Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients.The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data. However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization.In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model. We tackle this problem and propose an algorithm for client sided differential privacy preserving federated optimization.The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance. Empirical studies suggest that given a sufficiently large number of participating clients, our proposed procedure can maintain client-level differential privacy at only a minor cost in model performance.

Garantire che i modelli appresi in modo federated non rivelino la partecipazione di un client.


Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks.However, very few works have considered how to interpolate between these no- to high-data regimes.In particular, how can one use the availability of a small amount of data (even 5-25 examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well?In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest.Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization.Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than 1% of the data needed to train a generative model.

Mostriamo come il pre-training di una rete neurale non addestrata con un minimo di 5-25 esempi può migliorare i risultati di ricostruzione nel compressed sensing e nei problemi di semantic recovery come la colorazione.


We propose Cooperative Training (CoT) for training generative models that measure a tractable density for discrete data.CoT coordinately trains a generator G and an auxiliary predictive mediator M. The training target of M is to estimate a mixture density of the learned distribution G and the target distribution P, and that of G is to minimize the Jensen-Shannon divergence estimated through M. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE.This low-variance algorithm is theoretically proved to be superior for both sample generation and likelihood prediction.We also theoretically and empirically show the superiority of CoT over most previous algorithms in terms of generative quality and diversity, predictive generalization ability and computational cost.

Abbiamo proposto Cooperative Training, un nuovo algoritmo di training per la modellazione generativa di dati discreti.


Intrinsic rewards in reinforcement learning provide a powerful algorithmic capability for agents to learn how to interact with their environment in a task-generic way.However, increased incentives for motivation can come at the cost of increased fragility to stochasticity.We introduce a method for computing an intrinsic reward for curiosity using metrics derived from sampling a latent variable model used to estimate dynamics.Ultimately, an estimate of the conditional probability of observed states is used as our intrinsic reward for curiosity.In our experiments, a video game agent uses our model to autonomously learn how to play Atari games using our curiosity reward in combination with extrinsic rewards from the game to achieve improved performance on games with sparse extrinsic rewards.When stochasticity is introduced in the environment, our method still demonstrates improved performance over the baseline.

Introduciamo un metodo per calcolare una reward intrinseca per la curiosità usando metriche derivate dal campionamento di un modello di variabile latente usato per stimare le dinamiche.


Word embedding is a powerful tool in natural language processing.In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase.We give a generative model that can capture specific syntactic relations between words.Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition.The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings.We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.

Presentiamo un modello generativo per word embedding compositivi che cattura le relazioni sintattiche, e forniamo una verifica empirica e una valutazione.


Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities.We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures.When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations.We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects.LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.

Proponiamo un approccio ibrido basato sul modello e senza modello che utilizza informazioni semantiche per migliorare la generalizzazione della DRL in ambienti artificiali.


In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.First, real-world signals can seldom be described as perfectly sparse vectors in a known basis, and traditionally used random measurement schemes are seldom optimal for sensing them.Second, existing signal recovery algorithms are usually not fast enough to make them applicable to real-time problems.In this paper, we address these two challenges by presenting a novel framework based on deep learning.For the first challenge, we cast the problem of finding informative measurements by using a maximum likelihood (ML) formulation and show how we can build a data-driven dimensionality reduction protocol for sensing signals using convolutional architectures.For the second challenge, we discuss and analyze a novel parallelization scheme and show it significantly speeds-up the signal recovery process.We demonstrate the significant improvement our method obtains over competing methods through a series of experiments.

Usiamo tecniche di deep learning per risolvere il problema della rappresentazione e del recupero del segnale sparso.


To select effective actions in complex environments, intelligent agents need to generalize from past experience.World models can represent knowledge about the environment to facilitate such generalization.While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them.We present Dreamer, a reinforcement learning agent that solves long-horizon tasks purely by latent imagination.We efficiently learn behaviors by backpropagating analytic gradients of learned state values through trajectories imagined in the compact state space of a learned world model.On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

Presentiamo Dreamer, un agente che impara comportamenti a lungo orizzonte puramente dall'immaginazione latente usando gradienti con valori analitici.


Transfer reinforcement learning (RL) aims at improving learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments.In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently.To address this problem, the proposed approach, MULTI-source POLicy AggRegation (MULTIPOLAR), comprises two key techniques.We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance.Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy's expressiveness even when some of the source policies perform poorly.We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces.

Proponiamo MULTIPOLAR, un metodo di transfer RL che sfrutta un insieme di source policy raccolte sotto diverse dinamiche ambientali sconosciute per imparare in modo efficiente una target policy in un'altra dinamica.


Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent.However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is such intrinsic reward function which uses prediction error as a reward signal.In this paper:(a) We perform the first large-scale study of purely curiosity-driven learning, i.e. {\em without any extrinsic rewards}, across $54$ standard benchmark environments, including the Atari game suite.Our results show surprisingly good performance as well as a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many games.(b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.).(c) We demonstrate limitations of the prediction-based rewards in stochastic setups.Game-play videos and code are at https://doubleblindsupplementary.github.io/large-curiosity/.

Un agente addestrato solo con curiosity, e nessuna reward estrinseca, fa sorprendentemente bene su 54 ambienti popolari, tra cui la suite di giochi Atari, Mario ecc.


This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).  Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost.  This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant.Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set.

Per le trasformazioni spaziali il minimizzatore robusto minimizza anche la precisione standard; la regolarizzazione che induce invarianza porta a una migliore robustezza rispetto alle architetture specializzate


We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes.To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other.Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably.We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance.Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.

Viene proposta la nozione di order learning e la si applica a problemi di regressione nella computer vision


We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%.The goal is to shed light on two well-known mysteries in deep neural networks:(i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent;(ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well.We performed extensive experiments on persistent homology of a range of point cloud data sets.The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers.No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero.Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers.

