This paper addresses the problem of incremental domain adaptation (IDA).We assume each domain comes sequentially, and that we could only access data in the current domain.The goal of IDA is  to build a unified model performing well on all the encountered domains.We propose to augment a recurrent neural network (RNN) with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition.The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the model capacity.We learn the new memory slots and fine-tune existing parameters by back-propagation. Experiments show that our approach significantly outperforms naive fine-tuning and previous work on IDA, including elastic weight consolidation and the progressive neural network.  Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results.

Presentiamo un'architettura neurale basata su memoria per incremental domain adaptation e forniamo risultati teorici ed empirici.


In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations.In this work, we develop a new sparse signal recovery algorithm using reinforcement learning (RL) and Monte CarloTree Search (MCTS).Similarly to orthogonal matching pursuit (OMP), our RL+MCTS algorithm chooses the support of the signal sequentially.The key novelty is that the proposed algorithm learns how to choose the next support as opposed to following a pre-designed rule as in OMP.Empirical results are provided to demonstrate the superior performance of the proposed RL+MCTS algorithm over existing sparse signal recovery algorithms.

Formulando il recupero del segnale sparso come un problema di decisione sequenziale, sviluppiamo un metodo basato su RL e MCTS che impara una policy per scoprire il supporto del segnale sparso.


Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics.A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.An important open question is how to learn a representation that is amenable to existing control algorithms?In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR).By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions.These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC).Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function.Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance.  Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control.

Imparare l'embedding per il controllo con osservazioni ad alta dimensionalità


The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks.Curiously, the deep learning revolution that revived neural networks has not paid much attention to topological aspects.The architectures of deep neural networks (DNNs) do not resemble their biological counterparts in the topological sense.We bridge this gap by presenting initial results of Deep Connectomics Networks (DCNs) as DNNs with topologies inspired by real-world neuronal networks.We show high classification accuracy obtained by DCNs whose architecture was inspired by the biological neuronal networks of C. Elegans and the mouse visual cortex.

Risultati iniziali all'intersezione delle neuroscienze di rete e del deep learning. C. Elegans e una corteccia visiva di un topo imparano a riconoscere le cifre scritte a mano.


Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively.However, the structure of standard feed-forward neural networks (FNNs) is simply a stack of fully connected layers, regardless of the feature correlations in data.In addition, the number of layers and the number of neurons are manually tuned on validation data, which is time-consuming and may lead to suboptimal networks.In this paper, we propose an unsupervised structure learning method for learning parsimonious deep FNNs.Our method determines the number of layers, the number of neurons at each layer, and the sparse connectivity between adjacent layers automatically from data.The resulting models are called Backbone-Skippath Neural Networks (BSNNs).Experiments on 17 tasks show that, in comparison with FNNs,  BSNNs can achieve better or comparable classification performance with much fewer parameters.The interpretability of BSNNs is also shown to be better than that of FNNs.

Un metodo di apprendimento della frameworkunsupervised per reti Feed-forward profonde parsimoniose.


Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a mathematical model.Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to characterize mathematically.In this work we demonstrate how the approximate distribution learned by a generative adversarial network (GAN) may be used as a prior in a Bayesian update to address both these challenges.We demonstrate the efficacy of this approach by inferring and quantifying uncertainty in a physics-based inverse problem and an inverse problem arising in computer vision.In this latter example, we also demonstrate how the knowledge of the spatial variation of uncertainty may be used to select an optimal strategy of placing the sensors (i.e. taking measurements), where information about the image is revealed one sub-region at a time.

Utilizzo di GAN come prior per un'inferenza bayesiana efficiente di campi complessi.


We argue that the widely used Omniglot and miniImageNet benchmarks are too simple because their class semantics do not vary across episodes, which defeats their intended purpose of evaluating few-shot classification methods.The class semantics of Omniglot is invariably “characters” and the class semantics of miniImageNet, “object category”.Because the class semantics are so similar, we propose a new method called Centroid Networks which can achieve surprisingly high accuracies on Omniglot and miniImageNet without using any labels at metaevaluation time.Our results suggest that those benchmarks are not adapted for supervised few-shot classification since the supervision itself is not necessary during meta-evaluation.The Meta-Dataset, a collection of 10 datasets, was recently proposed as a harder few-shot classification benchmark.Using our method, we derive a new metric, the Class Semantics Consistency Criterion, and use it to quantify the difficulty of Meta-Dataset.Finally, under some restrictive assumptions, we show that Centroid Networks is faster and more accurate than a state-of-the-art learning-to-cluster method (Hsu et al., 2018).

Omniglot e miniImageNet sono troppo semplici per l'apprendimento few shot perché possiamo risolverli senza usare label durante meta-evaluation, come dimostrato con un metodo chiamato centroid network


We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.We utilize the VIC framework, which maximizes an agent’s `empowerment’, ie the ability to reliably reach a diverse set of states -- and formulate a sandwich bound on the empowerment objective that allows identification of decision states. Unlike previous work, our decision states are discovered without extrinsic rewards -- simply by interacting with the world.Our results show that our decision states are:1) often interpretable, and2) lead to better exploration on downstream goal-driven tasks in partially observable environments.

Identificare gli stati di decisione (dove l'agente può intraprendere azioni che contano) senza supervisione della reward, usandolo per il transfer.


Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations.Typically, a model of the physical process in the form of differential equations is available but leads to intractable inference over its parameters.While the forward propagation of parameters through the model simulates the evolution of the system, the inverse problem of finding the parameters given the sequence of states is not unique.In this work, we propose a generalisation of the Bayesian optimisation framework to approximate inference.The resulting method learns approximations to the posterior distribution by applying Stein variational gradient descent on top of estimates from a Gaussian process model.Preliminary results demonstrate the method's performance on likelihood-free inference for reinforcement learning environments.

Un approccio per combinare inferenza variazionale e ottimizzazione bayesiana per risolvere problemi inversi complicati


We present a data driven approach to construct a library of feedback motion primitives for non-holonomic vehicles that guarantees bounded error in following arbitrarily long trajectories.This ensures that motion re-planning can be avoided as long as disturbances to the vehicle remain within a certain bound and also potentially when the obstacles are displaced within a certain bound.The library is constructed along local abstractions of the dynamics that enables addition of new motion primitives through abstraction refinement.We provide sufficient conditions for construction of such robust motion primitives for a large class of nonlinear dynamics, including commonly used models, such as the standard Reeds-Shepp model.The algorithm is applied for motion planning and control of a rover with slipping without its prior modelling.

Mostriamo che sotto alcune ipotesi sulla dinamica del veicolo e l'incertezza dell'ambiente è possibile sintetizzare automaticamente primitive di movimento che non accumulano errori nel tempo.


Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset.These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood.Our work shows that visually similar procedural noise patterns also act as UAPs.In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns.This behaviour, its causes, and implications deserve further in-depth study.

Le deep convolutional neural network esistenti nei task di classificazione delle immagini sono sensibili ai modelli di rumore di Gabor, cioè piccoli cambiamenti strutturati all'input causano grandi cambiamenti all'output.


