Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence.Verification of networks enables us to gauge their vulnerability to such adversarial examples.We formulate verification of piecewise-linear neural networks as a mixed integer program.On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art.We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available.The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier.In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-âˆž norm Îµ=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder.Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.

Verifichiamo in modo efficiente la robustezza dei modelli neurali deep con oltre 100.000 ReLU, certificando più sample dello stato dell'arte e trovando più adversarial example rispetto ad un forte first-order attack.

Esegue un attento studio degli approcci di programmazione lineare intera mista per verificare la robustezza delle reti neurali alle adversarial perturbations e propone tre miglioramenti alle formulazioni MILP della verifica delle reti neurali.


Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it.None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it.To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout.They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods.Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods.Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.

Un insieme di metodi per ottenere la stima dell'incertezza di qualsiasi modello dato senza ri-progettazione, re-training o fine-tuning.

Descrive diversi approcci per misurare l'incertezza nelle reti neurali arbitrarie quando c'è un'assenza di distorsione durante il training.


Capturing spatiotemporal dynamics is an essential topic in video recognition.In this paper, we present learnable higher-order operation as a generic family of building blocks for capturing higher-order correlations from high dimensional input video space.We prove that several successful architectures for visual classification tasks are in the family of higher-order neural networks, theoretical and experimental analysis demonstrates their underlying mechanism is higher-order.  On the task of video recognition, even using RGB only without fine-tuning with other video datasets, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on both Something-Something (V1 and V2) and Charades datasets.

Operazione di ordine superiore proposta per context learning

Propone un nuovo blocco di convoluzione 3D che effettua la convoluzione tra l'input video e il suo contesto, basato sull'ipotesi che il contesto rilevante sia presente intorno all'oggetto dell'immagine.


Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures.The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data.Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule.We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule.With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data.For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.

I modelli basati sulla consistency per l'apprendimento semi-supervised non convergono verso un singolo punto, ma continuano ad esplorare un insieme vario di soluzioni plausibili sul perimetro di una regione piatta. Il weight averaging aiuta a migliorare le prestazioni di generalizzazione.

L'articolo propone di applicare lo Stochastic Weight Averaging al contesto dell'apprendimento semi-supervised, sostenendo che i modelli MT/Pi semi-supervisionati sono particolarmente adatti a SWA e propone SWA veloce per accelerare il training.


In this paper, we find that by designing a novel loss function entitled, ''tracking loss'', Convolutional Neural Network (CNN) based object detectors can be successfully converted to well-performed visual trackers without any extra computational cost.This property is preferable to visual tracking where annotated video sequences for training are always absent, because rich features learned by detectors from still images could be utilized by dynamic trackers.It also avoids extra machinery such as feature engineering and feature aggregation proposed in previous studies.Tracking loss achieves this property by exploiting the internal structure of feature maps within the detection network and treating different feature points discriminatively.Such structure allows us to simultaneously consider discrimination quality and bounding box accuracy which is found to be crucial to the success.We also propose a network compression method to accelerate tracking speed without performance reduction.That also verifies tracking loss will remain highly effective even if the network is drastically compressed.Furthermore, if we employ a carefully designed tracking loss ensemble, the tracker would be much more robust and accurate.Evaluation results show that our trackers (including the ensemble tracker and two baseline trackers), outperform all state-of-the-art methods on VOT 2016 Challenge in terms of Expected Average Overlap (EAO) and robustness.We will make the code publicly available.

Abbiamo convertito con successo un popolare rilevatore RPN in un tracker ben performante dal punto di vista della funzione di loss.


We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code.The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated.In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program.Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs.Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete.Specifically, the architecture (1) generates a  shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space.We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs.Our model is able to predict the exact correct repair 41% of the time with a single guess, compared to 13% accuracy for an attentional sequence-to-sequence model.

Un'architettura neurale per segnare e classificare i candidati alla riparazione del programma per eseguire la riparazione semantica del programma in modo statico senza accesso agli unit test.

Presenta un'architettura di rete neurale composta dalle parti share, specialize e compete per riparare il codice in quattro casi.


Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier.In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a â€œsweet point" in co-optimizing model accuracy, robustness, and efficiency.Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction.That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.

È possibile co-progettare l'accuratezza, la robustezza e l'efficienza dei modelli per raggiungere un triplo vantaggio? Sì!

Sfrutta l'input-adaptive multiple early-exits per il campo dell'attacco e della difesa adversarial, riducendo la complessità media dell'inferenza senza entrare in conflitto con l'ipotesi di maggiore capacità.


Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.Especially, little is known about how they represent language in their intermediate layers.In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text.We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.

Mostriamo che le singole unità nelle rappresentazioni CNN apprese in task NLP sono selettivamente reattive a specifici concetti del linguaggio naturale.

Utilizza le unità grammaticali del linguaggio naturale che conservano i significati per dimostrare che le unità delle CNN deep apprese in task NLP potrebbero agire come un rilevatore di concetti in linguaggio naturale.


We study the problem of building models that disentangle independent factors of variation.Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.This labeling is of particular interest as it may be readily available without annotation costs.We introduce an autoencoder model and train it through constraints on image pairs and triplets.We show the role of feature dimensionality and adversarial training theoretically and experimentally.We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used.The numerical value of a factor has different meaning in different reference frames.When the reference depends on other factors, transferring that factor becomes ambiguous.We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.

Si tratta di un documento prevalentemente teorico che descrive le sfide nel separare i fattori di variazione, utilizzando autoencoder e GAN.

Questo articolo considera la separazione dei fattori di variazione nelle immagini, mostra che in generale, senza ulteriori presupposti, non si possono distinguere due diversi fattori di variazione, e suggerisce una nuova architettura AE+GAN per cercare di distinguere i fattori di variazione.

