Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artificial image features.However, learning a shared cross-modal embedding by aligning the latent spaces of modality-specific autoencoders is shown to be promising in (generalized) zero-shot learning.While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier.We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shot as well as on few-shot learning.Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings.

Usiamo i VAE per imparare un embedding condiviso dello spazio latente tra le feature e gli attributi dell'immagine e quindi raggiungere risultati allo stato dell'arte nell'apprendimento generalizzato zero-shot.


Intuitively, image classification should profit from using spatial information.Recent work, however, suggests that this might be overrated in standard CNNs.In this paper, we are pushing the envelope and aim to further investigate the reliance on and necessity of spatial information.We propose and analyze three methods, namely Shuffle Conv, GAP+FC and 1x1 Conv, that destroy spatial information during both training and testing phases.We extensively evaluate these methods on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152, MobileNet, SqueezeNet).Interestingly, we consistently observe that spatial information can be completely deleted from a significant number of layers with no or only small performance drops.

Le informazioni spaziali negli ultimi layer non sono necessarie per una buona precisione di classificazione.


Disentangling underlying generative factors of a data distribution is important for interpretability and generalizable representations.In this paper,  we introduce two novel disentangling methods.Our first method, Unlabeled Disentangling GAN (UD-GAN, unsupervised), decomposes the latent noise by generating similar/dissimilar image pairs and it learns a distance metric on these pairs with siamese networks and a contrastive loss.This pairwise approach provides consistent representations for similar data points.Our second method (UD-GAN-G, weakly supervised) modifies the UD-GAN with user-defined guidance functions, which restrict the information that goes into the siamese networks.This constraint helps UD-GAN-G to focus on the desired semantic variations in the data.We  show  that  both  our  methods  outperform  existing  unsupervised approaches in quantitative metrics that measure semantic accuracy of the learned representations.In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.

Usiamo le reti siamesi per guidare e districare il processo di generazione nelle GAN senza dati annotati.


We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand.PVars aim to make using ML in programming easier by hybridizing the two.We leverage the existing concept of variables and create a new type, a predicted variable.PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated.We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced.PVars allow for a seamless integration of ML into existing systems and algorithms.Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods.To learn faster, PVars use the heuristic function, which they are replacing, as an initial function.We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.

Presentiamo Predicted Variables, un approccio per rendere il machine learning un membro di rilievo dei linguaggi di programmazione.


Much recent research has been devoted to video prediction and generation,  but mostly for short-scale time horizons.The hierarchical video prediction method by Villegas et al. (2017) is an example of a state of the art method for long term video prediction.  However, their method has limited applicability in practical settings as it requires a ground truth pose (e.g.,  poses of joints of a human) at training time.   This paper presents a long term hierarchical video prediction model that does not have such a restriction.We show that the network learns its own higher-level structure (e.g., pose equivalent hidden variables) that works better in cases where the ground truth pose does not fully capture all of the information needed to  predict  the  next  frame.   This  method  gives  sharper  results  than  other  video prediction methods which do not require a ground truth pose, and its efficiency is shown on the Humans 3.6M and Robot Pushing datasets.

Mostriamo come addestrare un modello di predizione video gerarchico senza bisogno di label di posa.


Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence.Specifically, human agents are very easily able to translate the task communicated in one sensory domain (say vision) into a representation that enables them to complete this task when they can only sense their environment using a separate sensory modality (say touch).In order to build agents with similar capabilities, in this work we consider the problem of a retrieving a target object from a drawer.The agent is provided with an image of a previously unseen object and it explores objects in the drawer using only tactile sensing to retrieve the object that was shown in the image without receiving any visual feedback.Success at this task requires close integration of visual and tactile sensing.We present a method for performing this task in a simulated environment using an anthropomorphic hand.We hope that future research in the direction of combining sensory signals for acting will find the object retrieval from a drawer to be a useful benchmark problem

In questo lavoro, studiamo il problema dell'apprendimento di rappresentazioni per identificare nuovi oggetti esplorando gli oggetti utilizzando il rilevamento tattile. Il punto chiave qui è che la query è fornita nel dominio delle immagini.


Locality sensitive hashing schemes such as \simhash provide compact representations of multisets from which similarity can be estimated.However, in certain applications, we need to estimate the similarity of dynamically changing sets.  In this case, we need the representation to be a homomorphism so that the hash of unions and differences of sets can be computed directly from the hashes of operands.  We propose two representations that have this property for cosine similarity (an extension of \simhash and angle-preserving random projections), and make substantial progress on a third representation for Jaccard similarity (an extension of \minhash).We employ these hashes to compress the sufficient statistics of a conditional random field (CRF) coreference model and study how this compression affects our ability to compute similarities as entities are split and merged during inference.\cut{We study these hashes in a conditional random field (CRF) hierarchical coreference model in order to compute the similarity of entities as they are merged and split during inference.}We also provide novel statistical analysis of \simhash to help justify it as an estimator inside a CRF, showing that the bias and variance reduce quickly with the number of bits.On a problem of author coreference, we find that our \simhash scheme allows scaling the hierarchical coreference algorithm by an order of magnitude without degrading its statistical performance or the model's coreference accuracy, as long as we employ at least 128 or 256 bits.  Angle-preserving random projections further improve the coreference quality, potentially allowing even fewer dimensions to be used.

Impieghiamo schemi di compressione lineare omomorfica per rappresentare le statistiche sufficienti di un conditional random field di coreferenza e questo ci permette di scalare l'inferenza e migliorare la velocità di un ordine di grandezza.