Mostriamo che le reti neurali operano cambiando la topologia di un dataset ed esploriamo come le scelte dell'architettura influenzano questo cambiamento.


The convergence rate and final performance of common deep learning models have significantly benefited from recently proposed heuristics such as learning rate schedules, knowledge distillation, skip connections and normalization layers.In the absence of theoretical underpinnings, controlled experiments aimed at explaining the efficacy of these strategies can aid our understanding of deep learning landscapes and the training dynamics.Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations.Instead, we revisit the empirical analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz. mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons why the heuristics succeed.In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and(c) that the latent knowledge shared by the teacher is primarily disbursed in the deeper layers.

Usiamo strumenti empirici di mode connectivity e SVCCA per studiare l'euristica di riavvio del learning rate, di warmup e di knowledge distillation per il training delle reti neurali.


The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs.While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs.To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions.In our experiments, we show that our model achieves state of the art performance on several real world data sets.In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.

Inferenza variazionale per l'inferenza di una distribuzione discreta da cui deriva una rete neurale a bassa precisione


Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs.In recent years, a variety of  graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs.In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework.Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique.Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed.Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.

Proponiamo un nuovo metodo basato su tensori per graph convolutional neural network dinamiche


Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data.Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data.  Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications.However, in the domain of materials science, there is a need to synthesize data with higher order complexity compared to observed samples, and the state-of-the-art cross-domain GANs can not be adapted directly. In this contribution, we propose a novel GAN called CrystalGAN which generates new chemically stable crystallographic structures with increased domain complexity.We introduce an original architecture, we provide the corresponding loss functions, and we show that the CrystalGAN generates very reasonable data.We illustrate the efficiency of the proposed method on a real original problem of novel hydrides discovery that can be further used in development of hydrogen storage materials.

Generazione di nuovi materiali chimici usando nuove GAN cross-domain.


Given samples from a group of related regression tasks, a data-enriched model describes observations by a common and per-group individual parameters.In high-dimensional regime, each parameter has its own structure such as sparsity or group sparsity.In this paper, we consider the general form of data enrichment where data comes in a fixed but arbitrary number of tasks $G$ and any convex function, e.g., norm, can characterize the structure of both common and individual parameters. 	We propose an estimator for the high-dimensional data enriched model and investigate its statistical properties.  We delineate the sample complexity of our estimator and provide high probability non-asymptotic bound for estimation error of all parameters under a condition weaker than the state-of-the-art.We propose an iterative estimation algorithm with a geometric convergence rate.Overall, we present a first through statistical and computational analysis of inference in the data enriched model.

Forniamo uno stimatore e un algoritmo di stima per una classe di problemi di regressione multi-task e forniamo analisi statistiche e computazionali.


Autonomous vehicles are becoming more common in city transportation.  Companies will begin to find a need to teach these vehicles smart city fleet coordination.  Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles.We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning.In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting.  Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them.We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low.The two evaluations presented show that our solution has shown  hat we are successfully able to teach agents cooperation policies while balancing multiple objectives.

Utilizziamo Deep Reinforcement Learning per insegnare agli agenti la coordinazione  della flotta di veicoli per ride-sharing.


Stability is a key aspect of data analysis.In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision.Scattering transforms construct deep convolutional representations which are certified stable to input deformations.This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain. In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps.The resulting representation is stable to metric perturbations of the domain while being able to capture ''high-frequency'' information, akin to the Euclidean Scattering.

Stabilità delle rappresentazioni della trasformata di scattering dei dati del grafo alle deformazioni del supporto del grafo sottostante.


We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks.DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them.We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters.

Proponiamo una nuova architettura di deep network che può decidere dinamicamente la sua capacità di rete mentre si allena su uno scenario di lifelong learning.


This paper fosters the idea that deep learning methods can be sided to classicalvisual odometry pipelines to improve their accuracy and to produce uncertaintymodels to their estimations.We show that the biases inherent to the visual odom-etry process can be faithfully learnt and compensated for, and that a learning ar-chitecture associated to a probabilistic loss function can jointly estimate a fullcovariance matrix of the residual errors, defining a heteroscedastic error model.Experiments on autonomous driving image sequences and micro aerial vehiclescamera acquisitions assess the possibility to concurrently improve visual odome-try and estimate an error associated to its outputs.

Questo articolo discute diversi metodi di accoppiamento del VO con deep learning e propone una predizione simultanea di correzioni e incertezze.


Building robust online content recommendation systems requires learning com- plex interactions between user preferences and content features.The field has evolved rapidly in recent years from traditional multi-arm bandit and collabora- tive filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions.Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.In this paper we provide a novel method, Deep Density Networks (DDN) which deconvolves measurement and data uncertainty and predicts probability densities of CTR, enabling us to perform more efficient exploration of the feature space.We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and present online and offline results to eval- uate the benefit of using DDN.

Abbiamo introdotto Deep Density Network, un modello DNN unificato per stimare l'incertezza per exploration/exploitation nei sistemi di raccomandazione.


While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences.On the sub-population of Twitter users, we examine whether climate change sentiment changes in response to five separate natural disasters occurring in the U.S. in 2018.We begin by showing that tweets can be classified with over 75% accuracy as either accepting or denying climate change when using our methodology to compensate for limited labelled data; results are robust across several machine learning models and yield geographic-level results in line with prior research.We then apply RNNs to conduct a cohort-level analysis showing that the 2018 hurricanes yielded a statistically significant increase in average tweet sentiment affirming climate change.However, this effect does not hold for the 2018 blizzard and wildfires studied, implying that Twitter users' opinions on climate change are fairly ingrained on this subset of natural disasters.

