attentional interpretability, and increased robustness to a variety of noise environments.

Introduciamo un'architettura di rete modulare multisensore con un meccanismo di attention che permette la selezione dinamica del sensore su dati rumorosi del mondo reale da CHiME-3.

Un'architettura neurale generica in grado di apprendere l'attention che deve essere prestata ai diversi canali di input a seconda della qualità relativa di ogni sensore rispetto agli altri.

Considera l'uso dell'attention per la selezione del sensore o del canale con risultati su TIDIGITS e GRID che mostrano un beneficio dell'attention rispetto alla concatenazione delle feature.


Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate representations of the data, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud. The local neural network (NN) is used to generate the feature representations. To avoid local training and protect data privacy, the local NN is derived from pre-trained NNs. The cloud NN is then trained based on the extracted intermediate representations for the target learning task. We validate the idea of DNN splitting by characterizing the dependency of privacy loss and classification accuracy on the local NN topology for a convolutional NN (CNN) based image classification task. Based on the characterization, we further propose PrivyNet to determine the local NN topology, which optimizes the accuracy of the target learning task under the constraints on privacy loss, local computation, and storage. The efficiency and effectiveness of PrivyNet are demonstrated with CIFAR-10 dataset.

Per consentire il training DNN basato sul cloud e proteggere contemporaneamente la privacy dei dati, proponiamo di sfruttare le rappresentazioni intermedie dei dati, il che si ottiene dividendo le DNN e distribuendole separatamente su piattaforme locali e sul cloud.

Questo articolo propone una tecnica per privatizzare i dati imparando una rappresentazione delle feature che è difficile da usare per la ricostruzione delle immagini, ma utile per la classificazione delle stesse.


Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks (RNNs) in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from ‘serialised’ MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data, and demonstrate results from differentially private training of the RCGAN.

GAN ricorrenti condizionali per la generazione di sequenze mediche a valori reali, mostrando nuovi approcci di valutazione e un'analisi empirica della privacy.

Propone di utilizzare i dati sintetici generati da GAN come sostituzione dei dati personali identificabili nel training di modelli ML per applicazioni sensibili alla privacy

Gli autori propongono una nuova architettura GAN ricorrente che genera sequenze di dominio continue, e la valutano su diversi task sintetici e su un task con dati costituiti da serie temporali ICU.

Propone di utilizzare RGAN e RCGAN per generare sequenze sintetiche di dati reali.


Emphasis effects – visual changes that make certain elements more  prominent – are commonly used in information visualization to draw  the user’s attention or to indicate importance. Although theoretical  frameworks of emphasis exist (that link visually diverse emphasis  effects through the idea of visual prominence compared to background  elements), most metrics for predicting how emphasis effects  will be perceived by users come from abstract models of human  vision which may not apply to visualization design. In particular,  it is difficult for designers to know, when designing a visualization,  how different emphasis effects will compare and what level of one  effect is equivalent to what level of another. To address this gap,  we carried out two studies that provide empirical evidence about  how users perceive different emphasis effects, using three visual  variables (colour, size, and blur/focus) and eight strength levels.  Results from gaze tracking, mouse clicks, and subjective responses  show that there are significant differences between visual variables  and between levels, and allow us to develop an initial understanding  of perceptual equivalence. We developed a model from the data in  our first study, and used it to predict the results in the second; the  model was accurate, with high correlations between predictions and  real values. Our studies and empirical models provide valuable new  information for designers who want to understand and control how  emphasis effects will be perceived by users.

I nostri studi e modelli empirici forniscono nuove informazioni preziose per i designer che vogliono capire e controllare come gli effetti dell'enfasi saranno percepiti dagli utenti

Questo articolo considera quale evidenziazione visiva venga percepita più velocemente nella visualizzazione dei dati e come i diversi metodi di evidenziazione si confrontino tra loro

Due studi sull'efficacia degli effetti di enfasi, uno che valuta i livelli di differenze utili, e uno più applicato utilizzando visualizzazioni effettive diverse per un'indagine più ecologicamente valida.


Memory Network based models have shown a remarkable progress on the task of relational reasoning.  Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced.   Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory.  We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures.   We follow memory network's four components while each component operates similar to the relation network without taking a pair of objects.   As a result, our model is as simple as RN but the computational complexity is reduced to linear time.  It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and  bAbI dialog dataset.

Una semplice architettura di ragionamento basata sulle memory network (MemNN) e la relation network (RN), riducendo la complessità temporale rispetto alla RN e raggiungendo un risultato allo stato dell'arte sulla storia bAbI basata su QA e sul dialogo bAbI.

Introduce la Related memory network (RMN), un miglioramento rispetto alle relation network (RN).