Questo articolo studia le sfide della separazione dei fattori indipendenti di variazione sotto dati debolmente annotati e introduce il termine ambiguità di riferimento per la mappatura dei punti dati.


In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query.Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism.This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion.The embeddings are trained with convolutional neural networks or the word2vec model.We demonstrate the performance of this model with image retrieval and text querying data sets.

learning to rank con diversi embedding e attention

Propone di usare l'attention per combinare rappresentazioni di input multipli sia per la query che per i risultati della ricerca nel task di learning to rank .


Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.Recent work has shown that functional diversity of neurons may be limited to that of relatively few cell types; other work has shown that incorporating constraints into artificial neural networks (ANNs) can improve their ability to mimic neural data.Here we develop an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters.The resulting models are both more predictive and more interpretable, revealing the contributions of functional cell types to neural computation and ultimately informing the design of future ANNs.

Abbiamo sviluppato un algoritmo che prende come input registrazioni di attività neurale e restituisce cluster di neuroni per tipo di cellule e modelli di attività neurale vincolati da questi cluster.


Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving.Here, instead, we focus on learning in the space of algorithms: we train several state-of-the-art GNN architectures to imitate individual steps of classical graph algorithms, parallel (breadth-first search, Bellman-Ford) as well as sequential (Prim's algorithm).As graph algorithms usually rely on making discrete decisions within neighbourhoods, we hypothesise that maximisation-based message passing neural networks are best-suited for such objectives, and validate this claim empirically.We also demonstrate how learning in the space of algorithms can yield new opportunities for positive transfer between tasks---showing how learning a shortest-path algorithm can be substantially improved when simultaneously learning a reachability algorithm.

Supervisioniamo le graph neural network per imitare gli output intermediate e step-wise dei classici algoritmi su grafo, recuperando intuizioni molto favorevoli.

Suggerisce il training di reti neurali per imitare gli algoritmi su grafo imparando primitive e subroutine al posto dell'output finale.


Prospection is an important part of how humans come up with new task plans, but has not been explored in depth in robotics.Predicting multiple task-level is a challenging problem that involves capturing both task semantics and continuous variability over the state of the world.Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.In this work, we propose a method for learning a model encoding just such a representation for task planning.We learn a neural net that encodes the k most likely outcomes from high level actions from a given world.Our approach creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future.We demonstrate this approach via application to a stacking task in a cluttered environment, where the robot must select between different colored blocks while avoiding obstacles, in order to perform a task.We also show results on a simple navigation task.Our algorithm generates realistic image and pose predictions at multiple points in a given task.

Descriviamo un'architettura per generare diverse ipotesi di obiettivi intermedi durante i task di manipolazione robotica.

Valuta la qualità di un modello predittivo generativo proposto per generare piani di esecuzione dei robot.

Questo articolo propone un metodo per imparare una funzione di transizione di alto livello utile per la pianificazione dei task.


Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks.While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs remain unclear.In this paper, we aim at bridging  this gap from both theoretical and empirical perspectives.First, we analyze a variant of Optimistic Stochastic Gradient (OSG) proposed in~\citep{daskalakis2017training} for solving a class of non-convex non-concave min-max problem and establish $O(\epsilon^{-4})$ complexity for finding $\epsilon$-first-order stationary point, in which the algorithm only requires invoking one stochastic first-order oracle while enjoying state-of-the-art iteration complexity achieved by stochastic extragradient method by~\citep{iusem2017extragradient}.Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $\widetilde{O}\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$~\footnote{Here $\widetilde{O}(\cdot)$ compresses a logarithmic factor of $\epsilon$.}, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.To the best of our knowledge, this is the first work for establishing adaptive complexity in non-convex non-concave min-max optimization.Empirically, our experiments show that indeed adaptive gradient algorithms outperform their non-adaptive counterparts in GAN training.Moreover, this observation can be explained by the slow growth rate of the cumulative stochastic gradient, as observed empirically.

Questo articolo fornisce un'analisi innovativa degli algoritmi adattivi per il gradiente per risolvere problemi di min-max non concavi come GAN, e spiega il motivo per cui i metodi di gradiente adattivi superano le loro controparti non adattive attraverso studi empirici.

Sviluppa algoritmi per la soluzione di disuguaglianze variazionali in ambiente stocastico, proponendo una variazione del metodo dell'extragradiente.


We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object.The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations.We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories.We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops.We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters.Training is performed purely end-to-end on the unmodified evidence lower bound objective.This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.

Rendiamo possibile l'apprendimento di traiettorie sofisticate di un oggetto puramente da pixel con un dataset giocattolo di video utilizzando una framework VAE con un processo Gaussiano precedente.


Dreams and our ability to recall them are among the most puzzling questions in sleep research.Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood.In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.).Our model achieves significant accuracy levels across all the sleep stages, thereby indicating subtle signatures of dream recall in the sleep microstructure.We also visualized the feature space to inspect the subject-specificity of the learned features, thus ensuring that the network captured population level differences.Beyond being the first study to apply deep learning to sleep EEG in order to classify HDR and LDR, guided backpropagation allowed us to visualize the most discriminant features in each sleep stage.The significance of these findings and future directions are discussed.

Indaghiamo la base neurale del dream recall usando una rete neurale convoluzionale e tecniche di visualizzazione delle feature, come tSNE e la backpropagation guidata.


This paper considers multi-agent reinforcement learning (MARL) in networked system control.Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors.We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent.Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL.Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.

Questo articolo propone una nuova formulazione e un nuovo protocollo di comunicazione per problemi di controllo multi-agente in rete

Riguarda le N-MARL dove gli agenti aggiornano la loro policy basandosi solo sui messaggi dei nodi vicini, mostrando che l'introduzione di un fattore di sconto spaziale stabilizza l'apprendimento.


Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.Recent work developing this class of methods has explored ever richer parameterizations of the approximate posterior in the hope of improving performance.In contrast, here we share a curious experimental finding that suggests instead restricting the variational distribution to a more compact parameterization.For a variety of deep Bayesian neural networks trained using Gaussian mean-field variational inference, we find that the posterior standard deviations consistently exhibit strong low-rank structure after convergence.This means that by decomposing these variational parameters into a low-rank factorization, we can make our variational approximation more compact without decreasing the models' performance.Furthermore, we find that such factorized parameterizations improve the signal-to-noise ratio of stochastic gradient estimates of the variational lower bound, resulting in faster convergence.

Mean field VB usa il doppio dei parametri; vincoliamo i parametri di varianza in mean field VB senza alcuna loss in ELBO, guadagnando velocità e gradienti con varianza più bassa.


Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining.Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest.In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available.Our main contributions are as follows.First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier.  Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student).Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words.Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.

Sfruttiamo efficacemente alcune keyword come supervisione debole per addestrare le reti neurali per aspect extraction.

Discute una variante di knowledge distillation che usa un "teacher" basato su un classificatore di bag-of-words con seed word e uno "studente" che è una rete neurale basata sull'embedding.


Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons.However, the relative contributions of these connections to perceptual grouping are poorly understood.We address this question by systematically evaluating neural network architectures featuring combinations of these connections on two synthetic visual tasks, which stress low-level "Gestalt" vs. high-level object cues for perceptual grouping.We show that increasing the difficulty of either task strains learning for networks that rely solely on bottom-up processing.Horizontal connections resolve this limitation on tasks with Gestalt cues by supporting incremental spatial propagation of activities, whereas top-down connections rescue learning on tasks with high-level object cues by modifying coarse predictions about the position of the target object.Our findings dissociate the computational roles of bottom-up, horizontal and top-down connectivity, and demonstrate how a model featuring all of these interactions can more flexibly learn to form perceptual groups.

Le connessioni di feedback orizzontali e top-down sono responsabili delle strategie complementari di raggruppamento percettivo nei sistemi di visione biologica e ricorrente.

Utilizzando le reti neurali come modello computazionale del cervello, esamina l'efficienza di diverse strategie per risolvere due visual challenge.


Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images.However, their application in the audio domain has received limited attention,and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech.Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes.The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced.  To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (FrÃ©chet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS.We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator.Listen to GAN-TTS reading this abstract at http://tiny.cc/gantts.

Introduciamo GAN-TTS, una Generative Adversarial Network per Text-to-Speech, che raggiunge il Mean Opinion Score (MOS) 4.2.

Risolve il problema delle GAN nella sintesi delle forme d'onda grezze e comincia a colmare il divario di prestazioni esistente tra i modelli autoregressivi e le GAN per gli audio grezzi.


This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.Different from existing works, our PiT framework employs the sparse penalties to train networks and thus help rank the importance of weights and filters.Our PiT algorithms can directly prune the network without any fine-tuning.The pruned networks can still achieve comparable performance to the original networks.In particular, we introduce the (Group) Lasso-type Penalty (L-P /GL-P), and (Group) Split LBI Penalty (S-P / GS-P) to regularize the networks, and a pruning strategy proposed  is used in help prune the network.We conduct the extensive experiments on MNIST, Cifar-10, and miniImageNet.The results validate the efficacy of our proposed methods.Remarkably, on MNIST dataset, our PiT framework can save 17.5% parameter size of LeNet-5, which achieves the 98.47% recognition accuracy.

proponiamo un algoritmo di apprendimento per fare pruning della rete applicando penalty di sparsità della struttura

Questo articolo introduce un approccio al pruning durante il training di una rete usando lasso e penalty split LBI


We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.Given limited labeled data just before inference, those representations can also be associated with specific object types to perform classification.To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM), which loosely models the function of a cortical column in the mammalian brain.Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns and forgetting outliers, and top-down predictions.We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examples per class.STAMs suggest a promising direction to solve the UCL problem without catastrophic forgetting.

Introduciamo l'unsupervised continual learning (UCL) e un'architettura ispirata dalla neurologia che risolve il problema UCL.

Propone l'uso di gerarchie di moduli STAM per risolvere il problema UCL, fornendo la prova che le rappresentazioni che i moduli imparano sono adatte alla classificazione few-shot.


Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems.Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation.We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters.Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect.

Nuovo metodo di estrazione del segnale nel dominio di Fourier

Contribuisce a una versione con convoluzione a valori complessi della Feature-Wise Linear Modulation che permette l'ottimizzazione dei parametri e progetta una loss che tiene conto di modulo e fase.


It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation in a different and unpredictable way.It is rare for one to have access to a large number of data to help separate the influences.In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner.We address this problem in a two-branch Autoencoder framework.For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge.This encourages the branch to distill geometry information.Another branch learns the complementary style information.The two branches form an effective framework that can disentangle object's content-style representation without any human annotation.We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data.We are able to generate photo-realistic images with 256x256 resolution that are clearly disentangled in content and style.

Presentiamo un nuovo framework per imparare la rappresentazione separata del contenuto e dello stile in modo completamente unsupervised.

Propongono un modello basato sul framework dell'autoencoder per distinguere la rappresentazione di un oggetto, i risultati mostrano che il modello può produrre rappresentazioni che catturano il contenuto e lo stile.