Addestriamo RNN su utenti famosi di Twitter per determinare se la popolazione generale di Twitter è più propensa a credere nel cambiamento climatico dopo un disastro naturale.


We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state.Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program.This approach eliminates the need to solve the non-convex problem of explicit identification of the system and its latent state, and allows for provable optimality guarantees for the control signal.We give the first efficient algorithm for finding the optimal control signal with an arbitrary time horizon T, with sample complexity (number of training rollouts) polynomial only in log(T) and other relevant parameters.

Usando una nuova rappresentazione dei sistemi dinamici lineari simmetrici con uno stato latente, formuliamo il controllo ottimale come un programma convesso, fornendo il primo algoritmo in tempo polinomiale che risolve il controllo ottimale con una sample complexity solo polilogaritmica nell'orizzonte temporale.


Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures.In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators.Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors.We introduce two tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks that only employ linear/convolutional blocks.We exhibit for the first time that by using our approach a GAN generator can approximate the data distribution without using any activation functions.Thorough experimental evaluation on both synthetic and real data (images and 3D point clouds) demonstrates the merits of PolyGAN against the state of the art.

Modelliamo il generatore di dati (in GAN) per mezzo di un polinomio di alto ordine rappresentato da tensori di alto ordine.


Deep neural networks trained on large supervised datasets have led to impressive results in recent years.However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained.In this paper, we investigate the behavior of deep neural networks on training sets with massively noisy labels.We show on multiple datasets such as MINST, CIFAR-10 and ImageNet that successful learning is possible even with an essentially arbitrary amount of noise.For example, on MNIST we find that accuracy of above 90 percent is still attainable even when the dataset has been diluted with 100 noisy examples for each clean example.Such behavior holds across multiple patterns of label noise, even when noisy labels are biased towards confusing classes.Further, we show how the required dataset size for successful training increases with higher label noise.Finally, we present simple actionable techniques for improving learning in the regime of high label noise.

Mostriamo che le deep neural network sono in grado di imparare da dati che sono stati diluiti con una quantità arbitraria di rumore.


In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for low resource neural machine translation (NMT).We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks.We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages.We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr, and Ko) as target tasks.We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples.For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (~600 parallel sentences).

proponiamo un approccio di meta-learning per la traduzione automatica neurale low-resource che può rapidamente imparare a tradurre in una nuova lingua


This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection.We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection.We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results.To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.

Un metodo per il rilevamento attivo delle anomalie. Presentiamo un nuovo layer che può essere integrato in qualsiasi modello di deep learning progettato per il rilevamento delle anomalie unsupervised per trasformarlo in un metodo attivo.


In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks.To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions.We generate these examples through interpolations in latent space.We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature space via latent code interpolations.We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision.Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.

Generiamo degli esempi per spiegare una designazione del classificatore tramite interpolazioni nello spazio latente. La funzione di costo del variational autoencoder è estesa con un funzionale del classificatore sul percorso dell'esempio generato nello spazio dei dati.


The soundness and optimality of a plan depends on the correctness of the domain model.In real-world applications, specifying complete domain models is difficult as the interactions between the agent and its environment can be quite complex.We propose a framework to learn a PPDDL representation of the model incrementally over multiple planning problems using only experiences from the current planning problem, which suits non-stationary environments.We introduce the novel concept of reliability as an intrinsic motivation for reinforcement learning, and as a means of learning from failure to prevent repeated instances of similar failures.Our motivation is to improve both learning efficiency and goal-directedness.We evaluate our work with experimental results for three planning domains.

Introdurre un approccio per permettere agli agenti di imparare i modelli di azione PPDDL in modo incrementale su più problemi di pianificazione nel framework del reinforcement learning.


The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms.  Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks.In this paper, we seek to understand the primary contribution  of the entropy term to the performance of maximum entropy algorithms.For the Mujoco benchmark, we demonstrate that the entropy term in Soft Actor Critic (SAC) principally addresses the bounded nature of the action spaces.With this insight, we propose a simple normalization scheme which allows a streamlined algorithm without entropy maximization match the performance of SAC.Our experimental results demonstrate a need to revisit the benefits of entropy regularization in DRL.We also propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training.  We further show that the streamlined algorithm with the simple non-uniform sampling scheme outperforms SAC and achieves state-of-the-art performance on challenging continuous control tasks.

Proponiamo un nuovo algoritmo DRL off-policy che raggiunge prestazioni allo stato dell'arte.


Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers.Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered.The above observations raise the problem of evidence aggregation from multiple passages.In this paper, we deal with this problem as answer re-ranking.Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question.Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8\% improvement on the former two datasets.

Proponiamo un metodo che può utilizzare le informazioni di passaggi multipli per QA open-domain.


Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.Furthermore, social networks can be extracted from email corpora, tweets, or social media. When it comes to visualising these large corpora, either the textual content or the network graph are used.In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Algoritmo di riduzione della dimensionalità per visualizzare il testo con informazioni di rete, per esempio un corpus di email o co-authorship.


Machine learned models exhibit bias, often because the datasets used to train them are biased.This presents a serious problem for the deployment of such technology, as the resulting models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them.We propose to use high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers.We present a framework that leverages Bayesian parameter search to efficiently characterize the high dimensional feature space and more quickly identify weakness in performance.We apply our approach to an example domain, face detection, and show that it can be used to help identify demographic biases in commercial face application programming interfaces (APIs).

Presentiamo un framework che sfrutta simulazioni al computer ad alta fedeltà per interrogare e diagnosticare i bias all'interno dei classificatori ML.