We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as "coupled ensembles". The approach is very generic and can be applied with almost any neural network architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and SVHN tasks. For the same parameter budget, DenseNet-BC has an error rate of 3.46%, 17.18%, and 1.8% respectively.   With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42% respectively on these tasks.

Mostriamo che dividere una rete neurale in rami paralleli migliora le prestazioni e che un corretto accoppiamento dei rami migliora ulteriormente le prestazioni.

Il lavoro propone una riconfigurazione del modello CNN esistente allo stato dell'arte utilizzando una nuova architettura di ramificazione, con prestazioni migliori.

Questo articolo mostra i benefici di risparmio di parametri dell'ensembling accoppiato.

Presenta un'architettura di deep network che elabora i dati utilizzando più rami paralleli e combina il posteriore da questi rami per calcolare i punteggi finali.


Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications.

Combina l'iniezione di rumore, la quantizzazione graduale e l'apprendimento del clamping dell'attivazione per ottenere una quantizzazione a 3, 4 e 5 bit allo stato dell'arte

Propone di iniettare rumore durante il training e di bloccare i valori dei parametri in un layer così come l'output dell'attivazione nella quantizzazione della rete neurale.

Un metodo per la quantizzazione delle deep neural network per la classificazione e la regressione, utilizzando l'iniezione di rumore, il clamping con attivazioni massime apprese e la quantizzazione graduale a blocchi per performare alla pari o meglio dei metodi allo stato dell'arte.


In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.

Proponiamo Leap, un framework che trasferisce la conoscenza tra i processi di apprendimento minimizzando la distanza prevista che il processo di training percorre sulla superficie della loss di un task.

L'articolo propone un nuovo obiettivo di meta-learning volto a superare gli approcci allo stato dell'arte quando si tratta di collezioni di task che mostrano una sostanziale diversità tra i task


Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added task, typically as many as the original network. We propose a method called Deep Adaptation Networks (DAN) that constrains newly learned filters to be linear combinations of existing ones. DANs preserve performance on the original task, require a fraction (typically 13%) of the number of parameters compared to standard fine-tuning procedures and converge in less cycles of training to a comparable or better level of performance. When coupled with standard network quantization techniques, we further reduce the parameter cost to around 3% of the original with negligible or no loss in accuracy. The learned architecture can be controlled to switch between various learned representations, enabling a single network to solve a task from multiple different domains. We conduct extensive experiments showing the effectiveness of our method on a range of image classification tasks and explore different aspects of its behavior.

Un'alternativa al transfer learning che impara più velocemente, richiede molti meno parametri (3-13%), di solito raggiunge risultati migliori e conserva precisamente le prestazioni sui vecchi task.

Moduli di controllo per l'apprendimento incrementale su dataset di classificazione delle immagini


High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents a general technique toward 8-bit low precision inference of convolutional neural networks, including 1) channel-wise scale factors of weights, especially for depthwise convolution, 2) Winograd convolution, and 3) topology-wise 8-bit support. We experiment the techniques on top of a widely-used deep learning framework. The 8-bit optimized model is automatically generated with a calibration process from FP32 model without the need of fine-tuning or retraining. We perform a systematical and comprehensive study on 18 widely-used convolutional neural networks and demonstrate the effectiveness of 8-bit low precision inference across a wide range of applications and use cases, including image classification, object detection, image segmentation, and super resolution. We show that the inference throughput  and latency are improved by 1.6X and 1.5X respectively with minimal within 0.6%1to no loss in accuracy from FP32 baseline. We believe the methodology can provide the guidance and reference design of 8-bit low precision inference for other frameworks. All the code and models will be publicly available soon.

Presentiamo una tecnica generale per l'inferenza a 8 bit a bassa precisione delle reti neurali convoluzionali.

Questo articolo progetta un sistema per quantizzare automaticamente i modelli preaddestrati CNN


Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space. We extend this line of work by imposing hyperbolic geometry on the embeddings used to compute the ubiquitous attention mechanisms for different neural networks architectures. By only changing the geometry of embedding of object representations, we can use the embedding space more efficiently without increasing the number of parameters of the model. Mainly as the number of objects grows exponentially for any semantic distance from the query, hyperbolic geometry  --as opposed to Euclidean geometry-- can encode those objects without having any interference. Our method shows improvements in generalization on neural machine translation on WMT'14 (English to German), learning on graphs (both on synthetic and real-world graph tasks) and visual question answering (CLEVR) tasks while keeping the neural representations compact.

Proponiamo di incorporare bias induttivi e operazioni provenienti dalla geometria iperbolica per migliorare il meccanismo di attention delle reti neurali.

Questo articolo sostituisce la similitudine tramite prodotto scalare usata nei meccanismi di attention con la distanza iperbolica negativa, e la applica al modello di Transformer esistente, alle graph attention network e alle Relation Network

Gli autori propongono un nuovo approccio per migliorare l'attention relazionale cambiando le funzioni di corrispondenza e aggregazione per utilizzare la geometrica iperbolica.