Deep neural networks (DNNs) perform well on a variety of tasks despite the fact that most used in practice are vastly overparametrized and even capable of perfectly fitting randomly labeled data.Recent evidence suggests that developing "compressible" representations is key for adjusting the complexity of overparametrized networks to the task at hand and avoiding overfitting (Arora et al., 2018; Zhou et al., 2018).In this paper, we provide new empirical evidence that supports this hypothesis, identifying two independent mechanisms that emerge when the network’s width is increased: robustness (having units that can be removed without affecting accuracy) and redundancy (having units with similar activity).In a series of experiments with AlexNet, ResNet and Inception networks in the CIFAR-10 and ImageNet datasets, and also using shallow networks with synthetic data, we show that DNNs consistently increase either their robustness, their redundancy, or both at greater widths for a comprehensive set of hyperparameters.These results suggest that networks in the deep learning regime adjust their effective capacity by developing either robustness or redundancy.

Analizzare la robustezza e la ridondanza nelle deep neural network rivela le feature di limitazione della capacità che aiutano a spiegare il non-overfitting.


Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest.For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction.One key challenge is that such derivatives are themselves inherently unstable.In this paper, we propose a new learning problem to encourage deep networks to have stable derivatives over larger regions.While the problem is challenging in general, we focus on networks with piecewise linear activation functions.Our algorithm consists of an inference step that identifies a region around a point where linear approximation is provably stable, and an optimization step to expand such regions.We propose a novel relaxation to scale the algorithm to realistic models.We illustrate our method with residual and recurrent networks on image and sequence datasets.

Un algoritmo scalabile per stabilire derivate robuste di deep network rispetto agli input.


Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs).On one hand, increasing the density of CNNs by adding cross-layer connections achieve higher accuracy.On the other hand, creating sparsity structures through regularization and pruning methods enjoys lower computational costs.In this paper, we bridge these two by proposing a new network structure with locally dense yet externally sparse connections.This new structure uses dense modules, as basic building blocks and then sparsely connects these modules via a novel algorithm during the training process.Experimental results demonstrate that the locally dense yet externally sparse structure could acquire competitive performance on benchmark tasks (CIFAR10, CIFAR100, and ImageNet) while keeping the network structure slim.

In questo articolo, esploriamo una struttura internamente densa ma esternamente sparsa di deep neural network e analizziamo le sue proprietà chiave.


Statistical inference methods are fundamentally important in machine learning.Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI).However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias. In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation.The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency.We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.

In questo lavoro, miriamo a migliorare MCMC e VI con un nuovo metodo ibrido basato sull'idea di ridurre i bias di simulazione delle catene MCMC di lunghezza finita usando l'ottimizzazione basata sul gradiente.


We show that information about whether a neural network's output will be correct or incorrect is present in the outputs of the network's intermediate layers.To demonstrate this effect, we train a new "meta" network to predict from either the final output of the underlying "base" network or the output of one of the base network's intermediate layers whether the base network will be correct or incorrect for a particular input.We find that, over a wide range of tasks and base networks, the meta network can achieve accuracies ranging from 65% - 85% in making this determination.

Le informazioni sul fatto che l'output di una rete neurale sarà corretto o errato sono in qualche modo presenti negli output dei layer intermedi della rete.


We develop a new algorithm for imitation learning from a single expert demonstration.In contrast to many previous one-shot imitation learning approaches, our algorithm does not assume access to more than one expert demonstration during the training phase.Instead, we leverage an exploration policy to acquire unsupervised trajectories, which are then used to train both an encoder and a context-aware imitation policy.The optimization procedures for the encoder, imitation learner, and exploration policy are all tightly linked.This linking creates a feedback loop wherein the exploration policy collects new demonstrations that challenge the imitation learner, while the encoder attempts to help the imitation policy to the best of its abilities.We evaluate our algorithm on 6 MujoCo robotics tasks.

Algoritmo di self-imitation unsupervised capace di inferenza da una singola dimostrazione di un esperto.


Significant work has been dedicated to developing methods for communicating reasons for decision-making within au-tomated scheduling systems to human users.However, much less focus has been placed on communicating reasons for whyscheduling systems are unable to arrive at a feasible solution when over-constrained.We investigate this problem in thecontext of task scheduling.We introduce the agent resource-constrained project scheduling problem (ARCPSP), an ex-tension of the resource-constrained project scheduling problem which includes a conception of agents that execute tasksin parallel.We outline a generic framework, based on efficiently enumerating minimal unsatisfiable sets (MUS) andmaximal satisfiable sets (MSS), to produce small descriptions of the source of infeasibility.These descriptions are supple-mented with potential relaxations that would fix the infeasibility found within the problem instance.We illustrate howthis method may be applied to the ARCPSP and demonstrate how to generate different types of explanations for an over-constrained instance of the ARCPSP.

Sviluppiamo un framework per generare spiegazioni umanamente comprensibili del perché l'infattibilità si verifica in istanze con troppi constraint di una classe di problemi di scheduling con risorse limitate.


Adversarial perturbations cause a shift in the salient features of an image, which may result in a misclassification.We demonstrate that gradient-based saliency approaches are unable to capture this shift, and develop a new defense which detects adversarial examples based on learnt saliency models instead.We study two approaches: a CNN trained to distinguish between natural and adversarial images using the saliency masks produced by our learnt saliency model, and a CNN trained on the salient pixels themselves as its input.On MNIST, CIFAR-10 and ASSIRA, our defenses are able to detect various adversarial attacks, including strong attacks such as C&W and DeepFool, contrary to gradient-based saliency and detectors which rely on the input image.The latter are unable to detect adversarial images when the L_2- and L_infinity- norms of the perturbations are too small.Lastly, we find that the salient pixel based detector improves on saliency map based detectors as it is more robust to white-box attacks.

Mostriamo che i gradienti non sono in grado di catturare i saliency shift dovuti a perturbazioni adversarial e presentiamo una difesa adversarial alternativa utilizzando modelli di saliency appresi che è efficace sia contro gli attacchi black-box che white-box.


Disentangled encoding is an important step towards a better representation learning.However, despite the numerous efforts, there still is no clear winner that captures the independent features of the data in an unsupervised fashion.In this work we empirically evaluate the performance of six unsupervised disentanglement approaches on the mpi3d toy dataset curated and released for the NeurIPS 2019 Disentanglement Challenge.The methods investigated in this work are Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE.The capacities of all models were progressively increased throughout the training and the hyper-parameters were kept intact across experiments.The methods were evaluated based on five disentanglement metrics, namely, DCI, Factor-VAE, IRS, MIG, and SAP-Score.Within the limitations of this study, the Beta-TCVAE approach was found to outperform its alternatives with respect to the normalized sum of metrics.However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.

Inadeguatezza delle metriche di disentanglement


Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward.In this paper, we aim to address this from a different angle.In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do.For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together.The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming.To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning.We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents.The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation.

Proponiamo il Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) per addestrare un manager a motivare i lavoratori self-interested a raggiungere una collaborazione ottimale, assegnando loro contratti adeguati.


Inferring temporally coherent data features is crucial for a large variety of learning tasks.We propose a network architecture that introduces temporal recurrent connections for the internal  state of the widely used residual blocks.We demonstrate that, with these connections, convolutional neural networks can more robustly learn stable temporal states that persist between evaluations.We demonstrate their potential for inferring high-quality super-resolution images from low resolution images produced with real-time renderers.This data arises in a wide range of applications,  and is particularly challenging as it contains a strongly aliased signal.Hence, the data differs substantially from the smooth inputs encountered  in natural videos, and existing techniques do not succeed at producing acceptable image quality.We additionally propose a series of careful adjustments of typical generative adversarial architectures for video super-resolution to arrive at a first model that can produce detailed, yet temporally coherent images from an aliased stream of inputs from a real-time renderer.