We develop the Y-learner for estimating heterogeneous treatment effects in experimental and observational studies.The Y-learner is designed to leverage the abilities of neural networks to optimize multiple objectives and continually update, which allows for better pooling of underlying feature information between treatment and control groups.We evaluate the Y-learner on three test problems: (1) A set of six simulated data benchmarks from the literature.(2) A real-world large-scale experiment on voter persuasion.(3) A task from the literature that estimates artificially generated treatment effects on MNIST didgits.The Y-learner achieves state of the art results on two of the three tasks.On the MNIST task, it gets the second best results.

Sviluppiamo una strategia di stima CATE che sfrutta alcune delle interessanti proprietà delle reti neurali.

Mostra i miglioramenti su X-learner modellando la funzione di risposta al trattamento, la funzione di risposta al controllo e la mappatura dall'effetto di trattamento imputato all'effetto di trattamento medio condizionato tramite reti neurali.

Gli autori propongono Y-learner per stimare l'effetto di trattamento medio condizionato (CATE), che aggiorna simultaneamente i parametri delle funzioni di risultato e lo stimatore CATE.


With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems.Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware.In recent years, we have witnessed devastating security breaches targeting IoT devices.These security concerns have significantly hamstrung further evolution of IoT technology.In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices.We do not encode into our emulator any specific information about a device.Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly.This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device.To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator.We successfully identified both self-injected and real-world vulnerabilities.

Esecuzione del firmware indipendente dal dispositivo


Deep neural models, such as convolutional and recurrent networks, achieve phenomenal results over spatial data such as images and text.However, when considering tabular data, gradient boosting of decision trees (GBDT) remains the method of choice.Aiming to bridge this gap, we propose \emph{deep neural forests} (DNF)--  a novel architecture that combines elements from decision trees as well as dense residual connections. We present the results of extensive empirical study in which we examine the performance of GBDTs, DNFs and (deep) fully-connected networks. These results indicate that DNFs achieve comparable results to GBDTs on tabular data, and open the door to end-to-end neural modeling of multi-modal data.To this end, we present a successful application of DNFs as part of a hybrid architecture for a multi-modal driving scene understanding classification task.

Un'architettura per dati tabulari, che emula i rami degli alberi decisionali e usa una densa connettività residua

Questo articolo propone la deep neural forest, un algoritmo che si rivolge ai dati tabulari e integra i punti forti del gradient boosting degli alberi decisionali.

Una nuova architettura di rete neurale che imita il funzionamento delle foreste decisionali per affrontare il problema generale del training di modelli deep per i dati tabulari e mostrare un'efficacia alla pari con GBDT.


Hyperparameter tuning is one of the most time-consuming workloads in deep learning.State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable.Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better results.Motivated by this trend, we ask: can simple adaptive methods, based on SGD perform as well or better?We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.We then analyze its robustness to learning rate misspecification and objective curvature variation.Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD.YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly.We empirically show YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to $3.28$x in synchronous and up to $2.69$x in asynchronous settings.

YellowFin è un ottimizzatore basato su SGD con adattabilità sia al momento che al learning rate.

Propone un metodo per sintonizzare automaticamente il parametro momento nei metodi SGD con momento, che raggiunge risultati migliori e una velocità di convergenza veloce rispetto all'algoritmo state-of-the-art Adam.


Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits.With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms.Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack.This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.

Adversarial attack sullo spazio latente degli autoencoder variazionali per cambiare il significato semantico degli input

Questo articolo riguarda la sicurezza e l'apprendimento automatico e propone un attacco man-in-middle che altera la codifica VAE dei dati di input in modo che l'output decodificato sia classificato erroneamente.


Graph-based dependency parsing consists of two steps: first, an encoder produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a decoder} finds the parse tree whose substructures have the largest total score.Over the past few years, powerful neural techniques have been introduced into the encoding step which substantially increases parsing accuracies.However, advanced decoding techniques, in particular high-order decoding, have seen a decline in usage.It is widely believed that contextualized features produced by neural encoders can help capture high-order decoding information and hence diminish the need for a high-order decoder.In this paper, we empirically evaluate the combinations of different neural and non-neural encoders with first- and second-order decoders and provide a comprehensive analysis about the effectiveness of these combinations with varied training data sizes.We find that: first, when there is large training data, a strong neural encoder with first-order decoding is sufficient to achieve high parsing accuracy and only slightly lags behind the combination of neural encoding and second-order decoding; second, with small training data, a non-neural encoder with a second-order decoder outperforms the other combinations in most cases.

Uno studio empirico che esamina l'efficacia di diverse combinazioni encoder-decoder per il task di dependency parsing

Analizza empiricamente vari encoder, decoder e le loro dipendenze per dependecy parsing basato su grafi.


Meta-learning will be crucial to creating lifelong, generalizable AI.In practice, however, it is hard to define the meta-training task distribution that is used to train meta-learners.If made too small, tasks are too similar for a model to meaningfully generalize.If made too large, generalization becomes incredibly difficult.We argue that both problems can be alleviated by introducing a teacher model that controls the sequence of tasks that a meta-learner is trained on.This teacher model is incentivized to start the student meta-learner on simple tasks then adaptively increase task difficulty in response to student progress.While this approach has been previously studied in curriculum generation, our main contribution is in extending it to meta-learning.

Teacher che allena i meta-learner come gli umani


Using higher order knowledge to reduce training data has become a popular research topic.However, the ability for available methods to draw effective decision boundaries is still limited: when training set is small, neural networks will be biased to certain labels.Based on this observation, we consider constraining output probability distribution as higher order domain knowledge.We design a novel algorithm that jointly optimizes output probability distribution on a clustered embedding space to make neural networks draw effective decision boundaries.  While directly applying probability constraint is not effective, users need to provide additional very weak supervisions: mark some batches that have output distribution greatly differ from target probability distribution.We use experiments to empirically prove that our model can converge to an accuracy higher than other state-of-art semi-supervised learning models with less high quality labeled training examples.