Point clouds are a flexible and ubiquitous way to represent 3D objects with arbitrary resolution and precision.Previous work has shown that adapting encoder networks to match the semantics of their input point clouds can significantly improve their effectiveness over naive feedforward alternatives.However, the vast majority of work on point-cloud decoders are still based on fully-connected networks that map shape representations to a fixed number of output points.In this work, we investigate decoder architectures that more closely match the semantics of variable sized point clouds.Specifically, we study sample-based point-cloud decoders that map a shape representation to a point feature distribution, allowing an arbitrary number of sampled features to be transformed into individual output points.We develop three sample-based decoder architectures and compare their performance to each other and show their improved effectiveness over feedforward architectures.In addition, we investigate the learned distributions to gain insight into the output transformation.Our work is available as an extensible software platform to reproduce these results and serve as a baseline for future work.

Presentiamo e valutiamo i decodificatori di point cloud basati sul campionamento che superano la baseline MLP, adattandosi meglio alla semantica delle nuvole di punti.


We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training.This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours.We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage.In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.

Usiamo il deep RL per imparare una policy che dirige la ricerca di un algoritmo genetico per ottimizzare meglio il costo di esecuzione dei computation graph, e mostriamo risultati migliori sui grafi TensorFlow del mondo reale.


Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint?Humans excel at this task.Our ability to imagine and fill in missing visual information is tightly coupled with perception: we feel as if we see  the world in 3 dimensions, while in fact, information from only the front surface of the world hits our (2D) retinas.This paper explores the connection between view-predictive representation learning and its role in the development of 3D visual recognition.We propose inverse graphics networks, which take as input 2.5D video streams captured by a moving camera, and map to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera.The model can also project its 3D feature maps to novel viewpoints, to predict and match against target views.We propose contrastive prediction losses that can handle stochasticity of the visual input and can scale view-predictive learning to more photorealistic scenes than those considered in previous works.We show that the proposed  model learns 3D visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating  motion of the inferred 3D feature maps in videos of dynamic scenes.To the best of our knowledge, this is the first work that empirically shows view prediction to be a useful and scalable self-supervised task beneficial to 3D object detection.

Mostriamo che con la giusta loss e architettura, l'apprendimento view-predictive migliora il rilevamento degli oggetti 3D


The modeling of style when synthesizing natural human speech from text has been the focus of significant attention.Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud.The synthesized audio waveform is expected to contain the verbal content of x_txt and the auditory style of x_aud.Unfortunately, modeling style in TTS is somewhat under-determined and training models with a reconstruction loss alone is insufficient to disentangle content and style from other factors of variation.In this work, we introduce an end-to-end TTS model that offers enhanced content-style disentanglement ability and controllability.We achieve this by combining a pairwise training procedure, an adversarial game, and a collaborative game into one training scheme.The adversarial game concentrates the true data distribution, and the collaborative game minimizes the distance between real samples and generated samples in both the original space and the latent space.As a result, the proposed model delivers a highly controllable generator, and a disentangled representation.Benefiting from the separate modeling of style and content, our model can generate human fidelity speech that satisfies the desired style conditions.Our model achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker's voice).

Una generative adversarial network per la modellazione dello stile in un sistema text-to-speech


Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization.However, there is currently no theoretical analysis that explains this observation.In this work, we study a simplified learning task with over-parameterized convolutional networks that empirically exhibits the same qualitative phenomenon.  For this setting, we provide a theoretical analysis of the optimization and generalization performance of gradient descent.Specifically, we prove data-dependent sample complexity bounds which show that over-parameterization improves the generalization performance of gradient descent.

Mostriamo in un task di apprendimento semplificato che l'iperparametrizzazione migliora la generalizzazione di una convnet addestrata con la gradient descent.


We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a model prior distribution of interest.Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot meta-learning setting to upper-bound generalisation errors on unseen tasks.We  also propose a generative-based approach to model the shared prior and task-specific posterior more expressively compared to the usual diagonal Gaussian assumption.We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on mini-ImageNet benchmark, and competitive results in a multi-modal task-distribution regression.

Il meta-learning bayesiano utilizzando la frameworkPAC-Bayes e le distribuzioni a priori implicite


As the area of Explainable AI (XAI), and Explainable AI Planning (XAIP), matures, the ability for agents to generate and curate explanations will likewise grow.We propose a new challenge area in the form of rebellious and deceptive explanations.We discuss how these explanations might be generated and then briefly discuss evaluation criteria.

Position paper che propone spiegazioni ribelli e ingannevoli per gli agenti.


We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data.When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model.We call our model the latent tree variational autoencoder (LTVAE).Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable.This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.

Studiamo una variante degli autoencoder variazionali in cui c'è una sovrastruttura di variabili latenti discrete in cima alle feature latenti.


Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals.Popular deep reinforcement learning approaches in this direction involve learning goal-conditioned policies or value functions, or Inverse Dynamics Models (IDMs).IDMs map an agent’s current state and desired goal to the required actions.We show that the key to achieving good performance with IDMs lies in learning the information shared between equivalent experiences, so that they can be generalized to unseen scenarios.We design a training process that guides the learning of latent representations to encode this shared information.Using a limited number of environment interactions, our agent is able to efficiently navigate to arbitrary points in the goal space.We demonstrate the effectiveness of our approach in high-dimensional locomotion environments such as the Mujoco Ant, PyBullet Humanoid, and PyBullet Minitaur.We provide quantitative and qualitative results to show that our method clearly outperforms competing baseline approaches.

Mostriamo che la chiave per ottenere buone prestazioni con gli IDM sta nell'apprendimento di rappresentazioni latenti per codificare le informazioni condivise tra esperienze equivalenti, in modo che possano essere generalizzate a scenari non visti.