Un metodo per gli stati latenti persistenti in ResBlocks dimostrato per la super-risoluzione di sequenze di immagini aliased.


The backpropagation algorithm is the most popular algorithm training neural networks nowadays.However, it suffers from the forward locking, backward locking and update locking problems, especially when a neural network is so large that its layers are distributed across multiple devices.Existing solutions either can only handle one locking problem or lead to severe accuracy loss or memory inefficiency.Moreover, none of them consider the straggler problem among devices.In this paper, we propose \textbf{Layer-wise Staleness} and a novel efficient training algorithm, \textbf{Diversely Stale Parameters} (DSP), which can address all these challenges without loss of accuracy nor memory issue.We also analyze the convergence of DSP with two popular gradient-based methods and prove that both of them are guaranteed to converge to critical points for non-convex problems.Finally, extensive experimental results on training deep convolutional neural networks demonstrate that our proposed DSP algorithm can achieve significant training speedup with stronger robustness and better generalization than compared methods.

Proponiamo Diversely Stale Parameters per rompere i blocchi dell'algoritmo di backpropoagation e formare una CNN in parallelo.


The emergence of language in multi-agent settings is a promising research direction to ground natural language in simulated agents.If AI would be able to understand the meaning of language through its using it, it could also transfer it to other situations flexibly.That is seen as an important step towards achieving general AI.The scope of emergent communication is so far, however, still limited.It is necessary to enhance the learning possibilities for skills associated with communication to increase the emergable complexity.We took an example from human language acquisition and the importance of the empathic connection in this process.We propose an approach to introduce the notion of empathy to multi-agent deep reinforcement learning.We extend existing approaches on referential games with an auxiliary task for the speaker to predict the listener's mind change improving the learning time.Our experiments show the high potential of this architectural element by doubling the learning speed of the test setup.

Un task ausiliario di predizione può accelerare l'apprendimento nei setup di language emergence.


Image paragraph captioning is the task of automatically generating multiple sentences for describing images in grain-fined and coherent text.Existing typical deep learning-based models for image captioning consist of an image encoder to extract visual features and a language model decoder, which has shown promising results in single high-level sentence generation.However, only the word-level scalar guiding signal is available when the image encoder is optimized to extract visual features.The inconsistency between the parallel extraction of visual features and sequential text supervision limits its success when the length of the generated text is long (more than 50 words).In this paper, we propose a new module, called the Text Embedding Bank (TEB) module, to address the problem for image paragraph captioning.This module uses the paragraph vector model to learn fixed-length feature representations from a variable-length paragraph.We refer to the fixed-length feature as the TEB.This TEB module plays two roles to benefit paragraph captioning performance.First, it acts as a form of global and coherent deep supervision to regularize visual feature extraction in the image encoder.Second, it acts as a distributed memory to provide features of the whole paragraph to the language model, which alleviating the long-term dependency problem.Adding this module to two existing state-of-the-art methods achieves a new state-of-the-art result by a large margin on the paragraph captioning Visual Genome dataset.


Learning Mahalanobis metric spaces is an important problem that has found numerous applications.Several algorithms have been designed for this problem, including Information Theoretic Metric Learning (ITML) [Davis et al. 2007] and Large Margin Nearest Neighbor (LMNN) classification [Weinberger and Saul 2009].  We consider a formulation of Mahalanobis metric learning as an optimization problem,where the objective is to minimize the number of violated similarity/dissimilarity constraints.  We show that for any fixed ambient dimension, there exists a fully polynomial time approximation scheme (FPTAS) with nearly-linear running time.This result is obtained using tools from the theory of linear programming in low dimensions.We also discuss improvements of the algorithm in practice, and present experimental results on synthetic and real-world data sets.Our algorithm is fully parallelizable and performs favorably in the presence of adversarial noise.

Algoritmo di metric learning completamente parallelizzabile e resistente al rumore adversarial con garanzie teoriche.


Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”).While such tasks are useful to verify that a machine understands the content of an image,  they are not engaging to humans as captions.   With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits.We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits.  We build models that combine existing work from (i) sentence representations (Mazaré et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images.  We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task.Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

Sviluppiamo modelli di image captioning coinvolgenti condizionati dalla personalità che sono anche allo stato dell'arte su task di captioning regolari.


Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones.To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) to train ML models with differential privacy (DP) guarantees.At the core of DP-LSSGD is the Laplacian smoothing, which smooths out the Gaussian noise used in the Gaussian mechanism.Under the same amount of noise used in the Gaussian mechanism, DP-LSSGD attains the same DP guarantee, but a better utility especially for the scenarios with strong DP guarantees.In practice, DP-LSSGD makes training both convex and nonconvex ML models more stable and enables the trained models to generalize better.The proposed algorithm is simple to implement and the extra computational complexity and memory overhead compared with DP-SGD are negligible.DP-LSSGD is applicable to train a large variety of ML models, including DNNs.

Proponiamo uno stochastic gradient descent con smoothing laplaciano differenzialmente privato per addestrare modelli di apprendimento automatico con una migliore utility e mantenere garanzie di privacy differenziale.


We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector$\theta_0\in\mathbb{R}^d$ \emph{uniformly} from $m$ quantized noisy measurements.Under the assumption that the measurements are sub-Gaussian,  to recover any $k$-sparse $\theta_0$ ($k\ll d$) \emph{uniformly} up to an error $\varepsilon$ with high probability, the best known computationally tractable algorithm requires\footnote{Here, an algorithm is ``computationally tractable'' if it has provable convergence guarantees.The notation $\tilde{\mathcal{O}}(\cdot)$ omits a logarithm factor of $\varepsilon^{-1}$.} $m\geq\tilde{\mathcal{O}}(k\log d/\varepsilon^4)$.In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$.Such a framework poses low-dimensional priors on $\theta_0$ without a known basis.We propose to recover the target $G(x_0)$ via an unconstrained empirical risk minimization (ERM) problem under a much weaker \emph{sub-exponential  measurement assumption}. For such a problem, we establish a joint statistical and computational analysis. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of $m=\tilde{\mathcal{O}} (kn\log d /\epsilon^2)$ recovering any $G(x_0)$ uniformly up to an error $\varepsilon$.Moreover, from the lens of computation, we prove that under proper conditions on the ReLU weights, our proposed empirical risk, despite non-convexity, has no stationary point outside of small neighborhoods around the true representation $x_0$ and its negative multiple.Furthermore, we show that the global minimizer of the empirical risk stays within the neighborhood around $x_0$ rather than its negative multiple.Our analysis sheds some light on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.

Forniamo un'analisi statistica e computazionale del problema del rilevamento compresso a un bit con un prior generativo.