Motivated by applications to unsupervised learning, we consider the problem of measuring mutual information.Recent analysis has shown that naive kNN estimators of mutual information have serious statistical limitations motivating more refined methods.In this paper we prove that serious statistical limitations are inherent to any measurement method.More specifically, we show that any distribution-free high-confidence lower bound on mutual information cannot be larger than $O(\ln N)$ where $N$ is the size of the data sample.We also analyze the Donsker-Varadhan lower bound on KL divergence in particular and show that, when simple statistical considerations are taken into account, this bound can never produce a high-confidence value larger than $\ln N$.While large high-confidence lower bounds are impossible, in practice one can use estimators without formal guarantees.We suggest expressing mutual information as a difference of entropies and using cross entropy as an entropy estimator. We observe that, although cross entropy is only an upper bound on entropy, cross-entropy estimates converge to the true cross entropy at the rate of $1/\sqrt{N}$.

Diamo un'analisi teorica della misura e dell'ottimizzazione dell'informazione reciproca.


In this paper, we propose a neural network framework called neuron hierarchical network (NHN), that evolves beyond the hierarchy in layers, and concentrates on the hierarchy of neurons.We observe mass redundancy in the weights of both handcrafted and randomly searched architectures.Inspired by the development of human brains, we prune low-sensitivity neurons in the model and add new neurons to the graph, and the relation between individual neurons are emphasized and the existence of layers weakened.We propose a process to discover the best base model by random architecture search, and discover the best locations and connections of the added neurons by evolutionary search.Experiment results show that the NHN achieves higher test accuracy on Cifar-10 than state-of-the-art handcrafted and randomly searched architectures, while requiring much fewer parameters and less searching time.

Rompendo la gerarchia dei layer, proponiamo un approccio in 3 fasi per la costruzione di neuron-hierarchy network che superano NAS, SMASH e la rappresentazione gerarchica con meno parametri e con un tempo di ricerca più breve.


Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data.In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data.We find that our approach(i) quickly converges to the optimal simulation parameters in controlled experiments and(ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

Proponiamo un algoritmo che regola automaticamente i parametri di un motore di simulazione per generare dati di training per una rete neurale in modo da massimizzare la validation accuracy.


Modelling statistical relationships beyond the conditional mean is crucial in many settings.Conditional density estimation (CDE) aims to learn the full conditional probability density from data.Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective.Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective.To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency.In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models.The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce.

Uno schema di regolarizzazione model-agnostic per la stima della densità condizionale basata su rete neurale.


Unsupervised representation learning holds the promise of exploiting large amount of available unlabeled data to learn general representations.A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs).However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervising for recognition.Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data.Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level.Our key contribution is a bottleneck formulation in a VAE framework that encourages mid-level style representations.Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs.

Una formulazione del collo di bottiglia basata su patch in un framework VAE che impara rappresentazioni non supervisionate più adatte al riconoscimento visivo.


Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs).In this paper, we present an efficient parametrization of the transition matrix of an RNN that allows us to stabilize the gradients that arise in its training.Specifically, we parameterize the transition matrix by its singular value decomposition (SVD), which allows us to explicitly track and control its singular values.We attain efficiency by using tools that are common in numerical linear algebra, namely Householder reflectors for representing the orthogonal matrices that arise in the SVD.By explicitly controlling the singular values, our proposed svdRNN method allows us to easily solve the exploding gradient problem and we observe that it empirically solves the vanishing gradient issue to a large extent.We note that the SVD parameterization can be used for any rectangular weight matrix, hence it can be easily extended to any deep neural network, such as a multi-layer perceptron.Theoretically, we demonstrate that our parameterization does not lose any expressive power, and show how it potentially makes the optimization process easier.Our extensive  experimental results also demonstrate that the proposed framework converges faster, and has good generalization, especially when the depth is large.

Per risolvere i problemi di vanishing/exploding gradient, proponiamo una parametrizzazione efficiente della matrice di transizione di RNN che non perde potenza espressiva, converge più velocemente e ha una buona generalizzazione.


Recent image style transferring methods achieved arbitrary stylization with input content and style images.To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks.This shortcoming resulted in generating partially and inexactly transferred style in the generated images.To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process.First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps.Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection.Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.

Un articolo che suggerisce un metodo per trasformare lo stile delle immagini utilizzando deep neural network.


Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill that is trivial for humans.Research in this area is made difficult by the paucity of suitable publicly available datasets both for emotion and dialogues.This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems.Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores), compared to models merely trained on large-scale Internet conversation data.We also present empirical comparisons of several ways to improve the performance of a given model by leveraging existing models or datasets without requiring lengthy re-training of the full model.

Miglioriamo i sistemi di dialogo esistenti per rispondere alle persone che condividono storie personali, incorporando rappresentazioni di predizione delle emozioni e rilasciamo anche un nuovo benchmark e un dataset di dialoghi empatici.