In this paper, we first identify \textit{angle bias}, a simple but remarkable phenomenon that causes the vanishing gradient problem in a multilayer perceptron (MLP) with sigmoid activation functions.We then propose \textit{linearly constrained weights (LCW)} to reduce the angle bias in a neural network, so as to train the network under the constraints that the sum of the elements of each weight vector is zero.A reparameterization technique is presented to efficiently train a model with LCW by embedding the constraints on weight vectors into the structure of the network.Interestingly, batch normalization (Ioffe & Szegedy, 2015) can be viewed as a mechanism to correct angle bias.Preliminary experiments show that LCW helps train a 100-layered MLP more efficiently than does batch normalization.

Identifichiamo il bias dell'angolo che causa il problema del vanishing gradient nelle deep network e proponiamo un metodo efficiente per ridurre il bias.


Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems.However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult.In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems.Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task.In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model.Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

Impieghiamo graph neural network nel framework dell'EM variazionale per un'inferenza e un apprendimento efficienti delle reti logiche di Markov.


Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance.However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments.In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions.Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.

Un approccio di meta-reinforcement learning che incorpora un controller di rete neurale applicato alla guida autonoma con il simulatore Carla.


The information bottleneck principle is an elegant and useful approach to representation learning.In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms.We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound.Then, we maximize this lower bound with the Stein variational (SV) gradient method. We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO).Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly.Finally, we study the information-bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE).We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process.We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.

Derivare un framework dell'information bottleneck nel reinforcement learning e alcune semplici teorie e strumenti pertinenti.


A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly.Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities.We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework.We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment.We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance.Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previously-seen tasks to substantially improve their own learning efficiency.

Proponiamo un approccio a moduli neurali per il continual learning utilizzando un ambiente visivo unificato con un grande spazio d'azione.


Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine.In this paper, we present a semi-supervised technique that addresses both these issues simultaneously.We learn dense representations from large unlabelled image datasets, then use those representations to both learn classifiers from small labeled sets and generate visual rationales explaining the predictions.Using chest radiography diagnosis as a motivating application, we show our method has good generalization ability by learning to represent our chest radiography dataset while training a classifier on an separate set from a different institution.Our method identifies heart failure and other thoracic diseases.For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space.Decoding the resultant latent representation produces an image without apparent disease.The difference between the original and the altered image forms an interpretable visual rationale for the algorithm's prediction.Our method simultaneously produces visual rationales that compare favourably to previous techniques and a classifier that outperforms the current state-of-the-art.

Proponiamo un metodo per utilizzare le GAN per generare spegazioni visive di alta qualità per aiutare a spiegare le previsioni del modello.


Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions.This paper investigates the potential benefits of using highly flexible search distributions in ES algorithms, in contrast to standard ones (typically Gaussians).We model such distributions with Generative Neural Networks (GNNs) and introduce a new ES algorithm that leverages their expressiveness to accelerate the stochastic search.Because it acts as a plug-in, our approach allows to augment virtually any standard ES algorithm with flexible search distributions.We demonstrate the empirical advantages of this method on a diversity of objective functions.

Proponiamo un nuovo algoritmo che sfrutta l'espressività delle reti neurali generative per migliorare gli algoritmi delle strategie evolutive.


We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR).For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications.Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models.For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library.Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.

Comprimiamo e velocizziamo i modelli di riconoscimento vocale sui dispositivi embedded attraverso una tecnica di regolarizzazione di trace norm e kernel ottimizzati.


Training activation quantized neural networks involves minimizing a piecewise constant training loss whose gradient vanishes almost everywhere, which is undesirable for the standard back-propagation or chain rule.An empirical way around this issue is to use a straight-through estimator (STE) (Bengio et al., 2013) in the backward pass only, so that the "gradient" through the modified chain rule becomes non-trivial.Since this unusual "gradient" is certainly not the gradient of loss function, the following question arises: why searching in its negative direction minimizes the training loss?In this paper, we provide the theoretical justification of the concept of STE by answering this question.We consider the problem of learning a two-linear-layer network with binarized ReLU activation and Gaussian input data.We shall refer to the unusual "gradient" given by the STE-modifed chain rule as coarse gradient.The choice of STE is not unique.We prove that if the STE is properly chosen, the expected coarse gradient correlates positively with the population gradient (not available for the training), and its negation is a descent direction for minimizing the population loss.We further show the associated coarse gradient descent algorithm converges to a critical point of the population loss minimization problem.  Moreover, we show that a poor choice of STE leads to instability of the training algorithm near certain local minima, which is verified with CIFAR-10 experiments.

Forniamo una giustificazione teorica per il concetto di stimatore straight-through.


This paper presents GumbelClip, a set of modifications to the actor-critic algorithm, for off-policy reinforcement learning.GumbelClip uses the concepts of truncated importance sampling along with additive noise to produce a loss function enabling the use of off-policy samples.The modified algorithm achieves an increase in convergence speed and sample efficiency compared to on-policy algorithms and is competitive with existing off-policy policy gradient methods while being significantly simpler to implement.The effectiveness of GumbelClip is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.

Con una serie di modifiche, sotto 10 LOC, ad A2C si ottiene un actor-critic off-policy che supera A2C e si comporta in modo simile ad ACER. Le modifiche sono grandi batch size, un clamping aggressivo, e una "forzatura" della policy con rumore di Gumbel.


In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs).GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer.In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data.Attempts have been made for utilizing GANs with word embeddings for text generation.This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures.The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

Generazione di testo usando sentence embedding dai vettori di Skip-Thought con l'aiuto delle Generative Adversarial Network.


Autoregressive recurrent neural decoders that generate sequences of tokens one-by-one and left-to-right are the workhorse of modern machine translation.In this work, we propose a new decoder architecture that can generate natural language sequences in an arbitrary order.Along with generating tokens from a given vocabulary, our model additionally learns to select the optimal position for each produced token.The proposed decoder architecture is fully compatible with the seq2seq framework and can be used as a drop-in replacement of any classical decoder.We demonstrate the performance of our new decoder on the IWSLT machine translation task as well as inspect and interpret the learned decoding patterns by analyzing how the model selects new positions for each subsequent token.