We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later exposed to the real world where it is hazardous to employ RL policies. This framework for training deep RL policies involve a zero-sum  dynamic game against an adversarial agent, where the goal is to drive the system dynamics to a saddle region. Using a variant of the guided policy search algorithm, our agent learns to adopt robust policies that require less samples for learning the dynamics and performs better than the GPS algorithm. Without loss of generality, we demonstrate that deep RL policies trained in this fashion will be maximally robust to a ``worst" possible adversarial disturbances.

Questo articolo dimostra come la teoria del controllo H-infinity può aiutare a progettare meglio policy profonde e robuste per i task di motor robot

Propone di incorporare elementi di controllo robusto nella ricerca guidata di una policy per concepire un metodo che sia resiliente alle perturbazioni e al mismatch del modello.

L'articolo presenta un metodo per valutare la sensibilità e la robustezza delle policy di deep RL, e propone un approccio di gioco dinamico per l'apprendimento di policy robuste.


Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exist shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.

Analisi della vulnerabilità dei classificatori alle perturbazioni universali e la relazione con la curvatura del decision boundary.

L'articolo fornisce un'interessante analisi che collega la geometria dei confini decisionali del classificatore a piccole perturbazioni universali adversarial.

Questo articolo discute le perturbazioni universali - perturbazioni che possono fuorviare un classificatore addestrato se aggiunte alla maggior parte dei datapoint di input.

L'articolo sviluppa modelli che tentano di spiegare l'esistenza di perturbazioni universali che ingannano le reti neurali


Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require a human expert to be able to actually perform the task in order to generate the demonstration. Instruction following from natural language instructions provides an appealing alternative: in the same way that we can specify goals to other humans simply by speaking or writing, we would like to be able to specify tasks for our machines. However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task. In this work, we propose an interactive formulation of the task specification problem, where iterative language corrections are provided to an autonomous agent, guiding it in acquiring the desired skill. Our proposed language-guided policy learning algorithm can integrate an instruction and a sequence of corrections to acquire new skills very quickly. In our experiments, we show that this method can enable a policy to follow instructions and corrections for simulated navigation and manipulation tasks, substantially outperforming direct, non-interactive instruction following.

Proponiamo un metodo di meta-learning per la correzione interattiva delle policy con il linguaggio naturale.

Questo articolo fornisce un framework di meta learning che mostra come apprendere nuovi task in una configurazione interattiva. Ogni task viene appreso attraverso un setup di reinforcement learning, e poi il task viene aggiornato osservando nuove istruzioni.

Questo paper insegna agli agenti a completare task tramite istruzioni in linguaggio naturale in un processo iterativo.


Deep generative models such as Generative Adversarial Networks (GANs) and  Variational Auto-Encoders (VAEs) are important tools to capture and investigate  the properties of complex empirical data. However, the complexity of their inner  elements makes their functionment challenging to assess and modify. In this  respect, these architectures behave as black box models. In order to better  understand the function of such networks, we analyze their modularity based on  the counterfactual manipulation of their internal variables. Our experiments on the  generation of human faces with VAEs and GANs support that modularity between  activation maps distributed over channels of generator architectures is achieved  to some degree, can be used to better understand how these systems operate and allow meaningful transformations of the generated images without further training.  erate and edit the content of generated images.

Studiamo la modularità dei deep generative model.

L'articolo fornisce un modo per indagare la struttura modulare dei deep generative model, con il concetto chiave di distribuire su canali di architetture generatrici.


Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from in the loop query execution over the database to learn a policy to generate the query, which contains unordered parts that are less suitable for optimization via cross entropy loss. Moreover, Seq2SQL leverages the structure of SQL to prune the space of generated queries and significantly simplify the generation problem. In addition to the model, we release WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets. By applying policy based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.

Introduciamo Seq2SQL, che traduce le domande in query SQL utilizzando le reward dell'esecuzione delle query online, e WikiSQL, un dataset di tabelle/question/query SQL di diversi ordini di grandezza maggiore dei dataset esistenti.

Un nuovo dataset di parsing semantico che si concentra sulla generazione di SQL dal linguaggio naturale utilizzando un modello basato sul reinforcement learning


We introduce Explainable Adversarial Learning, ExL, an approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We prove our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components. We show that models learnt with our approach perform remarkably well against a wide-range of attacks. Furthermore, combining ExL with state-of-the-art adversarial training extends the robustness of a model, even beyond what it is adversarially trained for, in both white-box and black-box attack scenarios.