Granger causality is a widely-used criterion for analyzing interactions in large-scale networks.As most physical interactions are inherently nonlinear, we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measurements.Our proposed approach relies on modeling the embedded nonlinearities in the measurements using a component-wise time series prediction model based on Statistical Recurrent Units (SRUs).We make a case that the network topology of Granger causal relations is directly inferrable from a structured sparse estimate of the internal parameters of the SRU networks trained to predict the processes’ time series measurements.We propose a variant of SRU, called economy-SRU, which, by design has considerably fewer trainable parameters, and therefore less prone to overfitting.The economy-SRU computes a low-dimensional sketch of its high-dimensional hidden state in the form of random projections to generate the feedback for its recurrent processing.Additionally, the internal weight parameters of the economy-SRU are strategically regularized in a group-wise manner to facilitate the proposed network in extracting meaningful predictive features that are highly time-localized to mimic real-world causal events.Extensive experiments are carried out to demonstrate that the proposed economy-SRU based time series prediction model outperforms the MLP, LSTM and attention-gated CNN-based time series models considered previously for inferring Granger causality.

Una nuova architettura di rete neurale ricorrente per rilevare la causalità di Granger a coppie tra serie temporali non linearmente interagenti.


Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data.However, GCN computes nodes' representation recursively from their neighbors, making the receptive field size grow exponentially with the number of layers.  Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds.In this paper, we develop a preprocessing strategy and two control variate based algorithms to further reduce the receptive field size.Our algorithms are guaranteed to converge to GCN's local optimum regardless of the neighbor sampling size.Empirical results show that our algorithms have a similar convergence speed per epoch with the exact algorithm even using only two neighbors per node.The time consumption of our algorithm on the Reddit dataset is only one fifth of previous neighbor sampling algorithms.

Un algoritmo di training stocastico basato sulla variabile di controllo per graph convolutional network per le quali il campo recettivo può essere solo due vicini per nodo.


Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption.We propose to apply Monte Carlo methods and importance sampling to sparsify and quantize pre-trained neural networks without any retraining.We obtain sparse, low bit-width integer representations that approximate the full precision weights and activations.The precision, sparsity, and complexity are easily configurable by the amount of sampling performed.Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, while the resulting quantized sparse networks show minimal accuracy loss compared to the original full-precision networks.Our method either outperforms or achieves results competitive with methods that do require additional training on a variety of challenging tasks.

Metodi Monte Carlo per quantizzare i modelli pre-trained senza alcun training aggiuntivo.


We propose the Information Maximization Autoencoder (IMAE), an information theoretic approach to simultaneously learn continuous and discrete representations in an unsupervised setting.Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.A decoder is included to approximate the posterior distribution of the data given their representations, where a high fidelity approximation can be achieved by leveraging the informative representations.     We show that the proposed objective is theoretically valid and provides a principled framework for understanding the tradeoffs regarding informativeness of each representation factor, disentanglement of representations, and decoding quality.

Approccio basato sulla teoria dell'informazione per l'apprendimento unsupervised di un ibrido di rappresentazioni discrete e continue,


Learning rules for neural networks necessarily include some form of regularization.Most regularization techniques are conceptualized and implemented in the space of parameters.However, it is also possible to regularize in the space of functions.Here, we propose to measure networks in an $L^2$ Hilbert space, and test a learning rule that regularizes the distance a network can travel through $L^2$-space each update.  This approach is inspired by the slow movement of gradient descent through parameter space as well as by the natural gradient, which can be derived from a regularization term upon functional change.The resulting learning rule, which we call Hilbert-constrained gradient descent (HCGD), is thus closely related to the natural gradient but regularizes a different and more calculable metric over the space of functions.Experiments show that the HCGD is efficient and leads to considerably better generalization.

È importante considerare l'ottimizzazione nello spazio delle funzioni, non solo nello spazio dei parametri. Introduciamo una regola di apprendimento che riduce la distanza percorsa nello spazio delle funzioni, proprio come SGD limita la distanza percorsa nello spazio dei parametri.


Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization.Research on SGD resurged recently in machine learning for optimizing convex loss functions and training nonconvex deep neural networks.The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization.There exist, however, many scenarios (e.g., graphs) where an unbiased estimator may be as expensive to compute as the full gradient because training examples are interconnected.Recently, Chen et al. (2018) proposed using a consistent gradient estimator as an economic alternative.Encouraged by empirical success, we show, in a general setting, that consistent estimators result in the same convergence behavior as do unbiased ones.Our analysis covers strongly convex, convex, and nonconvex objectives.We verify the results with illustrative experiments on synthetic and real-world data.This work opens several new research directions, including the development of more efficient SGD updates with consistent estimators and the design of efficient training algorithms for large-scale graphs.

Teoria della convergenza per stimatori di gradiente biased (ma consistent) nell'ottimizzazione stocastica e applicazione alle graph convolutional network


We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification.In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand.We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident.We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting.Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification).We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.

Usiamo gli snapshot del processo di training per migliorare qualsiasi metodo di stima dell'incertezza di un classificatore DNN.


Existing public face image datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented.The models trained from such datasets suffer from inconsistent classification accuracy, which limits the applicability of face analytic systems to non-White race groups.To mitigate the race bias problem in these datasets, we constructed a novel face image dataset containing 108,501 images which is balanced on race.We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure the generalization performance.We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent across race and gender groups.We also compare several commercial computer vision APIs and report their balanced accuracy across gender, race, and age groups.

Un nuovo dataset di immagini facciali con equilibrio di razza, sesso ed età che può essere utilizzato per la misurazione e l'attenuazione dei bias


Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object,  but instead identifying higher-level  attributes  that  best  summarize  the  aspects  of  an  object.   In  this  work  we attempt  to  model  the  drawing  process  of  fonts  by  building  sequential generative models of vector graphics.  This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation.We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset.We envision that our model can find use as a tool for designers to facilitate font design.