Introduciamo un approccio di embedding space per vincolare la distribuzione di probabilità dell'output delle reti neurali.

Questo articolo introduce un metodo per eseguire semi-supervised learning con deep neural network, e il modello raggiunge una precisione relativamente alta, data una piccola dimensione di training.

Questo articolo incorpora la distribuzione delle label nel training del modello quando è disponibile un numero limitato di istanze di training, e propone due tecniche per gestire il problema della distribuzione biased delle label di output.


We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy).  Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus.We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis.  We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

Introduciamo un nuovo tipo di rappresentazione deep contestualizzata delle parole che migliora significativamente lo stato dell'arte per una serie di task NLP impegnativi.


This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data.We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning.Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead.Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences.We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.

Questo lavoro introduce una nuova funzione di loss per il training robusto di una DNN per localizzazione temporale in presenza di label non allineate.

Una nuova loss per il training di modelli che prevedono dove si verificano gli eventi in una sequenza di training con label rumorose, confrontando le label con smoothing e la sequenza di predizione.


The driving force behind deep networks is their ability to compactly represent rich classes of functions.The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another.To date, expressive efficiency analyses focused on the architectural feature of depth, showing that deep networks are representationally superior to shallow ones.In this paper we study the expressive efficiency brought forth by connectivity, motivated by the observation that modern networks interconnect their layers in elaborate ways.We focus on dilated convolutional networks, a family of deep models delivering state of the art performance in sequence processing tasks.By introducing and analyzing the concept of mixed tensor decompositions, we prove that interconnecting dilated convolutional networks can lead to expressive efficiency.In particular, we show that even a single connection between intermediate layers can already lead to an almost quadratic gap, which in large-scale settings typically makes the difference between a model that is practical and one that is not.Empirical evaluation demonstrates how the expressive efficiency of connectivity, similarly to that of depth, translates into gains in accuracy.This leads us to believe that expressive efficiency may serve a key role in developing new tools for deep network design.

Introduciamo la nozione di decomposizioni tensoriali miste e la usiamo per dimostrare che l'interconnessione di reti convoluzionali dilatate aumenta la loro potenza espressiva.

Questo articolo prova teoricamente che l'interconnessione di reti con diverse dilatazioni può portare all'efficienza espressiva utilizzando la decomposizione tensoriale mista.

Gli autori studiano le reti convoluzionali dilatate e mostrano che l'intreccio di due reti convoluzionali dilatate A e B in varie fasi è più efficiente dal punto di vista espressivo che non intrecciarle.

Mostra che l'assunzione strutturale della WaveNet di un singolo albero binario perfetto ostacola le sue prestazioni e che le architetture simili alla WaveNet con strutture ad albero miste più complesse funzionano meglio.


We apply multi-task learning to image classification tasks on MNIST-like datasets.MNIST dataset has been referred to as the {\em drosophila} of machine learning and has been the testbed of many learning theories.The NotMNIST dataset and the FashionMNIST dataset have been created with the MNIST dataset as reference.In this work, we exploit these MNIST-like datasets for multi-task learning.The datasets are pooled together for learning the parameters of joint classification networks.Then the learned parameters are used as the initial parameters to retrain disjoint classification networks.The baseline recognition model are all-convolution neural networks.Without multi-task learning, the recognition accuracies for MNIST, NotMNIST and FashionMNIST are 99.56\%, 97.22\% and 94.32\% respectively.With multi-task learning to pre-train the networks, the recognition accuracies are respectively 99.70\%, 97.46\% and 95.25\%.The results re-affirm that multi-task learning framework, even with data with different genres, does lead to significant improvement.

Questo articolo presenta una rete neurale multi-task per la classificazione su dataset simili a MNIST


Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network.To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization.This approach provides us with a broad and unifying view on much prior work on this topic.Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.In particular, they specify a concrete security guarantee that would protect against a well-defined class of adversaries.These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks.They also suggest robustness against a first-order adversary as a natural security guarantee.We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.

Forniamo una rivisitazione basata sull'ottimizzazione della nozione di adversarial example, e sviluppiamo metodi che producono modelli che sono robusti contro una vasta gamma di avversari.

Studia una formulazione minimax dell'apprendimento delle deep neural network per aumentare la loro robustezza, utilizzando la discesa del gradiente proiettata come principale avversario.

Questo articolo propone di cercare di rendere le reti neurali resistenti alle loss adversarial attraverso il framework dei problemi del punto di sella.


In recent years there has been a rapid increase in classification methods on graph structured data.Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance.However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set.This prevents fair competition of the algorithms and raises a question of the validity of the obtained results.We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.

Molti dataset di classificazione dei grafi hanno duplicati, sollevando così domande sulle capacità di generalizzazione e sull'equo confronto dei modelli.

Gli autori discutono il bias di isomorfismo nei dataset di grafi, l'effetto di overfitting nell'apprendimento delle reti quando le feature di isomorfismo dei grafi sono incorporate nel modello, teoricamente analogo agli effetti di leakage dei dati.


Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.However, learning from demonstrations often suffers from the covariate shift problem, which resultsin cascading errors of the learned policy.We introduce a notion of conservatively extrapolated value functions, which provably lead to policies with self-correction.We design an algorithm Value Iteration with Negative Sampling (VINS) that practically learns such value functions with conservative extrapolation.We show that VINS can correct mistakes of the behavioral cloning policy on simulated robotics benchmark tasks.We also propose the algorithm of using VINS to initialize a reinforcement learning algorithm, which is shown to outperform prior works in sample efficiency.