La modellazione del rumore in ingresso durante il training discriminativo migliora l'adversarial robustness. Si propone una metrica di valutazione basata sulla PCA per l'adversarial robustness

Questo articolo propone ExL, un metodo di adversarial training che utilizza il rumore moltiplicativo e che si dimostra utile per difendersi dagli attacchi blackbox su tre dataset.

Questo paper include il rumore moltiplicativo N nei dati di training per ottenere l'adversarial robustness, quando si allena sia sui parametri del modello theta che sul rumore stesso.


We propose a method which can visually explain the classification decision of deep neural networks (DNNs). There are many proposed methods in machine learning and computer vision seeking to clarify the decision of machine learning black boxes, specifically DNNs.   All of these methods try to gain insight into why the network "chose class A" as an answer. Humans, when searching for explanations, ask two types of questions. The first question is, "Why did you choose this answer? " The second question asks, "Why did you not choose answer B over A?" The previously proposed methods are either not able to provide the latter directly or efficiently.   We introduce a method capable of answering the second question both directly and efficiently. In this work, we limit the inputs to be images. In general, the proposed method generates explanations in the input space of any model capable of efficient evaluation and gradient evaluation. We provide results, showing the superiority of this approach for gaining insight into the inner representation of machine learning models.

Un metodo per rispondere alla domanda "perché non la classe B?" per spiegare le deep network

L'articolo propone un approccio per fornire spiegazioni visive contrastanti per le deep neural network.


We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping. We provide theoretical and numerical results on the inverse of ReLU-layers. First, we derive a necessary and sufficient condition on the existence of invariance that provides a geometric interpretation. Next, we move to robustness via analyzing local effects on the inverse. To conclude, we show how this reverse point of view not only provides insights into key effects, but also enables to view adversarial examples from different perspectives.

Analizziamo l'invertibilità delle deep neural network studiando le preimmagini dei layer ReLU e la stabilità dell'inverso.

Questo articolo studia il volume di preimmagine dell'attivazione di una rete ReLU ad un certo layer, e si basa sulla linearità a tratti della funzione forward di una rete ReLU.

Questo articolo presenta un'analisi dell'invarianza inversa delle reti ReLU e fornisce limiti superiori sui valori singolari di una train network.


While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where small but carefully chosen perturbations to the input can make the models produce extremely inaccurate outputs. This makes these models particularly unsuitable for safety-critical application domains (e.g. self-driving cars) where robustness is extremely important. Recent work has shown that augmenting training with adversarially generated data provides some degree of robustness against test-time attacks. In this paper we investigate how this approach scales as we increase the computational budget given to the defender. We show that increasing the number of parameters in adversarially-trained models increases their robustness, and in particular that ensembling smaller models while adversarially training the entire ensemble as a single model is a more efficient way of spending said budget than simply using a larger single model. Crucially, we show that it is the adversarial training of the ensemble, rather than the ensembling of adversarially trained models, which provides robustness.

L'adversarial training degli ensemble fornisce robustezza agli adversarial example superiore a quella osservata nei modelli addestrati in modo adversarial e negli ensemble addestrati in modo indipendente.

Propone di addestrare congiuntamente un ensemble di modelli, dove, ad ogni passo temporale, un insieme di esempi che sono adversarial all'ensemble stesso viene incorporato nell'apprendimento.


Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network – for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR100 (20 tasks) we obtain cross-stitch performance levels with an 85% average reduction in training time.

Routing network: un nuovo tipo di rete neurale che impara a instradare in modo adattivo il suo input per multi-task learning

L'articolo suggerisce di usare una rete modulare con un controllore che prende decisioni, ad ogni passo temporale, riguardo al prossimo modulo da applicare.

L'articolo presenta una nuova formulazione per l'apprendimento dell'architettura ottimale di una rete neurale in un framework di multi-task learning utilizzando reinforcement learning multi-agente per trovare una policy e mostra un miglioramento rispetto alle architetture hard-coded con layer condivisi.


We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of $L_0$ regularization. However, since the $L_0$ norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected $L_0$ regularized objective is differentiable with respect to the distribution parameters. We further propose the \emph{hard concrete} distribution for the gates, which is obtained by ``stretching'' a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer.

Mostriamo come ottimizzare la norma L_0 attesa dei modelli parametrici con la gradient descent e introduciamo una nuova distribuzione che facilita l'hard gating.