We introduce an unsupervised structure learning algorithm for deep, feed-forward, neural networks.We propose a new interpretation for depth and inter-layer connectivity where a hierarchy of independencies in the input distribution is encoded in the network structure.This results in structures allowing neurons to connect to neurons in any deeper layer skipping intermediate layers.Moreover, neurons in deeper layers encode low-order (small condition sets) independencies and have a wide scope of the input, whereas neurons in the first layers encode higher-order (larger condition sets) independencies and have a narrower scope.Thus, the depth of the network is automatically determined---equal to the maximal order of independence in the input distribution, which is the recursion-depth of the algorithm.The proposed algorithm constructs two main graphical models:1) a generative latent graph (a deep belief network) learned from data and2) a deep discriminative graph constructed from the generative latent graph.We prove that conditional dependencies between the nodes in the learned generative latent graph are preserved in the class-conditional discriminative graph.Finally, a deep neural network structure is constructed based on the discriminative graph.We demonstrate on image classification benchmarks that the algorithm replaces the deepest layers (convolutional and dense layers) of common convolutional networks, achieving high classification accuracy, while constructing significantly smaller structures.The proposed structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.

Un approccio di principio per l'apprendimento della frameworkdelle deep neural network con una nuova interpretazione della profondità e della connettività interlayer.


L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions.However, imposing strong L1 or L2 regularization with gradient descent method easily fails, and this limits the generalization ability of the underlying neural networks.To understand this phenomenon, we investigate how and why training fails for strong regularization.Specifically, we examine how gradients change over time for different regularization strengths and provide an analysis why the gradients diminish so fast.We find that there exists a tolerance level of regularization strength, where the learning completely fails if the regularization strength goes beyond it.We propose a simple but novel method, Delayed Strong Regularization, in order to moderate the tolerance level.Experiment results show that our proposed approach indeed achieves strong regularization for both L1 and L2 regularizers and improves both accuracy and sparsity on public data sets.Our source code is published.

Studiamo come e perché una forte regolarizzazione L1/L2 fallisce e proponiamo un metodo che può implementare una forte regolarizzazione.


Despite neural network’s high performance, the lack of interpretability has been the main bottleneck for its safe usage in practice.In domains with high stakes (e.g., medical diagnosis), gaining insights into the network is critical for gaining trust and being adopted.One of the ways to improve interpretability of a NN is to explain the importance of a particular concept (e.g., gender) in prediction.This is useful for explaining reasoning behind the networks’ predictions, and for revealing any biases the network may have.This work aims to provide quantitative answers to \textit{the relative importance of concepts of interest} via concept activation vectors (CAV).In particular, this framework enables non-machine learning experts to express concepts of interests and  test hypotheses using examples (e.g., a set of pictures that illustrate  the concept).We show that CAV can be learned given a relatively small set of examples.Testing with CAV, for example, can answer whether a particular concept (e.g., gender) is more important in predicting a given class (e.g., doctor) than other set of concepts.Interpreting with CAV does not require any retraining or modification of the network.We show that many levels of meaningful concepts are learned (e.g., color, texture, objects, a person’s occupation), and we present CAV’s \textit{empirical deepdream} — where we maximize an activation using a set of example pictures.We show how various insights can be gained from the relative importance testing with CAV.

Questo lavoro mira a fornire risposte quantitative all'importanza relativa dei concetti di interesse attraverso i concept activator vector (CAV). In particolare, questo framework permette agli esperti di non-machine learning di esprimere concetti di interesse e di testare le ipotesi utilizzando esempi (ad esempio, un insieme di immagini che illustrano il concetto). Mostriamo che CAV può essere appreso con un insieme relativamente piccolo di esempi. Hypothesis testing con CAV può dire se un particolare concetto (ad esempio, il genere) è più importante nel predire una data classe (ad esempio, il medico) rispetto ad altri insiemi di concetti. L'interpretazione delle reti con CAV non richiede alcun retraining o modifica della rete.


We present a new family of objective functions, which we term the Conditional Entropy Bottleneck (CEB).These objectives are motivated by the Minimum Necessary Information (MNI) criterion.We demonstrate the application of CEB to classification tasks.We show that CEB gives: well-calibrated predictions; strong detection of challenging out-of-distribution examples and powerful whitebox adversarial examples; and substantial robustness to those adversaries.Finally, we report that CEB fails to learn from information-free datasets, providing a possible resolution to the problem of generalization observed in Zhang et al. (2016).

Il Conditional Entropy Bottleneck è una funzione obiettivo della teoria dell'informazione per l'apprendimento di rappresentazioni ottimali.


Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification.Retrieval of such  representations from a large database is however computationally challenging.Approximate methods based on learning compact representations, have been widely explored for this problem, such as locality sensitive hashing, product quantization, and PCA.In this work, in contrast to learning compact representations, we propose to learn high dimensional and sparse representations that have similar representational capacity as dense embeddings while being more efficient due to sparse matrix multiplication operations which can be much faster than dense multiplication.Following the key insight that the number of operations decreases quadratically with the sparsity of embeddings provided the non-zero entries are distributed uniformly across dimensions, we propose a novel approach to learn such distributed sparse embeddings via the use of a carefully constructed regularization function that directly minimizes a continuous relaxation of the number of floating-point operations (FLOPs) incurred during retrieval.Our experiments show that our approach is competitive to the other baselines and yields a similar or better speed-vs-accuracy tradeoff on practical datasets.

Proponiamo un approccio per imparare rappresentazioni sparse ad alta dimensionalità che sono veloci da cercare, incorporando un surrogato del numero di operazioni direttamente nella loss.


Model interpretability and systematic, targeted model adaptation present central challenges in deep learning.In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning.Our contributions are two-fold.Firstly, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate.In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses.Secondly, we deploy the stethoscope framework to provide an in-depth analysis of a state-of-the-art deep neural network for stability prediction, specifically examining its physical reasoning.We show that the baseline model is susceptible to being misled by incorrect visual cues.This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability.Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy.Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset.Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.

Combinazione di auxiliary and adversarial training per interrogare e aiutare la comprensione fisica.


Flow based models such as Real NVP are an extremely powerful approach to density estimation.However, existing flow based models are restricted to transforming continuous densities over a continuous input space into similarly continuous distributions over continuous latent variables.This makes them poorly suited for modeling and representing discrete structures in data distributions, for example class membership or discrete symmetries.To address this difficulty, we present a normalizing flow architecture which relies on domain partitioning using locally invertible functions, and possesses both real and discrete valued latent variables.  This Real and Discrete (RAD) approach retains the desirable normalizing flow properties of exact sampling, exact inference, and analytically computable probabilities, while at the same time allowing simultaneous modeling of both continuous and discrete structure in a data distribution.

Modelli basati sul flusso, ma non invertibili, per imparare anche le variabili discrete


We investigate the loss surface of neural networks.We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases.Our results thus indicate that in general "no spurious local minim" is a property limited to deep linear networks, and insights obtained from linear networks may not be robust.Specifically, for ReLU(-like) networks we constructively prove that for almost all practical datasets there exist infinitely many local minima.We also present a counterexample for more general activations (sigmoid, tanh, arctan, ReLU, etc.), for which there exists a bad local minimum.Our results make the least restrictive assumptions relative to existing results on spurious local optima in neural networks.We complete our discussion by presenting a comprehensive characterization of global optimality for deep linear networks, which unifies other results on this topic.

Dimostriamo costruttivamente che anche le minime funzioni di attivazione non lineari introducono minimi locali spuri, per dataset e funzioni di attivazione generali.