Introduciamo una nozione di value function estrapolate in modo conservativo, che portano probabilimente a policy che possono auto-correggersi per rimanere vicine agli stati di dimostrazione, e le impariamo con una nuova tecnica di negative sampling.

Un algoritmo chiamato value iteration con negative sampling per affrontare il problema del covariate shift nell'imitation learning.


A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition.Learning such a structured world model from raw sensory data remains a challenge.As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs).C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure.We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network.This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process.We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation.Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.

I world model strutturati contrastively-trained imparano rappresentazioni di stato orientate agli oggetti e un modello relazionale di un ambiente a partire da input di pixel grezzi.

Gli autori superano il problema dell'utilizzo di loss basate sui pixel nella costruzione e nell'apprendimento di world model strutturati utilizzando uno spazio latente contrastive.


Neural machine translation (NMT) models learn representations containing substantial linguistic information.However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons.We develop unsupervised methods for discovering important neurons in NMT models.Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision.We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena.Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.

Metodi non supervisionati per trovare, analizzare e controllare i neuroni importanti nella NMT

Questo lavoro propone di trovare neuroni "significativi" nei modelli di traduzione automatica neurale classificando in base alla correlazione tra coppie di modelli, diverse epoche o diversi dataset, e propone un meccanismo di controllo dei modelli.


Computations for the softmax function in neural network models are expensive when the number of output classes is large.This can become a significant issue in both training and inference for such models.In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.During inference, our method quickly locates the most probable expert to compute small-scale softmax.Our method is learning-based and requires no knowledge of the output class partition space a priori.We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of

Presentiamo un softmax doppiamente sparso, la miscela sparsa di esperti sparsi, per migliorare l'efficienza dell'inferenza softmax attraverso lo sfruttamento della gerarchia a due livelli di sovrapposizione.

L'articolo propone la nuova implementazione dell'algoritmo softmax con due livelli gerarchici di sparsità che accelera l'operazione nel language modelling.


Our work presents empirical evidence that layer rotation, i.e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance.Compared to previously studied indicators of generalization, we show that layer rotation has the additional benefit of being easily monitored and controlled, as well as having a network-independent optimum: the training procedures during which all layers' weights reach a cosine distance of 1 from their initialization consistently outperform other configurations -by up to 20% test accuracy.Finally, our results also suggest that the study of layer rotation can provide a unified framework to explain the impact of weight decay and adaptive gradient methods on generalization.

Questo articolo presenta prove empiriche che supportano la scoperta di un indicatore di generalizzazione: l'evoluzione durante il training della distanza coseno tra il vettore di peso di ogni layer e la sua inizializzazione.


Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program.Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths therein (e.g. data-flow relations), which are precise and abundantly available for code.This provides a strong inductive bias towards semantically meaningful relations, yielding more generalizable representations than classical sequence-based models.Unfortunately, these models primarily rely on graph-based message passing to represent relations in code, which makes them de facto local due to the high cost of message-passing steps, quite in contrast to modern, global sequence-based models, such as the Transformer.In this work, we bridge this divide between global and structured models by introducing two new hybrid model families that are both global and incorporate structural bias: Graph Sandwiches, which wrap traditional (gated) graph message-passing layers in sequential message-passing layers; and Graph Relational Embedding Attention Transformers (GREAT for short), which bias traditional Transformers with relational information from graph edge types.By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.Starting with a  graph-based model that already improves upon the prior state-of-the-art for this task by 20%, we show that our proposed hybrid models improve an additional 10-15%, while training both faster and using fewer parameters.

I modelli di codice sorgente che combinano feature globali e strutturali imparano rappresentazioni più potenti dei programmi.

Un nuovo metodo per modellare il codice sorgente per il task di riparazione dei bug usando un modello a sandwich come [RNN GNN RNN] che migliora significativamente la localizzazione e la precisione della riparazione.


Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate.While a number of works attempt to mitigate this effect through gated recurrent units, skip-connections, parametric constraints and design choices, we propose a novel incremental RNN (iRNN), where hidden state vectors keep track of incremental changes, and as such approximate state-vector increments of Rosenblatt's (1962) continuous-time RNNs.iRNN exhibits identity gradients and is able to account for long-term dependencies (LTD).We show that our method is computationally efficient overcoming overheads of many existing methods that attempt to improve RNN training, while suffering no performance degradation.We demonstrate the utility of our approach with extensive experiments and show competitive performance against standard LSTMs on LTD and other non-LTD tasks.

Le incremental-RNN risolvono il problema dell'exploding/vanishing gradient aggiornando i vettori di stato in base alla differenza tra lo stato precedente e quello previsto da una ODE.

Gli autori affrontano il problema della propagazione del segnale nelle reti neurali ricorrenti costruendo un sistema di attrazione per la transizione del segnale e controllando se converge ad un equilibrio.


Recent empirical results on over-parameterized deep networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks.Researchers are actively working on bridging this discrepancy by proposing better complexity measures.Instead, we directly measure prediction bias and variance for four classification and regression tasks on modern deep networks.We find that both bias and variance can decrease as the number of parameters grows.Qualitatively, the phenomenon persists over a number of gradient-based optimizers.To better understand the role of optimization, we decompose the total variance into variance due to training set sampling and variance due to initialization.Variance due to initialization is significant in the under-parameterized regime.In the over-parameterized regime, total variance is much lower and dominated by variance due to sampling.We provide theoretical analysis in a simplified setting that is consistent with our empirical findings.

Forniamo prove contro le affermazioni classiche sul bias-variance tradeoff e proponiamo una nuova decomposizione per la varianza.