Nuovo decoder out-of-order per la traduzione automatica neurale


In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem.Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph.To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting.We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models.Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy.Our DIAMNet can further improve existing representation learning models for this more global problem.

In questo articolo, studiamo un nuovo problema di graph learning: imparare a contare gli isomorfismi dei sottografi.


Domain adaptation is an open problem in deep reinforcement learning (RL).Often, agents are asked to perform in environments where data is difficult to obtain.In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment.The gap between visual observations of the source and target environments often causes the agent to fail in the target environment.We present a new RL agent, SADALA (Soft Attention DisentAngled representation Learning Agent).SADALA first learns a compressed state representation.It then jointly learns to ignore distracting features and solve the task presented.SADALA's separation of important and unimportant visual features leads to robust domain transfer.SADALA outperforms both prior disentangled-representation based RL and domain randomization approaches across RL environments (Visual Cartpole and DeepMind Lab).

Presentiamo un agente che utilizza un beta-vae per estrarre le feature visive e un meccanismo di attention per ignorare le feature irrilevanti dalle osservazioni visive per consentire un transfer robusto tra domini visivi.


Robustness verification that aims to formally certify the prediction behavior of  neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees.However, previous methods are usually limited to relatively simple neural networks.In this paper, we consider the robustness verification problem for Transformers.Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous work.We resolve these challenges and develop the first verification algorithm for Transformers.The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation.These bounds also shed light on interpreting Transformers as they consistently reflect the importance of words in sentiment analysis.

Proponiamo il primo algoritmo per verificare la robustezza dei trasformer.


In the last few years, deep learning has been tremendously successful in many applications.However, our theoretical understanding of deep learning, and thus the ability of providing principled improvements, seems to lag behind.A theoretical puzzle concerns the ability of deep networks to predict well despite their intriguing apparent lack of generalization: their classification accuracy on the training set is not a proxy for their performance on a test set.How is it possible that training performance is independent of testing performance?Do indeed deep networks require a drastically new theory of generalization?Or are there measurements based on the training data that are predictive of the network performance on future data?Here we show that when performance is measured appropriately, the training performance is in fact predictive of expected performance, consistently with classical machine learning theory.

Contrariamente alle credenze precedenti, le prestazioni di allenamento delle deep network, se misurate in modo appropriato, sono predittive delle prestazioni di test, in linea con la teoria classica dell'apprendimento automatico.


We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world.Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration.We study how local trajectory optimization can cope with approximation errors  in the value function, and can stabilize and accelerate value function learning.Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions.Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation.This exploration is critical for fast and stable learning of the value function.Combining these components enable solutions to complex control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.

Proponiamo un framework che incorpora la pianificazione per un'esplorazione e un apprendimento efficienti in ambienti complessi.


Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance.While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures' implicit regularization properties? In this work, we investigate the skip connection's effect on network's generalization features.Through experiments, we show that certain neural network architectures contribute to their generalization abilities.Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with 'skip connections'.We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.

Il nostro articolo analizza l'enorme potere di rappresentazione delle reti, specialmente con le "skip connection", che possono essere utilizzate come metodo per una migliore generalizzazione.


One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse.When the generator has too much capacity, it is prone to ignoring latent code.This problem is exacerbated when the dataset is small, and the latent dimension is high.The root of the problem is the ELBO objective, specifically the Kullback–Leibler (KL) divergence term in objective function.This paper proposes a new  objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective.It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space.A probabilistic autoencoder model, named $\mu$-VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and $\beta$-VAE objective.The $\mu$-VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality.Latent representations learned by $\mu$-VAE are shown to be good and can be used for downstream tasks such as classification.

Questo articolo propone una nuova funzione obiettivo per sostituire il termine KL con uno che emula l'obiettivo della massima discrepanza media (MMD).


Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data.In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods.We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison.We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.

Poniamo che le verosimiglianze dei modelli generativi siano eccessivamente influenzate dalla complessità dell'input, e proponiamo un modo per compensarle quando si individuano input out-of-distribution


Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients.In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings).We show that one cause for such failures is the exponential moving average used in the algorithms.We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm.Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with ``long-term memory'' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance.

Studiamo la convergenza di algoritmi di ottimizzazione popolari come Adam, RMSProp e proponiamo nuove varianti di questi metodi che convergono in modo dimostrabile alla soluzione ottimale in ambienti convessi.


Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy), no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases.In this work, we propose a set of simple, yet highly-effective defenses against these attacks. We test our proposed approach against two recently published clean-label poisoning attacks, both of which use the CIFAR-10 dataset.After reproducing their experiments, we demonstrate that our defenses are able to detect over 99% of poisoning examples in both attacks and remove them without any compromise on model performance.Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks.

Presentiamo difese efficaci contro gli attacchi di clean-label poisoning.


Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems.In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks.MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks.MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels.MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates.We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM).For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%.  For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin.

MXGNet è un'architettura multilayer e multiplex basata su grafi che raggiunge buone prestazioni su vari task di ragionamento diagrammatico.


Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types.Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge discovery.However, they are challenged by the diverse table structures and the spatial-correlated semantics on cell grids.To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction.First, we propose a multi-task framework that learns table region, structural components and cell types jointly; second, we leverage the advances of the recent language model to capture semantics in each cell value; third, we build a large human-labeled dataset with broad coverage of table structures.Our evaluation shows that our proposed multi-task framework is highly effective that outperforms the results of training each task separately.