Gli autori introducono un approccio basato sul gradiente per minimizzare una funzione obiettivo con una penalità L0 sparsa per aiutare l'apprendimento di reti neurali sparse


Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a fully-connected layer. Perhaps surprisingly, we show that a linear model, that removes all the intermediate fully-connected layers, is still able to achieve a performance comparable to the state-of-the-art models. This significantly reduces the number of parameters, which is critical for semi-supervised learning where number of labeled examples are small. This in turn allows a room for designing more innovative propagation layers. Based on this insight, we propose a novel graph neural network that removes all the intermediate fully-connected layers, and replaces the propagation layers with attention mechanisms that respect the structure of the graph. The attention mechanism allows us to learn a dynamic and adaptive local summary of the neighborhood to achieve more accurate predictions. In a number of experiments on benchmark citation networks datasets, we demonstrate that our approach outperforms competing methods. By examining the attention weights among neighbors, we show that our model provides some interesting insights on how neighbors influence each other.

Proponiamo una nuova architettura di Graph Neural Network interpretabile basata sull'attention che supera le attuali Graph Neural Network allo stato dell'arte in dataset standard di riferimento

Gli autori propongono due estensioni delle GCN, rimuovendo le non linearità intermedie dal calcolo nelle GCN e aggiungendo un meccanismo di attention nel livello di aggregazione.

L'articolo propone un algoritmo di apprendimento semi-supervised per la classificazione dei nodi dei grafi, ispirato alle Reti Neurali Grafiche.


Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to the latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. The resulting method, perceptual generative autoencoder (PGA), is then incorporated with maximum likelihood or variational autoencoder (VAE) objective to train the generative model. With maximum likelihood, PGA generalizes the idea of reversible generative models to unrestricted neural network architectures and arbitrary latent dimensionalities. When combined with VAE, PGA can generate sharper samples than vanilla VAE.

Un framework per il training di modelli generativi basati su autoencoder, con loss non-adversarial e architetture di reti neurali non limitate.

Questo articolo usa autoencoder per fare il matching di distribuzioni in uno spazio ad alta dimensione.


The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.  Euclidean space has been the workhorse for embeddings; recently hyperbolic and spherical spaces have gained popularity due to their ability to better embed new types of structured data---such as hierarchical data---but most data is not structured so uniformly.  We address this problem by proposing learning embeddings in a product manifold combining multiple copies of these model spaces (spherical, hyperbolic, Euclidean), providing a space of heterogeneous curvature suitable for a wide variety of structures.  We introduce a heuristic to estimate the sectional curvature of graph data and directly determine an appropriate signature---the number of component spaces and their dimensions---of the product manifold.  Empirically, we jointly learn the curvature and the embedding in the product space via Riemannian optimization.  We discuss how to define and compute intrinsic quantities such as means---a challenging notion for product manifolds---and provably learnable optimization functions.  On a range of datasets and reconstruction tasks, our product space embeddings outperform single Euclidean or hyperbolic spaces used in previous works, reducing distortion by 32.55% on a Facebook social network dataset. We learn word embeddings and find that a product of hyperbolic spaces in 50 dimensions consistently improves on baseline Euclidean and hyperbolic embeddings, by 2.6  points in Spearman rank correlation on similarity tasks  and 3.4 points on analogy accuracy.

Gli spazi di embedding di product manifold con curvatura eterogenea producono rappresentazioni migliori rispetto agli spazi di embedding tradizionali per una varietà di strutture.

Propone un metodo di riduzione della dimensionalità che incorpora i dati in un product manifold di manifold sferici, euclidei e iperbolici. L'algoritmo si basa sul matching tra le distanze geodetiche sul product manifold e le distanze nei grafi.


Synthesizing user-intended programs from a small number of input-output exam-  ples is a challenging problem with several important applications like spreadsheet  manipulation, data wrangling and code refactoring. Existing synthesis systems  either completely rely on deductive logic techniques that are extensively hand-  engineered or on purely statistical models that need massive amounts of data, and in  general fail to provide real-time synthesis on challenging benchmarks. In this work,  we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique  that combines the best of both symbolic logic techniques and statistical models.  Thus, it produces programs that satisfy the provided specifications by construction  and generalize well on unseen examples, similar to data-driven systems. Our  technique effectively utilizes the deductive search framework to reduce the learning  problem of the neural component to a simple supervised learning setup. Further,  this allows us to both train on sparingly available real-world data and still leverage  powerful recurrent neural network encoders. We demonstrate the effectiveness  of our method by evaluating on real-world customer scenarios by synthesizing  accurate programs with up to 12× speed-up compared to state-of-the-art systems.

Integriamo metodi simbolici (deduttivi) e statistici (neural-based) per permettere la sintesi di programmi in tempo reale con una generalizzazione quasi perfetta da 1 esempio di input-output.