The tasks that an agent will need to solve often aren’t known during training.However, if the agent knows which properties of the environment we consider im- portant, then after learning how its actions affect those properties the agent may be able to use this knowledge to solve complex tasks without training specifi- cally for them.Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest.We propose a model that learns a policy for transitioning between “nearby” sets of attributes, and maintains a graph of possible transitions.Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan.We show in grid-world games and 3D block stacking that our model is able to generalize to longer, more complex tasks at test time even when it only sees short, simple tasks at train time.

Pianificazione compositiva basata sugli attributi che si generalizza a task di prova lunghi, nonostante sia stata addestrata su task brevi e semplici.


The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods.Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks.However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once.For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels.If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.  This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs.In some domains, this makes meta-learning entirely inapplicable.In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation.This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input.By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult.Our approach substantially outperforms standard meta-learning algorithms in these settings.

Identifichiamo e formalizziamo il problema della memorizzazione nel meta-learning e risolviamo questo problema con un nuovo metodo di meta-regolarizzazione, che amplia notevolmente i domini in cui il meta-learning può essere applicabile e risultare efficace.


Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning.Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better initialisations or scaling factors for a gradient-based update rule.Both these approaches pose challenges.On one hand, directly producing an update forgoes a useful inductive bias and can easily lead to non-converging behaviour.On the other hand, approaches that try to control a gradient-based update rule typically resort to computing gradients through the learning process to obtain their meta-gradients, leading to methods that can not scale beyond few-shot task adaptation.In this work we propose Warped Gradient Descent (WarpGrad), a method that intersects these approaches to mitigate their limitations.WarpGrad meta-learns an efficiently parameterised preconditioning matrix that facilitates gradient descent across the task distribution.Preconditioning arises by interleaving non-linear layers, referred to as warp-layers, between the layers of a task-learner.Warp-layers are meta-learned without backpropagating through the task training process in a manner similar to methods that learn to directly produce updates.WarpGrad is computationally efficient, easy to implement, and can scale to arbitrarily large meta-learning problems.We provide a geometrical interpretation of the approach and evaluate its effectiveness in a variety of settings, including few-shot, standard supervised, continual and reinforcement learning.

Proponiamo un nuovo framework per il meta-learning di una regola di aggiornamento basata sul gradiente che va oltre il few-shot learning ed è applicabile a qualsiasi forma di apprendimento, compreso il continual learning.


In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification.However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images.We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks.Defense-GAN is trained to model the distribution of unperturbed images.At inference time, it finds a close output to a given image which does not contain the adversarial changes.This output is then fed to the classifier.Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure.It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples.We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.

Defense-GAN utilizza una Generative Adversarial Network per difendersi dagli attacchi white-box e black-box nei modelli di classificazione.


We study the problem of learning similarity functions over very large corpora using neural network embedding models.These models are typically trained using SGD with random sampling of unobserved pairs, with a sample size that grows quadratically with the corpus size, making it expensive to scale.We propose new efficient methods to train these models without having to sample unobserved pairs.Inspired by matrix factorization, our approach relies on adding a global quadratic penalty and expressing this term as the inner-product of two generalized Gramians.We show that the gradient of this term can be efficiently computed by maintaining estimates of the Gramians, and develop variance reduction schemes to improve the quality of the estimates.We conduct large-scale experiments that show a significant improvement both in training time and generalization performance compared to sampling methods.

Sviluppiamo metodi efficienti per addestrare modelli di embedding neurale con una struttura con dot-product, riformulando la funzione obiettivo in termini di matrici di Gram generalizzate e mantenendo le stime di queste matrici.


For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset.In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks.By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks.GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models.We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task.However, the low absolute performance of our best model indicates the need for improved general NLU systems.

Presentiamo un benchmark multi-task e una piattaforma di analisi per valutare la generalizzazione nei sistemi di natural language understanding.


A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success.This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit-assignment for interactions between actions and goals of different agents.To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.We use a function augmentation scheme to bridge value and policy functions across the curriculum.The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.

Un metodo modulare per il reinforcement learning multi-agente completamente cooperativo, basato sul curriculum learning per l'esplorazione efficiente e il credit assignment per le interazioni azione-obiettivo.


We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework.The first problem is implicit bias present in the reward functions used in these algorithms.While these biases might work well for some environments, they can also lead to sub-optimal behavior in others.Secondly, even though these algorithms can learn from few expert demonstrations, they require a prohibitively large number of interactions with the environment in order to imitate the expert for many real-world applications.In order to address these issues, we propose a new algorithm called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning to reduce policy-environment interaction sample complexity by an average factor of 10.Furthermore, since our reward function is designed to be unbiased, we can apply our algorithm to many problems without making any task-specific adjustments.

Affrontiamo la sample inefficiency e il bias della reward negli algoritmi di adversarial imitation learning come GAIL e AIRL.


Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB.Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable.Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points.In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks.The model is trained using contrastive loss with l2-normalized capsule encoded pose features.We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

Una variante delle capsule network che può essere usata per task di pairwise learning. I risultati mostrano che le Siamese Capsule Network funzionano bene nel setting di few shot learning.


Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack.Such adaptation property strongly relies on cellular neuromodulation, the biological mechanism that dynamically controls neuron intrinsic properties and response to external stimuli in a context dependent manner.In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours.The network adaptation capabilities are tested on navigation benchmarks in a meta-learning context and compared with state-of-the-art approaches.Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.

Questo articolo introduce la neuromodulazione nelle reti neurali artificiali.


Convolution operator is the core of convolutional neural networks (CNNs) and occupies the most computation cost.To make CNNs more efficient, many methods have been proposed to either design lightweight networks or compress models.Although some efficient network structures have been proposed, such as MobileNet or ShuffleNet, we find that there still exists redundant information between convolution kernels.To address this issue, we propose a novel dynamic convolution method named \textbf{DyNet} in this paper, which can adaptively generate convolution kernels based on image contents.To demonstrate the effectiveness, we apply DyNet on multiple state-of-the-art CNNs.The experiment results show that DyNet can reduce the computation cost remarkably, while maintaining the performance nearly unchanged.Specifically, for ShuffleNetV2 (1.0), MobileNetV2 (1.0), ResNet18 and ResNet50, DyNet reduces 40.0%, 56.7%, 68.2% and 72.4% FLOPs respectively while the Top-1 accuracy on ImageNet only changes by +1.0%, -0.27%, -0.6% and -0.08%.Meanwhile, DyNet further accelerates the inference speed of MobileNetV2 (1.0), ResNet18 and ResNet50 by 1.87x,1.32x and 1.48x on CPU platform respectively.To verify the scalability, we also apply DyNet on segmentation task, the results show that DyNet can reduces 69.3% FLOPs while maintaining the Mean IoU on segmentation task.

Proponiamo un metodo di convoluzione dinamica per accelerare significativamente il tempo di inferenza delle CNN mantenendo la precisione.


Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements.In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values.We show that this method is very flexible and that many other techniques can be seen as special cases of LUT-Q.For example, we can constrain the dictionary trained with LUT-Q to generate networks with pruned weight matrices or restrict the dictionary to powers-of-two to avoid the need for multiplications.In order to obtain fully multiplier-less networks, we also introduce a multiplier-less version of batch normalization.Extensive experiments on image recognition and object detection tasks show that LUT-Q consistently achieves better performance than other methods with the same quantization bitwidth.