Proponiamo un nuovo framework multi-task che impara il rilevamento delle tabelle, il riconoscimento dei componenti semantici e la classificazione del tipo di cella per le tabelle dei fogli di calcolo, mostrando risultati promettenti.


Open-domain dialogue generation has gained increasing attention in Natural Language Processing.Comparing these methods requires a holistic means of dialogue evaluation.Human ratings are deemed as the gold standard.As human evaluation is inefficient and costly, an automated substitute is desirable.In this paper, we propose holistic evaluation metrics which capture both the quality and diversity of dialogues.Our metrics consists of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, and, (3) $n$-gram based diversity in responses to augmented queries.The empirical validity of our metrics is demonstrated by strong correlation with human judgments.We provide the associated code, datasets and human ratings.

Proponiamo metriche automatiche per valutare olisticamente la generazione open-dialogue che risultano fortemente correlate con la valutazione umana.


Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.Recently, there has been an increasing interest in extending CNNs to the general spatial domain.Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood.In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation.Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.

Abbiamo ideato un nuovo Depthwise Separable Graph Convolution (DSGC) per i dati generici del dominio spaziale, che è altamente compatibile con la convoluzione separabile depthwise.


Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments.Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude (~0.1 ms to ~100 s), a process we call Wave2Midi2Wave.This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms.The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music.

Addestriamo una suite di modelli in grado di trascrivere, comporre e sintetizzare forme d'onda audio con una struttura musicale coerente, grazie al nuovo dataset MAESTRO.


Variational inference based on chi-square divergence minimization (CHIVI) provides a way to approximate a model's posterior while obtaining an upper bound on the marginal likelihood.However, in practice CHIVI relies on Monte Carlo (MC) estimates of an upper bound objective that at modest sample sizes are not guaranteed to be true bounds on the marginal likelihood.This paper provides an empirical study of CHIVI performance on a series of synthetic inference tasks.We show that CHIVI is far more sensitive to initialization than classic VI based on KL minimization, often needs a very large number of samples (over a million), and may not be a reliable upper bound.We also suggest possible ways to detect and alleviate some of these pathologies, including diagnostic bounds and initialization strategies.

Uno studio empirico dell'inferenza variazionale basata sulla minimizzazione della divergenza del chi-quadro, mostrando che minimizzare il CUBO è più complicato che massimizzare l'ELBO


It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples.Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial perturbations, which introduces the globally linear behavior in-between training examples.However, in previous work, the mixup-trained models only passively defend adversarial attacks in inference by directly classifying the inputs, where the induced global linearity is not well exploited.Namely, since the locality of the adversarial perturbations, it would be more efficient to actively break the locality via the globality of the model predictions.Inspired by simple geometric intuition, we develop an inference principle, named mixup inference (MI), for mixup-trained models.MI mixups the input with other random clean samples, which can shrink and transfer the equivalent perturbation if the input is adversarial.Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants.

Sfruttiamo la linearità globale dei modelli addestrati con mixup nell'inferenza per rompere la località delle perturbazioni adversarial.


Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text.In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain.Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature.As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.

Il fine-tuning di BERT sui corpora legali fornisce miglioramenti marginali, ma preziosi, nei task di NLP nel dominio legale.


We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques.Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus.By hypothesizing a parallel latent sequence that generates each observed sequence, our model learns to transform sequences from one domain to another in a completely unsupervised fashion.In contrast with traditional generative sequence models (e.g. the HMM), our model makes few assumptions about the data it generates: it uses a recurrent language model as a prior and an encoder-decoder as a transduction distribution.While computation of marginal data likelihood is intractable in this model class, we show that amortized variational inference admits a practical surrogate.Further, by drawing connections between our variational objective and other recent unsupervised style transfer and machine translation techniques, we show how our probabilistic view can unify some known non-generative objectives such as backtranslation and adversarial loss.Finally, we demonstrate the effectiveness of our method on a wide range of unsupervised style transfer tasks, including sentiment transfer, formality transfer, word decipherment, author imitation, and related language translation.Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.Further, we conduct experiments on a standard unsupervised machine translation task and find that our unified approach matches the current state-of-the-art.

Formuliamo un modello probabilistico di sequenza latente per affrontare il transfer unsupervised dello stile del testo e mostriamo la sua efficacia in una serie di task di transfer unsupervised dello stile del testo.


Current practice in machine learning is to employ deep nets in an overparametrized limit, with the nominal number of parameters typically exceeding the number of measurements.This resembles the situation in compressed sensing, or in sparse regression with $l_1$ penalty terms, and provides a theoretical avenue for understanding phenomena that arise in the context of deep nets.One such phenonemon is the success of deep nets in providing good generalization in an interpolating regime with zero training error.Traditional statistical practice calls for regularization or smoothing to prevent "overfitting" (poor generalization performance).However, recent work shows that there exist data interpolation procedures which are statistically consistent and provide good generalization performance\cite{belkin2018overfitting} ("perfect fitting").In this context, it has been suggested that "classical" and "modern" regimes for machine learning are separated by a peak in the generalization error ("risk") curve, a phenomenon dubbed "double descent"\cite{belkin2019reconciling}.While such overfitting peaks do exist and arise from ill-conditioned design matrices, here we challenge the interpretation of the overfitting peak as demarcating the regime where good generalization occurs under overparametrization. We propose a model of Misparamatrized Sparse Regression (MiSpaR) and analytically compute the GE curves for $l_2$ and $l_1$ penalties.We show that the overfitting peak arising in the interpolation limit is dissociated from the regime of good generalization.The analytical expressions are obtained in the so called "thermodynamic" limit.We find an additional interesting phenomenon: increasing overparametrization in the fitting model increases sparsity, which should intuitively improve performance of $l_1$ penalized regression.However, at the same time, the relative number of measurements decrease compared to the number of fitting parameters, and eventually overparametrization does lead to poor generalization.Nevertheless, $l_1$ penalized regression can show good generalization performance under conditions of data interpolation even with a large amount of overparametrization.These results provide a theoretical avenue into studying inverse problems in the interpolating regime using overparametrized fitting functions such as deep nets.