Tentiamo di modellare il processo di disegno dei font costruendo modelli generativi sequenziali di grafica vettoriale (SVGs), una rappresentazione altamente strutturata dei caratteri dei font.


What can we learn about the functional organization of cortical microcircuits from large-scale recordings of neural activity?  To obtain an explicit and interpretable model of time-dependent functional connections between neurons and to establish the dynamics of the cortical information flow, we develop 'dynamic neural relational inference' (dNRI).We study  both synthetic and real-world neural spiking data and demonstrate that the developed method is able to uncover the dynamic relations between neurons more reliably than existing baselines.

Sviluppiamo "dynamic neural relational inference", un modello di autoencoder variazionale che può rappresentare in modo esplicito e interpretabile le relazioni dinamiche nascoste tra i neuroni.


DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.DeePa optimizes parallelism at the granularity of each individual layer in the network.We present an elimination-based algorithm that finds an optimal parallelism configuration for every layer.Our evaluation shows that DeePa achieves up to 6.5× speedup compared to state-of-the-art deep learning frameworks and reduces data transfers by up to 23×.

Per quanto ne sappiamo, DeePa è il primo framework di deep learning che controlla e ottimizza il parallelismo delle CNN in tutte le dimensioni parallelizzabili per ogni layer.


One can substitute each neuron in any neural network with a kernel machine and obtain a counterpart powered by kernel machines.The new network inherits the expressive power and architecture of the original but works in a more intuitive way since each node enjoys the simple interpretation as a hyperplane (in a reproducing kernel Hilbert space).Further, using the kernel multilayer perceptron as an example, we prove that in classification, an optimal representation that minimizes the risk of the network can be characterized for each hidden layer.This result removes the need of backpropagation in learning the model and can be generalized to any feedforward kernel network.Moreover, unlike backpropagation, which turns models into black boxes, the optimal hidden representation enjoys an intuitive geometric interpretation, making the dynamics of learning in a deep kernel network simple to understand.Empirical results are provided to validate our theory.

Combiniamo i metodi kernel con i modelli connessionisti e dimostriamo che le architetture profonde risultanti possono essere addestrate a layer e hanno dinamiche di apprendimento più trasparenti.


Many notions of fairness may be expressed as linear constraints, and the resulting constrained objective is often optimized by transforming the problem into its Lagrangian dual with additive linear penalties.In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium.  In this paper, we propose to modify the linear penalties to second-order ones, and we argue that this results in a more practical training procedure in non-convex, large-data settings.For one, the use of second-order penalties allows training the penalized objective with a fixed value of the penalty coefficient, thus avoiding the instability and potential lack of convergence associated with two-player min-max games.Secondly, we derive a method for efficiently computing the gradients associated with the second-order penalties in stochastic mini-batch settings.Our resulting algorithm performs well empirically, learning an appropriately fair classifier on a number of standard benchmarks.

Proponiamo un metodo per ottimizzare stocasticamente le penalità di secondo ordine e mostriamo come questo possa applicarsi al training di classificatori fairness-aware.


Training methods for deep networks are primarily variants on stochastic gradient descent.  Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts.  However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure.  This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent.  To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants.  There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets.  CR proved particularly successful in escaping plateau regions of the objective function.  We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.

Mostriamo che le derivate delle reti di deep learning hanno una struttura a basso rango, e questa struttura ci permette di utilizzare le informazioni sulle derivate del secondo ordine per calcolare i learning rate in modo adattivo e in un modo computazionalmente fattibile.


The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.Nonetheless, min-max optimization beyond the purpose of AT has not been rigorously explored in the research of adversarial attack and defense.In particular, given a set of risk sources (domains), minimizing the maximal loss induced from the domain set can be reformulated as a general min-max problem that is different from AT.Examples of this general formulation include attacking model ensembles, devising universal perturbation under multiple inputs or data transformations, and generalized AT over different types of attack models.We show that these problems can be solved under a unified and theoretically principled min-max optimization framework.  We also show that the self-adjusted domain weights learned from our method provides a means to explain the difficulty level of attack and defense over multiple domains.Extensive experiments show that our approach leads to substantial performance improvement over the conventional averaging strategy.

Un framework unificato di ottimizzazione min-max per l'attacco e la difesa adversarial


Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification.However, the high dimensionality of these representations makes them difficult to interpret and prone to over-fitting.We propose a simple, intuitive and scalable dimension reduction framework that takes into account the soft probabilistic interpretation of standard deep models for classification.When applying our framework to visualization, our representations more accurately reflect inter-class distances than standard visualization techniques such as t-SNE.We show experimentally that our framework improves generalization performance to unseen categories in zero-shot learning.We also provide a finite sample error upper bound guarantee for the method.

riduzione della dimensionalità per i casi in cui gli esempi possono essere rappresentati come distribuzioni soft di probabilità


Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments.These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces.However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy.In this work, we propose an approach using deep representation learning algorithms to learn an adequate goal space.This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space.We present experiments with a simulated robot arm interacting with an object, and we show that exploration algorithms using such learned representations can closely match, and even sometimes improve, the performance obtained using engineered representations.

Proponiamo una nuova architettura di Intrinsically Motivated Goal Exploration con apprendimento unsupervised delle rappresentazioni dello spazio degli obiettivi, e valutiamo come le varie implementazioni permettono la scoperta di una diversità di policy.


One of the main challenges of deep learning methods is the choice of an appropriate training strategy.In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures.In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network.We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding.This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task.This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance.