L'articolo presenta un approccio branch-and-bound per imparare buoni programmi dove una LSTM è usato per predire quali rami nell'albero di ricerca dovrebbero portare a buoni programmi

Propone un sistema che sintetizza programmi da un singolo esempio che generalizza meglio dello stato dell'arte precedente


Variational auto-encoders  (VAEs) offer a tractable approach when performing approximate inference in otherwise intractable generative models. However, standard VAEs often produce latent codes that are disperse and lack interpretability, thus making the resulting representations unsuitable for auxiliary tasks (e.g. classiﬁcation) and human interpretation. We address these issues by merging ideas from variational auto-encoders and sparse coding, and propose to explicitly model sparsity in the latent space of a VAE with a Spike and Slab prior distribution. We derive the evidence lower bound using a discrete mixture recognition function thereby making approximate posterior inference as computational efﬁcient as in the standard VAE case. With the new approach, we are able to infer truly sparse representations with generally intractable non-linear probabilistic models. We show that these sparse representations are advantageous over standard VAE representations on two benchmark classiﬁcation tasks (MNIST and Fashion-MNIST) by demonstrating improved classiﬁcation accuracy and signiﬁcantly increased robustness to the number of latent dimensions. Furthermore, we demonstrate qualitatively that the sparse elements capture subjectively understandable sources of variation.

Esploriamo l'intersezione di VAE e sparse coding.

Questo articolo propone un'estensione dei VAE con prior e posterior sparsi per imparare rappresentazioni sparse interpretabili.


A widely observed phenomenon in deep learning is the degradation problem: increasing  the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating mechanisms. However, the degradation problem persists in the context of plain feed-forward networks. In this work we propose a simple method to address this issue. The proposed method poses the learning of weights in deep networks as a constrained optimization problem where the presence of skip-connections is penalized by Lagrange multipliers. This allows for skip-connections to be introduced during the early stages of training and subsequently phased out in a principled manner. We demonstrate the benefits of such an approach with experiments on MNIST, fashion-MNIST, CIFAR-10 and CIFAR-100 where the proposed method is shown to greatly decrease the degradation effect (compared to plain networks) and is often competitive with ResNets.

L'eliminazione graduale delle skip-connection evita la degradazione nelle deep feed-forward network.

Gli autori presentano una nuova strategia di training, VAN, per il training di reti feed-forward molto profonde senza skip-connection

L'articolo introduce un'architettura che interpola linearmente tra ResNet e le deep network standard senza skip-connection.


Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prevent them from being deployed effectively on embedded platforms, limiting their applications. This work addresses the problem by proposing methods {\em Weight Reduction Quantisation} for compressing the memory footprint of the models, including reducing the number of weights and the number of bits to store each weight. Beside, applying with sparsity-inducing regularization, our work focuses on speeding up stochastic variance reduced gradients (SVRG) optimization on non-convex problem. Our method that mini-batch SVRG with $\ell$1 regularization on non-convex problem has faster and smoother convergence rates than SGD by using adaptive learning rates. Experimental evaluation of our approach uses MNIST and CIFAR-10 datasets on LeNet-300-100 and LeNet-5 models, showing our approach can reduce the memory requirements both in the convolutional and fully connected layers by up to 60$\times$ without affecting their test accuracy.

Compressione di deep neural network distribuite su dispositivi embedded.

Gli autori presentano un algoritmo di training basato su SVRG regolarizzato l-1 che è in grado di forzare molti pesi della rete ad essere pari a 0.

Questo lavoro riduce i requisiti di memoria.


It has been argued that the brain is a prediction machine that continuously learns how to make better predictions about the stimuli received from the external environment. For this purpose, it builds a model of the world around us and uses this model to infer the external stimulus. Predictive coding has been proposed as a mechanism through which the brain might be able to build such a model of the external environment. However, it is not clear how predictive coding can be used to build deep neural network models of the brain while complying with the architectural constraints imposed by the brain. In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment. Specifically, we used predictive coding to train a deep neural network on real-world images in a unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the latent representations generated by the model on images of objects that are never presented to the model during training. Despite the novel features of these objects the model is able to infer the latent representations for them. Furthermore, the reconstructions of the original images obtained from these latent representations preserve the important details of these objects.

Un algoritmo di apprendimento basato sulla codifica predittiva per costruire modelli di deep neural network del cervello

L'articolo considera l'apprendimento di una rete neurale generativa utilizzando una configurazione di codifica predittiva


In this paper, we propose deep convolutional generative adversarial networks (DCGAN) that learn to produce a 'mental image' of the input image as internal representation of a certain category of input data distribution.   This mental image is what the DCGAN 'imagines' that the input image might look like under ideal conditions.   The mental image contains a version of the input that is iconic, without any peculiarities that do not contribute to the ideal representation of the input data distribution within a category. A DCGAN learns this association by training an encoder to capture salient features from the original image and a decoder to convert salient features into its associated mental image representation.   Our new approach, which we refer to as a Mental Image DCGAN (MIDCGAN), learns features that are useful for recognizing entire classes of objects, and that this in turn has the benefit of helping single and zero shot recognition.   We demonstrate our approach on object instance recognition and handwritten digit recognition tasks.