In questo articolo introduciamo un metodo di training, chiamato look-up table quantization (LUT-Q), che impara un dizionario e assegna ogni peso a uno dei valori del dizionario


An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer.  In this paper, we revisit the problem of negative transfer in multitask setting and find that its corrosive effects are applicable to a wide range of linear and non-linear models, including neural networks.We first study the effects of negative transfer in a principled way and show that previously proposed counter-measures are insufficient, particularly for trainable features.We propose an adversarial training approach to mitigate the effects of negative transfer by viewing the problem in a domain adaptation setting.Finally, empirical results on attribute prediction multi-task on AWA and CUB datasets further validate the need for correcting negative sharing in an end-to-end manner.

Guardiamo al transfer negativo da un punto di vista di domain adaptation per derivare un algoritmo di adversarial training.


Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks.In this paper, we introduce free-energy-based reinforcement learning (FERL) as an application of quantum hardware.We propose a method for processing a quantum annealer’s measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM).We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer.The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.

Addestriamo le Quantum Boltzmann Machine usando un metodo di replica stacking e un annealer quantistico per eseguire un task di reinforcement learning.


Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs.However, under the black-box setting, most existing adversaries often have a poor transferability to attack other defense models.In this work, from the perspective of regarding the adversarial example generation as an optimization process, we propose two new methods to improve the transferability of adversarial examples, namely Nesterov Iterative Fast Gradient Sign Method (NI-FGSM) and Scale-Invariant attack Method (SIM).NI-FGSM aims to adapt Nesterov accelerated gradient into the iterative attacks so as to effectively look ahead and improve the transferability of adversarial examples.While SIM is based on our discovery on the scale-invariant property of deep learning models, for which we leverage to optimize the adversarial perturbations over the scale copies of the input images so as to avoid "overfitting” on the white-box model being attacked and generate more transferable adversarial examples.NI-FGSM and SIM can be naturally integrated to build a robust gradient-based attack to generate more transferable adversarial examples against the defense models.Empirical results on ImageNet dataset demonstrate that our attack methods exhibit higher transferability and achieve higher attack success rates than state-of-the-art gradient-based attacks.

Abbiamo proposto un Nesterov Iterative Fast Gradient Sign Method (NI-FGSM) e un Scale-Invariant attack Method (SIM) che può aumentare la trasferibilità degli adversarial example per la classificazione delle immagini.


Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks.In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called PBNet.By embracing stochasticity during training, we circumvent the need to approximate the gradient of functions for which the derivative is zero almost always, such as $\textrm{sign}(\cdot)$, while still obtaining a fully Binary Neural Network at test time.Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution.Since all operations in a layer of the PBNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time.  We evaluate two related training methods for the PBNet: one in which activation distributions are propagated throughout the network, and one in which binary activations are sampled in each layer.Our experiments indicate that sampling the binary activations is an important element for stochastic training of binary Neural Networks.

Introduciamo un metodo di training stocastico per il training di reti neurali binarie con pesi e attivazioni binarie.


The goal of generative models is to model the underlying data distribution of asample based dataset.Our intuition is that an accurate model should in principlealso include the sample based dataset as part of its induced probability distribution.To investigate this, we look at fully trained generative models using the GenerativeAdversarial Networks (GAN) framework and analyze the resulting generatoron its ability to memorize the dataset.Further, we show that the size of the initiallatent space is paramount to allow for an accurate reconstruction of the trainingdata.This gives us a link to compression theory, where Autoencoders (AE) areused to lower bound the reconstruction capabilities of our generative model.Here,we observe similar results to the perception-distortion tradeoff (Blau & Michaeli(2018)).Given a small latent space, the AE produces low quality and the GANproduces high quality outputs from a perceptual viewpoint.In contrast, the distortionerror is smaller for the AE.By increasing the dimensionality of the latentspace the distortion decreases for both models, but the perceptual quality onlyincreases for the AE.

Analizziamo l'impatto dello spazio latente dei generatori completamente addestrati tramite pseudo inversione.


We address the problem of open-set authorship verification, a classification task that consists of attributing texts of unknown authorship to a given author when the unknown documents in the test set are excluded from the training set.We present an end-to-end model-building process that is universally applicable to a wide variety of corpora with little to no modification or fine-tuning.It relies on transfer learning of a deep language model and uses a generative adversarial network and a number of text augmentation techniques to improve the model's generalization ability.The language model encodes documents of known and unknown authorship into a domain-invariant space, aligning document pairs as input to the classifier, while keeping them separate.The resulting embeddings are used to train to an ensemble of recurrent and quasi-recurrent neural networks.The entire pipeline is bidirectional; forward and backward pass results are averaged.We perform experiments on four traditional authorship verification datasets, a collection of machine learning papers mined from the web, and a large Amazon-Reviews dataset.Experimental results surpass baseline and current state-of-the-art techniques, validating the proposed approach.

Proponiamo un processo di costruzione di un modello end-to-end che è universalmente applicabile a un'ampia varietà di corpora di verifica dell'authorship e supera lo stato dell'arte con poche o nessuna modifica o fine-tuning.


We consider tackling a single-agent RL problem by distributing it to $n$ learners.These learners, called advisors, endeavour to solve the problem from a different focus.Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system.We show that the local planning method for the advisors is critical and that none of the ones found in the literature is flawless: the \textit{egocentric} planning overestimates values of states where the other advisors disagree, and the \textit{agnostic} planning is inefficient around danger zones.We introduce a novel approach called \textit{empathic} and discuss its theoretical aspects.We empirically examine and validate our theoretical findings on a fruit collection task.

Consideriamo di affrontare un problema di RL a singolo agente distribuendolo a $n$ apprendisti.


We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise.By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way.Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.

Un modello ibrido che utilizza sia l'audio grezzo che le informazioni dello spettrogramma per task di speech enhancement.


Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science.This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms.After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size.We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances).Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah.We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.

Questo articolo confronta i test statistici per i confronti RL (falsi positivi, potenza statistica), controlla la robustezza delle ipotesi usando distribuzioni simulate e distribuzioni empiriche (SAC, TD3), fornisce linee guida per studenti e ricercatori di RL.


In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation?In this paper, we set out to answer these questions by studying multiple phase transitions in the IB objective: IB_β[p(z|x)] = I(X; Z) − βI(Y; Z) defined on the encoding distribution p(z|x) for input X, target Y and representation Z, where sudden jumps of dI(Y; Z)/dβ and prediction accuracy are observed with increasing β.We introduce a definition for IB phase transitions as a qualitative change of the IB loss landscape, and show that the transitions correspond to the onset of learning new classes.Using second-order calculus of variations, we derive a formula that provides a practical condition for IB phase transitions, and draw its connection with the Fisher information matrix for parameterized models.We provide two perspectives to understand the formula, revealing that each IB phase transition is finding a component of maximum (nonlinear) correlation between X and Y orthogonal to the learned representation, in close analogy with canonical-correlation analysis (CCA) in linear settings.Based on the theory, we present an algorithm for discovering phase transition points.Finally, we verify that our theory and algorithm accurately predict phase transitions in categorical datasets, predict the onset of learning new classes and class difficulty in MNIST, and predict prominent phase transitions in CIFAR10.