Proponiamo un ulteriore step di training, chiamato post-training, che calcola i pesi ottimali per l'ultimo layer della rete.


Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint.Deploying neural NLP models to mobile devices requires compressing the word embeddings without any significant sacrifices in performance.For this purpose, we propose to construct the embeddings with few basis vectors.For each word, the composition of basis vectors is determined by a hash code.To maximize the compression rate, we adopt the multi-codebook quantization approach instead of binary coding scheme.Each code is composed of multiple discrete numbers, such as (3, 2, 1, 8), where the value of each component is limited to a fixed range.We propose to directly learn the discrete codes in an end-to-end neural network by applying the Gumbel-softmax trick.Experiments show the compression rate achieves 98% in a sentiment analysis task and 94% ~ 99% in machine translation tasks without performance loss.In both tasks, the proposed method can improve the model performance by slightly lowering the compression rate.Compared to other approaches such as character-level segmentation, the proposed method is language-independent and does not require modifications to the network architecture.

Comprimere i word embedding oltre il 94% senza danneggiare le prestazioni.


It is important to detect anomalous inputs when deploying machine learning systems.The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples.At the same time, diverse image and text data are available in enormous quantities.We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE).This enables anomaly detectors to generalize and detect unseen anomalies.In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance.We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue.We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

OE insegna ai rilevatori di anomalie ad apprendere l'euristica per rilevare anomalie non viste; gli esperimenti riguardano la classificazione, la stima della densità e la calibrazione in setting NLP e di visione; non ci accordiamo su sample di distribuzione di test, a differenza del lavoro precedente


While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets.For instance, to fool the discriminator, a generative adversarial network (GAN) exclusively trained to transform images of black-haired *men* to blond-haired *men* would need to change gender-related characteristics as well as hair color when given images of black-haired *women* as input.This is problematic, as often it is possible to obtain *a* pair of (source, target) distributions but then have a second source distribution where the target distribution is unknown.The computational challenge is that generative models are good at generation within the manifold of the data that they are trained on.However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied.To address this, we introduce a technique called *neuron editing* that learns how neurons encode an edit for a particular transformation in a latent space.We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons.By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations.Our technique is general and works on a wide variety of data domains and applications.We first demonstrate it on image transformations and then move to our two main biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.

Un metodo per imparare una trasformazione tra una coppia di dataset source/target e applicarla a un dataset source separato per il quale non esiste un dataset target


In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle.We study the effectiveness of the near- optimal cost-to-go oracle on the planning horizon and demonstrate that the cost- to-go oracle shortens the learner’s planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one- step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance.Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning.Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal.We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.

Combinare l'imitation learning e il reinforcement learning per imparare a superare l'esperto


Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.Most of the existing works implicitly assume that the clean samples from the target distribution are easily available.However, in many applications, this assumption is violated.In this paper, we consider the observation setting in which the samples from a target distribution are given by the superposition of two structured components, and leverage GANs for learning of the structure of the components.We propose a novel framework, demixing-GAN, which learns the distribution of two components at the same time.Through extensive numerical experiments, we demonstrate that the proposed framework can generate clean samples from unknown distributions, which further can be used in demixing of the unseen test images.

Un approccio di unsupervised learning per separare due segnali strutturati a partire dalla loro sovrapposizione


Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.They are based on the idea of transforming a simple density into that of the data.We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning.For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows.The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE).Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits.The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.

Esploriamo la relazione tra i normalizing flow e i variational/denoising autoencoder, e proponiamo un nuovo modello che li generalizza.


We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer.Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set.Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such asan ensemble of agents trained on the full data.We evaluate on the tasks of document retrieval and question answering.Theimproved performance seems due to the increased diversity of reformulation strategies.This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind.However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results.Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes.At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.

Usiamo il reinforcement learning per la riformulazione delle query su due task e sorprendentemente troviamo che quando si addestrano più agenti la diversità delle riformulazioni è più importante della specializzazione.


We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence.We formulate these problems as {\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints.Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data.Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline.We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.

Un framework generale per incorporare vincoli di sicurezza a lungo termine nel reinforcement learning basato su policy


Generative networks are known to be difficult to assess.Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images.But the validation of their quality is highly dependent on the method used.A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset.This paper presents a new method to assess a generator.Our approach is based on training a classifier with a mixture of real and generated samples.We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data.This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset.We compare this result with the score of the same classifier trained on the real training data mixed with noise.By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset.Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.

Valutazione delle reti generative attraverso la loro capacità di data augmentation su modelli discrimativi.


We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization.ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience.For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily.While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source.To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets:(i) scientific abstracts of sources and(ii) partitioned press releases.We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are.Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.

Novità: applicazione della modellazione seq2seq all'automazione del giornalismo scientifico; dataset highly abstractive; trucchi per il transfer learning; misura di valutazione automatica.


The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction.To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent.An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent.To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior.In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors.Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors.We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.

Presentiamo un approccio per riprogettare l'ambiente in modo che i comportamenti non interpretabili degli agenti siano ridotti al minimo o eliminati.


Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).In this paper, we propose iterative inference models, which learn how to optimize a variational lower bound through repeatedly encoding gradients.Our approach generalizes VAEs under certain conditions, and by viewing VAEs in the context of iterative inference, we provide further insight into several recent empirical findings.We demonstrate the inference optimization capabilities of iterative inference models, explore unique aspects of these models, and show that they outperform standard inference models on typical benchmark data sets.