Il riconoscimento di istanze di oggetti con adversarial autoencoder è stato eseguito con un nuovo obiettivo di "mental image" che è una rappresentazione canonica dell'immagine di input.

L'articolo propone un metodo per imparare le feature per il riconoscimento degli oggetti che è invariante a varie trasformazioni dell'oggetto, in particolare alla posa dell'oggetto.

Questo articolo ha studiato il task di riconoscimento few shot tramite un'immagine mentale generata come rappresentazione intermedia data l'immagine di input.


An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines techniques in \textit{formal methods} with \textit{hierarchical reinforcement learning} (HRL). The set of techniques we provide allows for the convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards using any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot.

Si combina la logica temporale con il reinforcement learning gerarchico per la composizione delle skill

L'articolo offre una strategia per costruire un MDP prodotto da un MDP originale e dall'automa associato a una formula LTL.

Propone di unire la logica temporale con il reinforcement learning gerarchico per semplificare la composizione delle skill.


The tremendous memory and computational complexity of Convolutional Neural Networks (CNNs) prevents the inference deployment on resource-constrained systems. As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented with just a few bits while achieving impressive prediction performance. However, aggressive quantization techniques still fail to achieve full-precision prediction performance on state-of-the-art CNN architectures on large-scale classification tasks. In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs. Our weight quantization scheme is based on trainable scaling factors and a nested-means clustering strategy which is robust to weight updates and therefore exhibits good convergence properties. The flexibility of nested-means clustering enables exploration of various n-ary weight representations with the potential of high parameter compression. For activations, we propose a linear quantization strategy that takes the statistical properties of batch normalization into account. We demonstrate the effectiveness of our approach using state-of-the-art models on ImageNet.

Proponiamo uno schema di quantizzazione per i pesi e le attivazioni delle deep neural network. Questo riduce l'impronta di memoria in modo sostanziale e accelera l'inferenza.

Compressione del modello CNN e accelerazione dell'inferenza usando la quantizzazione.


Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently. This means that we must not only specify what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since these preferences are already satisfied in our environment. This motivates our key insight: when a robot is deployed in an environment that humans act in, the state of the environment is already optimized for what humans want. We can therefore use this implicit preference information from the state to fill in the blanks. We develop an algorithm based on Maximum Causal Entropy IRL and use it to evaluate the idea in a suite of proof-of-concept environments designed to show its properties. We find that information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized. Our code can be found at https://github.com/HumanCompatibleAI/rlsp.

Quando un robot viene schierato in un ambiente in cui gli umani hanno agito, lo stato dell'ambiente è già ottimizzato per ciò che gli umani vogliono e possiamo usare questo fatto per dedurre le preferenze umane.

Gli autori propongono di arricchire la funzione di reward esplicitamente dichiarata di un agente RL con reward/costi ausiliari dedotti dallo stato iniziale e un modello della dinamica dello stato

Questo lavoro propone un modo per dedurre le informazioni implicite nello stato iniziale usando IRL e per combinare la reward dedotta con una reward specificata.


Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.

Categorizzazione sistematica dei metodi di regolarizzazione per il deep learning, rivelando le loro somiglianze.

Tenta di costruire una tassonomia per le tecniche di regolarizzazione impiegate nel deep learning.


Deep neural networks are surprisingly efficient at solving practical tasks,  but the theory behind this phenomenon is only starting to catch up with  the practice. Numerous works show that depth is the key to this efficiency.  A certain class of deep convolutional networks – namely those that correspond  to the Hierarchical Tucker (HT) tensor decomposition – has been  proven to have exponentially higher expressive power than shallow networks.  I.e. a shallow network of exponential width is required to realize  the same score function as computed by the deep architecture. In this paper,  we prove the expressive power theorem (an exponential lower bound on  the width of the equivalent shallow network) for a class of recurrent neural  networks – ones that correspond to the Tensor Train (TT) decomposition.  This means that even processing an image patch by patch with an RNN  can be exponentially more efficient than a (shallow) convolutional network  with one hidden layer. Using theoretical results on the relation between  the tensor decompositions we compare expressive powers of the HT- and  TT-Networks. We also implement the recurrent TT-Networks and provide  numerical evidence of their expressivity.

Dimostriamo l'efficienza esponenziale delle reti neurali di tipo ricorrente rispetto alle reti poco profonde.