Propone un modello analiticamente trattabile e una procedura di inferenza (regressione sparsa misparametrizzata, dedotta usando la penalità L_1 e studiata nel limite di interpolazione dei dati) per studiare i fenomeni legati alle deep network nel contesto dei problemi inversi.


Hashing-based collaborative filtering learns binary vector representations (hash codes) of users and items, such that recommendations can be computed very efficiently using the Hamming distance, which is simply the sum of differing bits between two hash codes.A problem with hashing-based collaborative filtering using the Hamming distance, is that each bit is equally weighted in the distance computation, but in practice some bits might encode more important properties than other bits, where the importance depends on the user. To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent.This allows a binary user-level importance weighting of each item without the need to store additional weights for each user.We experimentally evaluate our approach against state-of-the-art baselines on 4 datasets, and obtain significant gains of up to 12% in NDCG.We also make available an efficient implementation of self-masking, which experimentally yields <4% runtime overhead compared to the standard Hamming distance.

Proponiamo un nuovo approccio variazionale di filtraggio collaborativo basato sull'hashing, ottimizzato per una nuova variante self-mask della distanza di Hamming, che supera lo stato dell'arte fino al 12% in termini di NDCG.


Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search.This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit that achieves performance equivalent to that of best fixed batch-size.At each epoch, the RMGD samples a batch size according to a certain probability distribution proportional to a batch being successful in reducing the loss function.Sampling from this probability provides a mechanism for exploring different batch size and exploiting batch sizes with history of success.  After obtaining the validation loss at each epoch with the sampled batch size, the probability distribution is updated to incorporate the effectiveness of the sampled batch size.Experimental results show that the RMGD achieves performance better than the best performing single batch size.It is surprising that the RMGD achieves better performance than grid search.Furthermore, it attains this performance in a shorter amount of time than grid search.

Un algoritmo di ottimizzazione che esplora diverse batch size in base alla probabilità e sfrutta automaticamente la batch size di successo che minimizza la validation loss.


Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors.Several attempts have been made to complete knowledge graph as well as to detect noise.However, none of them considers unifying these two tasks even though they are inter-dependent and can mutually boost the performance of each other.In this paper, we proposed to jointly combine these two tasks with a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding.Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms both in regard to knowledge graph completion and error detection.

Abbiamo proposto una struttura unificata di Generative Adversarial Network (GAN) per imparare embedding di knowledge graph consapevoli del rumore.


Energy-based models (EBMs), a.k.a.un-normalized models, have had recent successes in continuous spaces.However, they have not been successfully applied to model text sequences.  While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult.   In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete.  Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models.  The EBM then worksin the {\em residual} of the language model; and is trained to discriminate real text from text generated by the auto-regressive models.We  investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications.  We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives.Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives.However, EBMs exhibit more sensitivity to the training set used by such generators.

Un EBM residuo per il testo la cui formulazione è equivalente alla discriminazione tra testo umano e testo generato automaticamente. Studiamo il suo comportamento di generalizzazione.


Semi-supervised learning, i.e. jointly learning from labeled an unlabeled samples, is an active research topic due to its key role on relaxing human annotation constraints.In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different  perturbations of unlabeled samples.We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions.We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it.The proposed approach achieves state-of-the-art results in CIFAR-10/100 and Mini-ImageNet despite being much simpler than other state-of-the-art.These results demonstrate that pseudo-labeling can outperform consistency regularization methods, while the opposite was supposed in previous work.Code will be made available.

Lo pseudo-labeling ha dimostrato di essere un'alternativa debole per l'apprendimento semi-supervised. Noi, al contrario, dimostriamo che la gestione dei confirmation bias con diverse regolarizzazioni rende lo pseudo-labeling un approccio adatto.


Model-free reinforcement learning (RL) has been proven to be a powerful, general tool for learning complex behaviors.However, its sample efficiency is often impractically large for solving challenging real-world problems, even for off-policy algorithms such as Q-learning.A limiting factor in classic model-free RL is that the learning signal consists only of scalar rewards, ignoring much of the rich information contained in state transition tuples.Model-based RL uses this information, by training a predictive model, but often does not achieve the same asymptotic performance as model-free RL due to model bias.We introduce temporal difference models (TDMs), a family of goal-conditioned value functions that can be trained with model-free learning and used for model-based control.TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods.Our experimental results show that, on a range of continuous control tasks, TDMs provide a substantial improvement in efficiency compared to state-of-the-art model-based and model-free methods.

Mostriamo che una speciale funzione di valore goal-conditioned addestrata con metodi senza modello può essere usata all'interno del controllo basato sul modello, ottenendo una sample efficiency e prestazioni sostanzialmente migliori.


We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects.The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions defined over sets.Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches.We illustrate the method in sets of 2D points and MNIST images.

Una nuova architettura neurale per un'efficiente inferenza ammortizzata su permutazioni latenti


Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance.However, collecting users' explicit feedback is costly.In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval systems.Specifically, we adopt a two-tower neural net architecture to model query-item relevance given both collaborative and content information.By introducing auxiliary tasks trained with much richer implicit user feedback data, we improve the quality and resolution for the learned representations of queries and items.Applying these learned representations to an industrial retrieval system has delivered significant improvements.

Proponiamo una nuova architettura per un modello a due torri shared-bottom per trasferire la conoscenza da ricchi feedback impliciti per prevedere la rilevanza per sistemi di retrieval su larga scala.