Proponiamo una nuova classe di modelli di inferenza che codificano iterativamente i gradienti per stimare le distribuzioni posteriori approssimate.


In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients.For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes.This produces the so-called "weight transport problem" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli.This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm.However, such random weights do not appear to work well for large networks.Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design.We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights.As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10.Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.

Presentiamo una regola di apprendimento per i pesi di feedback in una spiking neural network che affronta il problema del trasporto dei pesi.


Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space.However, these approximations are well-documented to become degenerate in training.Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.Conversely, diffusion maps (DM) automatically \textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.In this paper, we propose \textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model.The measure, the \textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret.The method, the \textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data.To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization.We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models.

Combiniamo l'inferenza variazionale e l'apprendimento del manifold (in particolare VAE e mappe di diffusione) per costruire un modello generativo basato su un cammino casuale di diffusione su un manifold di dati; generiamo sample attingendo dalla distribuzione stazionaria del cammino.


While deep learning and deep reinforcement learning systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch.Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning.However, the multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently.The reasons why multi-task learning is so challenging compared to single task learning are not fully understood.Motivated by the insight that gradient interference causes optimization challenges, we develop a simple and general approach for avoiding interference between gradients from different tasks, by altering the gradients through a technique we refer to as “gradient surgery”.We propose a form of gradient surgery that projects the gradient of a task onto the normal plane of the gradient of any other task that has a conflicting gradient.On a series of challenging multi-task supervised and multi-task reinforcement learning problems, we find that this approach leads to substantial gains in efficiency and performance.  Further, it can be effectively combined with previously-proposed multi-task architectures for enhanced performance in a model-agnostic way.

Sviluppiamo un approccio semplice e generale per evitare l'interferenza tra i gradienti di diversi task, che migliora le prestazioni del multi-task learning in entrambi i domini di supervised learning e reinforcement learning.


In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density.This point of view unifies the goals of such approaches as Markov Chain Monte Carlo (MCMC), Generative Adversarial Networks (GANs), variational inference.To reveal the connection we derive the lower bound on the acceptance rate and treat it as the objective for learning explicit and implicit samplers.The form of the lower bound allows for doubly stochastic gradient optimization in case the target distribution factorizes (i.e. over data points).We empirically validate our approach on Bayesian inference for neural networks and generative models for images.

Learning to sample tramite il limite inferiore del tasso di accettazione dell'algoritmo Metropolis-Hastings


This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE).We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more.

BERT generalizzato per input continui e cross-modali; rappresentazioni video self-supervised allo stato dell'arte.


We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure.We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches, our model achieves higher accuracy in half as many epochs with five times fewer learnable parameters.Our model directly models underlying question logic using a recurrent controller that jointly predicts and executes functional neural modules; it explicitly forks subprocesses to handle logical branching.While FiLM and other competitive models are static architectures with less supervision, we argue that inclusion of program labels enables learning of higher level logical operations -- our architecture achieves particularly high performance on questions requiring counting and integer comparison. We further demonstrate the generality of our approach though DDRstack -- an application of our method to reverse Polish notation expression evaluation in which the inclusion of a stack assumption allows our approach to generalize to long expressions, significantly outperforming an LSTM with ten times as many learnable parameters.

Un'architettura dinamica generica che impiega un meccanismo di forking differenziabile specifico del problema per codificare ipotesi hard di strutture di dati. Applicato a CLEVR VQA e alla valutazione delle espressioni.


We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.SAIL addresses two important challenges of AIL, including the implicit reward bias and potential training instability.We also show that SAIL is at least as efficient as standard AIL.In an extensive evaluation, we demonstrate that the proposed method effectively handles the reward bias and achieves better performance and training stability than other baseline methods on a wide range of benchmark control tasks.

Unifichiamo la stima del supporto con la famiglia degli algoritmi di Adversarial Imitation Learning nel Support-guided Adversarial Imitation Learning, un framework di imitation learning più robusto e stabile.


We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges.We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a multi-graph setting and are unable to effectively learn from very sparse data.To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph, that leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization.Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph enables not only fast adaptation but also better final convergence and can effectively learn using only a small sample of true edges.

Applichiamo il meta-learning basato sul gradiente al dominio dei grafi e introduciamo una nuova funzione di transfer specifica per i grafi per effettuare un ulteriore bootstrapping del processo.


Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space.To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space.We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network's distribution space.

Proponiamo un nuovo metodo per addestrare incrementalmente un modello generativo con mixture per approssimare la proiezione delle informazioni della distribuzione dei dati reali.


Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements.With a generative model we can represent an image with a much lower dimensional latent codes.In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements.However, recent studies revealed that even untrained deep neural networks can work as a prior for recovering natural images.These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements.In this paper, we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem.We show that by optimizing over latent code, we can additionally get concise representation of the frames which retain the structural similarity of the video frames.We also apply low-rank constraint on the latent codes to represent the video sequences in even lower dimensional latent space.We empirically show that our proposed methods provide better or comparable accuracy and low computational complexity compared to the existing methods.

Recuperare i video da misure compressive imparando una rappresentazione a bassa dimensione (low-rank) direttamente dalle misure mentre si allena un deep generator.


Magnitude-based pruning is one of the simplest methods for pruning neural networks.Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures.Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization.Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.

Studiamo una generalizzazione multilayer del pruning basato sulla magnitudine.


Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary.Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average.In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem.Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets.Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently.Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.