Gli autori confrontano la complessità delle reti tensor train con reti strutturate dalla decomposizione CP


Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN). In this paper, we propose a novel probabilistic framework for GANs, ProbGAN, which iteratively learns a distribution over generators with a carefully crafted prior. Learning is efficiently triggered by a tailored stochastic gradient Hamiltonian Monte Carlo with a novel gradient approximation to perform Bayesian inference. Our theoretical analysis further reveals that our treatment is the first probabilistic framework that yields an equilibrium where generator distributions are faithful to the data distribution. Empirical evidence on synthetic high-dimensional multi-modal data and image databases (CIFAR-10, STL-10, and ImageNet) demonstrates the superiority of our method over both start-of-the-art multi-generator GANs and other probabilistic treatment for GANs.

Un nuovo trattamento probabilistico per le GAN con garanzie teoriche.

Questo articolo propone una GAN bayesiana che ha garanzie teoriche di convergenza alla distribuzione reale e pone le verosimiglianze sul generatore e sul discriminatore con logaritmi proporzionali alle funzioni obiettivo delle GAN tradizionali.


In the adversarial-perturbation problem of neural networks, an adversary starts with a neural network model $F$ and a point $\bfx$ that $F$ classifies correctly, and applies a \emph{small perturbation} to  $\bfx$ to produce another point $\bfx'$ that $F$ classifies \emph{incorrectly}.  In this paper, we propose taking into account \emph{the inherent confidence information} produced by models when studying adversarial perturbations, where a natural measure of ``confidence'' is \|F(\bfx)\|_\infty$ (i.e. how confident $F$ is about its prediction?) . Motivated by a thought experiment based on the manifold assumption, we propose a ``goodness property'' of models which states that \emph{confident regions of a good model should be well separated}. We give formalizations of this property and examine existing robust training objectives in view of them. Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence. However, if Madry et al.'s model is indeed a good solution to their objective, then good and bad points are now distinguishable and we can try to embed uncertain points back to the closest confident region to get (hopefully) correct predictions. We thus propose embedding objectives and algorithms, and perform an empirical study using this method. Our experimental results are encouraging: Madry et al.'s model wrapped with our embedding procedure achieves almost perfect success rate in defending against attacks that the base model fails on, while retaining good generalization behavior.

Difesa contro le adversarial perturbation delle reti neurali a partire dall'assunzione del manifold

Il manoscritto propone due funzioni obiettivo basate sull'assunzione del manifold come meccanismi di difesa contro gli adversarial example.

Difendersi dagli adversarial attack basati sull'assunzione del manifold dei dati naturali


Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning view to NAS problem. In specific, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, we impose sparse regularizations to prune useless connections in the architecture. Lastly, we derive an efficient and theoretically sound optimization method to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore can be directly applied to large datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an average test error 2.84%, while on the ImageNet dataset DSO-NAS achieves 25.4% test error under 600M FLOPs with 8 GPUs in 18 hours.

Ricerca dell'architettura neurale single shot tramite ottimizzazione diretta sparsa

Presenta un metodo di ricerca dell'architettura in cui le connessioni vengono rimosse con una regolarizzazione sparsa.

Questo articolo propone Direct Sparse Optimization, che è un metodo per ottenere architetture neurali su problemi specifici ad un costo computazionale ragionevole.

Questo articolo propone un metodo di ricerca dell'architettura neurale basato su un'ottimizzazione sparsa diretta


Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher, into the training of a student network whose weights are quantized to a limited set of levels. The second method,  differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model.   We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to full-precision teacher models, while providing order of magnitude compression, and inference speedup that is linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.

Ottiene una precisione allo stato dell'arte per reti quantizzate e poco profonde sfruttando la distillation.

Propone modelli piccoli e a basso costo combinando distillation e quantizzazione per esperimenti di computer vision e neural machine translation

Questo articolo presenta un framework di utilizzo di un teacher model per aiutare la compressione del modello di deep learning nel contesto della compressione del modello.


Previous work has demonstrated the benefits of incorporating additional linguistic annotations such as syntactic trees into neural machine translation. However the cost of obtaining those syntactic annotations is expensive for many languages and the quality of unsupervised learning linguistic structures is too poor to be helpful. In this work, we aim to improve neural machine translation via source side dependency syntax but without explicit annotation. We propose a set of models that learn to induce dependency trees on the source side and learn to use that information on the target side. Importantly, we also show that our dependency trees capture important syntactic features of language and improve translation quality on two language pairs En-De and En-Ru.

Migliorare la NMT con gli alberi latenti

Questo articolo descrive un metodo per indurre strutture di dipendenza source-side al servizio della traduzione automatica neurale.


Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.

Imparare lavorando a ritroso da una singola dimostrazione, anche una inefficiente, e progressivamente delegare all'agente una parte sempre maggiore della risoluzione.

Questo articolo presenta un metodo per aumentare l'efficienza dei metodi di RL a reward sparsa attraverso un curriculum backward sulle dimostrazioni di esperti.

L'articolo presenta una strategia per risolvere task di reward sparsi con RL campionando gli stati iniziali dalle dimostrazioni.