Diamo un'analisi teorica dell'obiettivo dell'Information Bottleneck per comprendere e prevedere le transizioni di fase osservate nel tradeoff tra predizione e compressione.


We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks.The local adaptation of activation function is achieved by introducing scalable hyper-parameters in each layer (layer-wise) and for every neuron separately (neuron-wise), and then optimizing it using the stochastic gradient descent algorithm.Introduction of neuron-wise activation function acts like a vector activation function as opposed to the traditional scalar activation function given by fixed, global and layer-wise activations.In order to further increase the training speed, an activation slope based slope recovery term is added in the loss function, which further accelerate convergence, thereby reducing the training cost.For numerical experiments, a nonlinear discontinuous function is approximated using a deep neural network with layer-wise and neuron-wise locally adaptive activation functions with and without the slope recovery term and compared with its global counterpart.Moreover, solution of the nonlinear Burgers equation, which exhibits steep gradients, is also obtained using the proposed methods.On the theoretical side, we prove that in the proposed method the gradient descent algorithms are not attracted to sub-optimal critical points or local minima under practical conditions on the initialization and learning rate.Furthermore, the proposed adaptive activation functions with the slope recovery are shown to accelerate the training process in standard deep learning benchmarks using CIFAR-10, CIFAR-100, SVHN, MNIST, KMNIST, Fashion-MNIST, and Semeion data sets with and without data augmentation.

Proporre funzioni di attivazione localmente adattive in deep neural network e informate sulla fisica per una convergenza più veloce


Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function.The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called Type II maximum likelihood or ML-II). An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call Fully Bayesian Gaussian Process Regression (GPR). This work considers two approximations to the intractable hyperparameter posterior, 1) Hamiltonian Monte Carlo (HMC) yielding a sampling based approximation and2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full rank Gaussian accounting for correlations between hyperparameters.We analyse the predictive performance for fully Bayesian GPR on a range of benchmark data sets.

Analisi dell'inferenza bayesiana degli iperparametri nella regressione del processo gaussiano


We consider the problem of using variational latent-variable models for data compression.For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent representation needs to be subjected to entropy coding.Range coding as an entropy coding technique is optimal, but it can fail catastrophically if the computation of the prior differs even slightly between the sending and the receiving side.Unfortunately, this is a common scenario when floating point math is used and the sender and receiver operate on different hardware or software platforms, as numerical round-off is often platform dependent.We propose using integer networks as a universal solution to this problem, and demonstrate that they enable reliable cross-platform encoding and decoding of images using variational models.

Alleniamo modelli variazionali con reti quantizzate per il determinismo computazionale. Questo permette di usarli per la compressione dati cross-platform.


Neural networks powered with external memory simulate computer behaviors.These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks.In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures.The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine.A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.

Una simulazione neurale della macchina di Turing universale


It is common practice to decay the learning rate.Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training.This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam.It reaches equivalent test accuracies after the same number of training epochs, but with fewer parameter updates, leading to greater parallelism and shorter training times.We can further reduce the number of parameter updates by increasing the learning rate $\epsilon$ and scaling the batch size $B \propto \epsilon$.Finally, one can increase the momentum coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly reduce the test accuracy.Crucially, our techniques allow us to repurpose existing training schedules for large batch training with no hyper-parameter tuning.We train ResNet-50 on ImageNet to 76.1% validation accuracy in under 30 minutes.

Diminuire il learning rate e aumentare la dimensione del batch durante l'allenamento sono equivalenti.


Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.We propose a mixed objective that combines cross entropy loss with self-critical policy learning, using rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective.In addition to the mixed objective, we introduce a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks.Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies.On the Stanford Question Answering Dataset, our model achieves state of the art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.

Introduciamo la DCN+ con coattention profonda dei residui e RL a obiettivi misti, che raggiunge prestazioni allo stato dell'arte su Stanford Question Answering Dataset.


Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years.It could be time to take a step back and analyze the good and bad aspects in the field of NAS.A variety of algorithms search architectures under different search space.These searched architectures are trained using different setups, e.g., hyper-parameters, data augmentation, regularization.This raises a comparability problem when comparing the performance of various NAS algorithms.NAS-Bench-101 has shown success to alleviate this problem.In this work, we propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information.NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms.The design of our search space is inspired by the one used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.Each edge here is associated with an operation selected from a predefined operation set.For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes all possible architectures generated by 4 nodes and 5 associated operation options, which results in 15,625 neural cell candidates in total.The training log using the same setup and the performance for each architecture candidate are provided for three datasets.This allows researchers to avoid unnecessary repetitive training for selected architecture and focus solely on the search algorithm itself.The training time saved for every architecture also largely improves the efficiency of most NAS algorithms and presents a more computational cost friendly NAS community for a broader range of researchers.We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.In further support of the proposed NAS-Bench-102, we have analyzed it from many aspects and benchmarked 10 recent NAS algorithms, which verify its applicability.

Un benchmark NAS applicabile a quasi tutti gli algoritmi NAS.


Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions.However, generalization properties of GANs have not been well understood.In this paper, we analyze the generalization of GANs in practical settings.We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator.We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator.The penalty guarantees the generalization and convergence of GANs.Experiments on synthetic and large scale datasets verify our theoretical analysis.

Proponiamo una zero-centered gradient penalty per migliorare la generalizzazione e la stabilità delle GAN


Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks".The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures.In this work we take a sober view of the current state of GANs from a practical perspective.We reproduce the current state of the art and go beyond fairly exploring the GAN landscape.We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.

Una visione sobria sullo stato attuale delle GAN da una prospettiva pratica


As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents.Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action.Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent.Our approach generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency.The first captures the impact of perturbation on the relative expected reward of the action to be explained.  The second downweights irrelevant features that alter the relative expected rewards of actions other than the action to be explained.  We compare our approach with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders).  We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.

Proponiamo un approccio model-agnostic per spiegare il comportamento di deep RL agent black-box, addestrato a giocare a giochi Atari e da tavolo, evidenziando le feature rilevanti di uno stato di input.


To understand the inner work of deep neural networks and provide possible theoretical explanations, we study the deep representations through the untrained, random weight CNN-DCN architecture.As a convolutional AutoEncoder, CNN indicates the portion of a convolutional neural network from the input to an intermediate convolutional layer, and DCN indicates the corresponding deconvolutional portion.As compared with DCN training for pre-trained CNN, training the DCN for random-weight CNN converges more quickly and yields higher quality image reconstruction.Then, what happens for the overall random CNN-DCN?We gain intriguing results that the image can be reconstructed with good quality.To gain more insight on the intermediate random representation, we investigate the impact of network width versus depth, number of random channels, and size of random kernels on the reconstruction quality, and provide theoretical justifications on empirical observations.We further provide a fast style transfer application using the random weight CNN-DCN architecture to show the potential of our observation.

Indaghiamo la rappresentazione profonda delle architetture CNN-DCN non addestrate, a peso casuale, e mostriamo la loro qualità di ricostruzione delle immagini e le possibili applicazioni.