Introduciamo la massimizzazione dell'ipervolume per il training di GAN con discriminatori multipli, mostrando miglioramenti delle prestazioni in termini di qualità e diversità del sample.


Designing of search space is a critical problem for neural architecture search (NAS) algorithms.We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms.This search space facilitates direct selection of channel numbers and kernel sizes in convolutions.In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training.The algorithm is further accelerated by a dynamic network shrinkage technique.Instead of a  search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost.We open our entire codebase at: https://github.com/meijieru/AtomNAS.

Un nuovo stato dell'arte su Imagenet per il setting mobile


We introduce Lyceum, a high-performance computational ecosystem for robotlearning.   Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming  language  with  the  performance  of  native  C.  Lyceum  is  up  to  10-20Xfaster  compared  to  other  popular  abstractions  like  OpenAI’sGymand  Deep-Mind’sdm-control.  This substantially reduces training time for various re-inforcement learning algorithms;  and is also fast enough to support real-timemodel  predictive  control  with  physics  simulators.   Lyceum  has  a  straightfor-ward API and supports parallel computation across multiple cores or machines.The code base,  tutorials,  and demonstration videos can be found at: https://sites.google.com/view/lyceum-anon.

Un framework per la simulazione robotica ad alte prestazioni e per lo sviluppo di algoritmi.


There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift.Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA).Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation.Devoid of such highly impractical assumptions, we propose a novel two-stage learning process.Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework.Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples.To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM).A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.

Un nuovo paradigma di domain adaptation unsupervised - che esegue l'adattamento senza accedere ai dati di origine ('source-free') e senza alcuna assunzione circa il gap di categoria tra source e target ('universale').


One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks.Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks.While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training.In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision.Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones.We propose three distinct models under our active sampling framework.An adaptive method with extremely competitive multi-tasking performance.A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem.A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor-critic methods for optimizing the multi-tasking performance directly.We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.

Lasciare che un meta-learner decida il task su cui addestrare un agente in un ambiente multi-task migliora sostanzialmente la capacità di multi-tasking


Numerous machine reading comprehension (MRC) datasets often involve manual annotation, requiring enormous human effort, and hence the size of the dataset remains significantly smaller than the size of the data available for unsupervised learning.Recently, researchers proposed a model for generating synthetic question-and-answer data from large corpora such as Wikipedia.This model is utilized to generate synthetic data for training an MRC model before fine-tuning it using the original MRC dataset.This technique shows better performance than other general pre-training techniques such as language modeling, because the characteristics of the generated data are similar to those of the downstream MRC data.However, it is difficult to have high-quality synthetic data comparable to human-annotated MRC datasets.To address this issue, we propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data involving two advanced techniques, (1) dynamically determining K answers and (2) pre-training the question generator on the answer-containing sentence generation task.We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.Experimental results show that our approach outperforms existing generation methods and increases the performance of the state-of-the-art MRC models across a range of MRC datasets such as SQuAD-v1.1, SQuAD-v2.0, KorQuAD and QUASAR-T without any architectural modifications to the original MRC model.

Proponiamo Answer-containing Sentence Generation (ASGen), un nuovo metodo di pre-training per generare dati sintetici per la comprensione automatica della lettura.


Deep neural networks (DNNs) dominate current research in machine learning.Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables.In contrast, embedded devices have a very limited capability.As a result, both model size and inference time must be significantly reduced if DNNs are to achieve suitable performance on embedded devices.We propose a soft quantization approach to train DNNs that can be evaluated using pure fixed-point arithmetic.By exploiting the bit-shift mechanism, we derive fixed-point quantization constraints for all important components, including batch normalization and ReLU.Compared to floating-point arithmetic, fixed-point calculations significantly reduce computational effort whereas low-bit representations immediately decrease memory costs.We evaluate our approach with different architectures on common benchmark data sets and compare with recent quantization approaches.We achieve new state of the art performance using 4-bit fixed-point models with an error rate of 4.98% on CIFAR-10.

Approccio di quantizzazione soft per imparare rappresentazioni pure e fixed-point di deep neural network


We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization.Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process.We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably.By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters.Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification.Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet.Combined with weight quantization, the resulted models are up to \textbf{180$\times$} smaller and theoretically up to \textbf{16$\times$} faster than the well-established baselines, without noticeable performance drop.

Presentiamo una nuova architettura di rete per l'apprendimento di deep neural network compatte ed efficienti


Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles.Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy.In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection.Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards.We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.

Studiamo per la prima volta gli adversarial attack di machine learning contro i meccanismi di Multiple Object Tracking.


Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years.Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data.This work further explores this direction by proposing a new framework to seamlessly couple SlfSL with SemSL.Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes.By implementing this framework through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Prediction (CRAP).Specifically, CRAP is featured by adopting a module which predicts the image rotation angle \textbf{conditioned on the candidate image class}.Through experimental evaluation, we show that CRAP achieves superior performance over the other existing ways of combining SlfSL and SemSL.Moreover, the proposed SemSL framework is highly extendable.By augmenting CRAP with a simple SemSL technique and a modification of the rotation angle prediction task, our method has already achieved the state-of-the-art SemSL performance.