Real world images often contain large amounts of private / sensitive information that should be carefully protected without reducing their utilities.In this paper, we propose a privacy-preserving deep learning framework with a learnable ob- fuscator for the image classification task.Our framework consists of three mod- els: learnable obfuscator, classifier and reconstructor.The learnable obfuscator is used to remove the sensitive information in the images and extract the feature maps from them.The reconstructor plays the role as an attacker, which tries to recover the image from the feature maps extracted by the obfuscator.In order to best protect usersâ€™ privacy in images, we design an adversarial training methodol- ogy for our framework to optimize the obfuscator.Through extensive evaluations on real world datasets, both the numerical metrics and the visualization results demonstrate that our framework is qualified to protect usersâ€™ privacy and achieve a relatively high accuracy on the image classification task.

Abbiamo proposto un nuovo framework di classificazione delle immagini di deep learning che può sia classificare accuratamente le immagini che proteggere la privacy degli utenti.

Questo articolo propone un framework che conserva le informazioni private nell'immagine e non compromette l'usabilità dell'immagine.

Questo lavoro suggerisce l'uso di adversarial network per offuscare le immagini e quindi permettere di raccoglierle senza problemi di privacy per utilizzarle per il training di modelli di machine learning.


Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority.All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security itâ€™s original structure does not allow for direct analysis of address transactions. Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate.We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms.We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.

un modello 2vec per i grafi delle transazioni di criptovaluta

L'articolo propone di usare un autoencoder, networkX e node2Vec per prevedere se un indirizzo Bitcoin diventerà vuoto dopo un anno, ma i risultati sono peggiori di una baseline esistente.


Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poorly understood.In this work, we analyze last-iterate convergence of simultaneous gradient descent (simGD) and its variants under the assumption of convex-concavity, guided by a continuous-time analysis with differential equations.First, we show that simGD, as is, converges with stochastic sub-gradients under strict convexity in the primal variable.Second, we generalize optimistic simGD to accommodate an optimism rate separate from the learning rate and show its convergence with full gradients.Finally, we present anchored simGD, a new method, and show convergence with stochastic subgradients.

Prova di convergenza del metodo stocastico dei sub-gradienti e variazioni su problemi minimax convessi-concavi

Un'analisi del simultaneous stochastic subgradient, del simultaneous gradient con optimism e del simultaneous gradient con anchoring nel contesto dei giochi concavi convessi minmax.

Questo articolo analizza la dinamica dello stochastic gradient descent quando applicata a giochi convessi-concavi, così come la GD con optimism e un nuovo algoritmo GD ancorato che converge sotto ipotesi più deboli di SGD o SGD con optimism.


Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice.When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage.In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation.The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution.The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest.In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control.We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering.We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods.The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.

Proponiamo un framework algoritmico per programmare costellazioni di piccoli veicoli spaziali con capacità di ri-orientamento 3-DOF, collegati in rete con collegamenti inter-sat.

Questo articolo propone un modulo di comunicazione per ottimizzare il programma di comunicazione per il problema delle costellazioni di veicoli spaziali, e confronta l'algoritmo in setting distribuiti e centralizzati.


Importance sampling (IS) is a standard Monte Carlo (MC) tool to compute information about random variables such as moments or quantiles with unknown distributions.  IS is asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity.However, retaining infinitely many particles is intractable.We propose a scheme for only keeping a \emph{finite representative subset} of particles and their augmented importance weights that is \emph{nearly consistent}. To do so in {an online manner}, we approximate importance sampling in two ways.  First, we replace the deltas by kernels, yielding kernel density estimates (KDEs).  Second, we sequentially project KDEs onto nearby lower-dimensional subspaces.We characterize the asymptotic bias of this scheme as determined by a compression parameter and kernel bandwidth, which yields a tunable tradeoff between consistency and memory.In experiments,  we observe a favorable tradeoff between memory and accuracy, providing for the first time near-consistent compressions of arbitrary posterior distributions.

Abbiamo proposto un nuovo algoritmo di importance sampling compresso e kernelizzato.


We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator?(2) how to correctly use cross-validation to choose the regularization parameter?and (3) how to accelerate computation without losing too much accuracy?We consider the three problems in a unified large-data linear model.We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise. We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction.We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate.Our results are illustrated by simulations and by analyzing empirical data.

Studiamo il framework della ridge regression in un framework asintotico ad alta dimensione, e otteniamo intuizioni sulla cross-validation e sullo sketching.

Uno studio teorico della ridge regression sfruttando una nuova caratterizzazione asintotica dello stimatore di ridge regression.


Attention mechanisms have advanced the state of the art in several machine learning tasks.Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness.In this paper, we address this problem by studying the landscape of population and empirical loss functions of attention-based neural networks.Our results show that, under mild assumptions, every local minimum of a two-layer global attention model has low prediction error, and attention models require lower sample complexity than models not employing attention.We then extend our analyses to the popular self-attention model, proving that they deliver consistent predictions with a more expressive class of functions.Additionally, our theoretical results provide several guidelines for designing attention mechanisms.Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.

Analizziamo il panorama delle loss delle reti neurali con attention e spieghiamo perché l'attention è utile nel training delle reti neurali per ottenere buone prestazioni.

Questo articolo dimostra dal punto di vista teorico che le reti con attention possono generalizzare meglio delle baseline senza attention per l'attention fissa (monolayer e multilayer) e self-attention nel setting a singolo layer.