The current trade-off between depth and computational cost makes it difficult to adopt deep neural networks for many industrial applications, especially when computing power is limited.Here, we are inspired by the idea that, while deeper embeddings are needed to discriminate difficult samples, a large number of samples can be well discriminated via much shallower embeddings.In this study, we introduce the concept of decision gates (d-gate), modules trained to decide whether a sample needs to be projected into a deeper embedding or if an early prediction can be made at the d-gate, thus enabling the computation of dynamic representations at different depths.  The proposed d-gate modules can be integrated with any deep neural network and reduces the average computational cost of the deep neural networks while maintaining modeling accuracy.Experimental results show that leveraging the proposed d-gate modules led to a ~38% speed-up and ~39% FLOPS reduction on ResNet-101 and ~46% speed-up and $\sim$36\% FLOPS reduction on DenseNet-201 trained on the CIFAR10 dataset with only ~2% drop in accuracy.

Questo articolo introduce un nuovo approccio di rappresentazione dinamica delle feature per fornire un modo più efficiente per fare inferenza con le deep neural network.


This work provides an additional step in the theoretical understanding of neural networks.We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees.We empirically verify this and show that this does not hold when the initial conditions are chosen at random.The proof of convergence investigates the interaction between the two layers of the network.Our results highlight the importance of using symmetry in the design of neural networks.

Se inizializzate correttamente, le reti neurali possono imparare la semplice classe di funzioni simmetriche; se inizializzate in modo casuale, falliscono.


Architecture search aims at automatically finding neural architectures that are competitive with architectures designed by human experts.While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption; (2)most architecture search methods require vast computational resources.We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method.We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents.This is accomplished by using (approximate) network morphism operators for generating children.The combination of these two contributions allows finding models that are on par or even outperform different-sized NASNets, MobileNets, MobileNets V2 and Wide Residual Networks on CIFAR-10 and ImageNet64x64 within only one week on eight GPUs, which is about 20-40x less compute power than previous architecture search methods that yield state-of-the-art performance.

Proponiamo un metodo per un'efficiente ricerca multiobiettivo dell'architettura neurale basata sull'eredità lamarckiana e sugli algoritmi evolutivi.


We address two challenges of probabilistic topic modelling in order to better estimatethe probability of a word in a given context, i.e., P(wordjcontext) : (1) NoLanguage Structure in Context: Probabilistic topic models ignore word order bysummarizing a given context as a “bag-of-word” and consequently the semanticsof words in the context is lost.In this work, we incorporate language structureby combining a neural autoregressive topic model (TM) with a LSTM based languagemodel (LSTM-LM) in a single probabilistic framework.The LSTM-LMlearns a vector-space representation of each word by accounting for word orderin local collocation patterns, while the TM simultaneously learns a latent representationfrom the entire document.In addition, the LSTM-LM models complexcharacteristics of language (e.g., syntax and semantics), while the TM discoversthe underlying thematic structure in a collection of documents.We unite two complementaryparadigms of learning the meaning of word occurrences by combininga topic model and a language model in a unified probabilistic framework, namedas ctx-DocNADE.(2) Limited Context and/or Smaller training corpus of documents:In settings with a small number of word occurrences (i.e., lack of context)in short text or data sparsity in a corpus of few documents, the application of TMsis challenging.We address this challenge by incorporating external knowledgeinto neural autoregressive topic models via a language modelling approach: weuse word embeddings as input of a LSTM-LM with the aim to improve the wordtopicmapping on a smaller and/or short-text corpus.The proposed DocNADEextension is named as ctx-DocNADEe.We present novel neural autoregressive topic model variants coupled with neurallanguage models and embeddings priors that consistently outperform state-of-theartgenerative topic models in terms of generalization (perplexity), interpretability(topic coherence) and applicability (retrieval and classification) over 6 long-textand 8 short-text datasets from diverse domains.

Modello neurale unificato di topic e language modeling per introdurre la struttura della lingua nei topic model per topic vector contestualizzati


The large memory requirements of deep neural networks strain the capabilities of many devices, limiting their deployment and adoption.Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization.In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques.The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors.Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496x; with the same model accuracy, this results in up to a 1.51x improvement over the state-of-the-art.

Proponiamo un nuovo modo di comprimere le reti neurali usando strutture dati probabilistiche.


Recent work has shown that contextualized word representations derived from neural machine translation (NMT) are a viable alternative to such from simple word predictions tasks.This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more comprehensive.Unfortunately, computational and memory limitations as of present prevent NMT models from using large word vocabularies, and thus alternatives such as subword units (BPE and morphological segmentations) and characters have been used.Here we study the impact of using different kinds of units on the quality of the resulting representations when used to model syntax, semantics, and morphology.  We found that while representations derived from subwords are slightly better for modeling syntax, character-based representations are superior for modeling morphology and are also more robust to noisy input.

Studiamo l'impatto dell'uso di diversi tipi di di subword unit sulla qualità delle rappresentazioni risultanti quando sono usate per modellare la sintassi, la semantica e la morfologia.


Few-shot learning is the process of learning novel classes using only a few examples and it remains a challenging task in machine learning.Many sophisticated few-shot learning algorithms have been proposed based on the notion that networks can easily overfit to novel examples if they are simply fine-tuned using only a few examples.In this study, we show that in the commonly used low-resolution mini-ImageNet dataset, the fine-tuning method achieves higher accuracy than common few-shot learning algorithms in the 1-shot task and nearly the same accuracy as that of the state-of-the-art algorithm in the 5-shot task.We then evaluate our method with more practical tasks, namely the high-resolution single-domain and cross-domain tasks.With both tasks, we show that our method achieves higher accuracy than common few-shot learning algorithms.We further analyze the experimental results and show that:1) the retraining process can be stabilized by employing a low learning rate,2) using adaptive gradient optimizers during fine-tuning can increase test accuracy, and3) test accuracy can be improved by updating the entire network when a large domain-shift exists between base and novel classes.

Uno studio empirico che fornisce una nuova prospettiva su few shot learning, in cui un metodo di fine-tuning mostra un'accuratezza paragonabile ai metodi più complessi allo stato dell'arte in diversi task di classificazione.


We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.

Proponiamo un approccio per generare dati di mercato azionario realistici e ad alta fedeltà basati su adversarial generative network.


We present a novel black-box adversarial attack algorithm with state-of-the-art model evasion rates for query efficiency under $\ell_\infty$ and $\ell_2$ metrics.It exploits a \textit{sign-based}, rather than magnitude-based, gradient estimation approach that shifts the gradient estimation from continuous to binary black-box optimization.It adaptively constructs queries to estimate the gradient, one query relying upon the previous, rather than re-estimating the gradient each step with random query construction.Its reliance on sign bits yields  a smaller memory footprint and it requires neither hyperparameter tuning or dimensionality reduction.Further, its theoretical performance is guaranteed and it can characterize  adversarial subspaces better than white-box gradient-aligned subspaces.On two public black-box attack challenges and a model robustly trained against transfer attacks, the algorithm's evasion rates surpass all submitted attacks.For a suite of published models,  the algorithm is $3.8\times$ less failure-prone while spending $2.5\times$  fewer queries versus the best combination of state of art algorithms.For example, it evades a standard MNIST model using just $12$ queries on average.Similar performance is observed on a standard IMAGENET model with an average of $579$ queries.

Presentiamo un approccio di stima del gradiente basato sul segno, piuttosto che sulla magnitudine, che sposta la stima del gradiente dall'ottimizzazione black-box continua a quella binaria.