Accoppiare l'apprendimento semi-supervised con l'apprendimento self-supervised e modellare esplicitamente il task self-supervised condizionato da quello semi-supervised


Ranking is a central task in machine learning and information retrieval.In this task, it is especially important to present the user with a slate of items that is appealing as a whole.This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be chosen alongside it.In this work, we propose a sequence-to-sequence model for ranking called seq2slate.At each step, the model predicts the next item to place on the slate given the items already chosen.The recurrent nature of the model allows complex dependencies between items to be captured directly in a flexible and scalable way.We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data.We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.

Un'architettura di pointer network per il re-ranking degli articoli, appresa dai log dei click-through.


While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.In practice, the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance.In this work, we use the framework of algorithmic stability to provide an upper-bound on the generalization error for the class of strongly convex loss functions, under mild technical assumptions.Our bound decays to zero inversely with the size of the training set, and increases as the momentum parameter is increased.We also develop an upper-bound on the expected true risk,  in terms of the number of training steps, the size of the training set, and the momentum parameter.

Il metodo dello stochastic gradient con momentum è capace di generalizzare.


Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions.However, in real life expert demonstrations, often the action information is missing and only state trajectories are available.We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories.Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability.Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics.Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods.We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.

Imparare a imitare un esperto in assenza di azioni ottimali imparando al tempo stesso un modello dinamico durante l'esplorazione dell'ambiente.


Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.While this discovery is insightful, finding proper sub-networks requires iterative training and pruning.The high cost incurred limits the applications of the lottery ticket hypothesis.We show there exists a subset of the aforementioned sub-networks that converge significantly faster during the training process and thus can mitigate the cost issue.We conduct extensive experiments to show such sub-networks consistently exist across various model structures for a restrictive setting of hyperparameters (e.g., carefully selected learning rate, pruning ratio, and model capacity).  As a practical application of our findings, we demonstrate that such sub-networks can help in cutting down the total time of adversarial training, a standard approach to improve robustness, by up to 49% on CIFAR-10 to achieve the state-of-the-art robustness.

Mostriamo la possibilità del pruning per trovare una piccola sottorete con un tasso di convergenza significativamente più alto del modello completo.


Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors.We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement.We also propose a new disentanglement metric which is better aligned with the qualitative disentanglement observed in the decoder's output.We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).

Proponiamo un approccio basato sull'inferenza variazionale per favorire l'inferenza di latent disentangled. Proponiamo anche una nuova metrica per quantificare il disentanglement.


In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution.Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents.Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents.Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination.However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents.In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents.ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance.Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.

La nostra proposta ASN caratterizza l'influenza di diverse azioni su altri agenti usando reti neurali basate sulla semantica delle azioni tra di loro.


Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network.While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed to convert gated recurrent neural networks, so far.Recurrent neural networks in the form of networks of gating memory cells have been central in state-of-the-art solutions in problem domains that involve sequence recognition or generation.Here, we design an analog gated LSTM cell where its neurons can be substituted for efficient stochastic spiking neurons.These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.For such neurons, we approximate the effective activation function, which resembles a sigmoid.We show how analog neurons with such activation functions can be used to create an analog LSTM cell; networks of these cells can then be trained with standard backpropagation.We train these LSTM networks on a noisy and noiseless version of the original sequence prediction task from Hochreiter & Schmidhuber (1997), and also on a noisy and noiseless version of a classical working memory reinforcement learning task, the T-Maze.Substituting the analog neurons for corresponding adaptive spiking neurons, we then show that almost all resulting spiking neural network equivalents correctly compute the original tasks.

Mostriamo una spiking neural network asincrona, ricorrente e gated che corrisponde a un'unità LSTM.


CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation.This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average.The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance.We propose VideoEpitoma,  a neural network architecture comprising two modules: a timestamp selector and a video classifier.Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video.This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep.This decision is conditioned on both the timestep-level feature and the video-level consensus.A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification.Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\% without compromising the accuracy.In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs.Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation.In particular, we match the accuracy of I3D by using less than half of the computation.

Classificazione video efficiente utilizzando un modulo di gating condizionale basato sui frame per selezionare i frame più dominanti, seguito dalla modellazione temporale e dal classificatore.


Human annotation for syntactic parsing is expensive, and large resources are available only for a  fraction of languages.A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings).To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing.As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters.Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores.We demonstrate effectiveness of our approach with experiments on English, French and Swedish.

Programmazione dinamica differenziabile su pesi di ingresso perturbati con applicazione al VAE semi-supervised


DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks.We show that these methods do not produce the theoretically correct explanation for a linear model.Yet they are used on multi-layer networks with millions of parameters.This is a cause for concern since linear models are simple neural networks.We argue that explanation methods for neural nets should work reliably in the limit of  simplicity, the linear models.Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.

Senza apprendimento, è impossibile spiegare le decisioni di un modello di machine learning.


Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks.Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation.Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs.To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM).GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly.These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations.With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks.Furthermore, we empirically demonstrate the significance of considering global information.The source code will be publicly available in the near future.

Una semplice ed efficace graph neural network con un mix di step di una random walk e attention


Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints.Here we use a deep neural network prior instead of l1-regularization.Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data.During training, we use alternating minimization across an unrolled iterative network and jointly solve for the neural network weights and training set image reconstructions.At inference, we fix the weights and pass the measurements through the network.We compare reconstruction performance between unsupervised and supervised (i.e. with ground-truth) methods.We hypothesize this technique could be used to learn reconstruction when ground-truth data are unavailable, such as in high-resolution dynamic MRI.

Presentiamo una ricostruzione non supervisionata con deep learning per problemi inversi di imaging che combina reti neurali con vincoli basati su modelli.