Recent advances in deep learning techniques has shown the usefulness of the deep neural networks in extracting features required to perform the task at hand.However, these features learnt are in particular helpful only for the initial task.This is due to the fact that the features learnt are very task specific and does not capture the most general and task agnostic features of the input.In fact the way humans are seen to learn is by disentangling features which task agnostic.This indicates that leaning task agnostic features by disentangling only the most informative features from the input data.Recently Variational Auto-Encoders (VAEs) have shown to be the de-facto models to capture the latent variables in a generative sense.As these latent features can be represented as continuous and/or discrete variables, this indicates us to use VAE with a mixture of continuous and discrete variables for the latent space.We achieve this by performing our experiments using a modified version of joint-vae to learn the disentangled features.

Mixture Model per Neural Disentanglement


To improve how neural networks function it is crucial to understand their learning process.The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task.However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions.In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression.In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions.Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions.With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions.We also find that there is a large amount of variation in compression between different network initializations.Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting.Finally, we show that only compression of the last layer is positively correlated with generalization.

Abbiamo sviluppato stime robuste dell'informazione mutua per le DNN e le abbiamo usate per osservare la compressione nelle reti con funzioni di attivazione non saturanti

Questo articolo ha studiato la credenza popolare che le deep neural network operino la compressione dell'informazione per task supervisionati

Questo articolo propone un metodo per la stima dell'informazione reciproca per reti con funzioni di attivazione non limitate e l'uso della regolarizzazione L2 per indurre una maggiore compressione.


In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation.We introduce TimbreTron, a method for musical timbre transfer which applies â€œimageâ€ domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer.We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance.Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.We made an accompanying demo video here: https://www.cs.toronto.edu/~huang/TimbreTron/index.html which we strongly encourage you to watch before reading the paper.

Presentiamo il TimbreTron, una pipeline per il transfer del timbro di alta qualità su forme d'onda musicali utilizzando il transfer in stile dominio CQT.

Un metodo per convertire le registrazioni di uno specifico strumento musicale in un altro applicando CycleGAN, sviluppato per il transfer dello stile delle immagini, per trasferire gli spettrogrammi.

Gli autori usano tecniche/strumenti multipli per permettere il transfer neurale del timbro (conversione della musica da uno strumento all'altro) senza esempi di allenamento accoppiati.

Descrive un modello per il transfer del timbro musicale con i risultati che indicano che il sistema proposto è efficace per il transfer dell'altezza e del tempo, così come per l'adattamento del timbro.


Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.But also generic hardware and software implementations of deep learning run more efficiently for sparse networks.Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints.We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network.DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded.We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance.DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

L'articolo presenta Deep Rewiring, un algoritmo che può essere utilizzato per addestrare deep neural network quando la connettività della rete è fortemente limitata durante il training.

Un approccio per implementare il deep learning direttamente su grafi sparsamente connessi, permettendo alle reti di essere addestrate in modo efficiente online e per un apprendimento veloce e flessibile.

Gli autori forniscono un semplice algoritmo in grado di allenarsi con una memoria limitata


Deep learning's success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters.These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements.Some model compression methods have been successfully applied on image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks.In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods.We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator.We show that this framework has a compelling performance to high degrees of sparsity, generalizes well to new tasks and models, and enables meaningful comparisons between different pruning granularities.

I metodi di pruning esistenti falliscono quando applicati alle GAN che affrontano task complessi, quindi presentiamo un metodo semplice e robusto per fare pruning sui generatori che funziona bene per un'ampia varietà di reti e task.

Gli autori propongono una modifica al metodo classico di distillation per il task di comprimere una rete per affrontare il fallimento delle soluzioni precedenti quando applicate alle generative adversarial network.


Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth.To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training.We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus.On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB.Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.

troviamo che il 99,9% del gradient exchange in SGD distribuito è ridondante; riduciamo la larghezza di banda di comunicazione di due ordini di grandezza senza perdere precisione.

Questo documento propone un ulteriore miglioramento rispetto al gradient dropping per migliorare l'efficienza della comunicazione


Image-to-image translation has recently received significant attention due to advances in deep learning.Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way.However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations.To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain.Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain.To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

Proponiamo la rete Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) che condiziona il processo di traduzione su un'immagine esemplare nel dominio di destinazione.

Discute un fallimento centrale e la necessità di modelli di traduzione I2I.

L'articolo esplora l'idea che un'immagine ha due componenti e applica un modello di attention in cui le maschere di feature che guidano il processo di traduzione non richiedono label semantiche


Deep neural networks can learn meaningful representations of data.However, these representations are hard to interpret.For example, visualizing a latent layer is generally only possible for at most three dimensions.Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer.Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions.To do so, we propose a class of regularizations we call \textit{Graph Spectral Regularizations} that impose graph-structure on latent layers.This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters.This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data.First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets.Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation.In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations.Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.

Imporre una struttura a grafo sui layer delle reti neurali per una migliore interpretabilità visiva.

Un nuovo regolarizzatore per imporre la struttura del grafo sui layer nascosti di una rete neurale per migliorare l'interpretabilità delle rappresentazioni nascoste.

Evidenzia il contributo del regolarizzatore spettrale del grafo all'interpretabilità delle reti neurali.


Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.The dominant parametric approach is based on locally normalized models which predict one word at a time.While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process.In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation.Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa.Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines.Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.

Mostriamo che i modelli Energy-Based, quando addestrati sul residuo di un language model autoregressive, possono essere utilizzati in modo efficace ed efficiente per generare testo.

Un modello proposto basato sull'energia residua (EBM) per la generazione di testo che opera a livello di frase, e può quindi sfruttare BERT, e raggiunge una perplexity inferiore ed è preferito dalla valutazione umana.


We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale.As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models.It does not, however, improve the robustness against more natural, and typically larger, perturbations.Learning more robust features during training appears to be necessary for robustness in this second sense.We show that features derived from a model that was encouraged to learn global, shape-based representations (Geirhos et al., 2019) do not only improve the robustness against natural perturbations, but when used in conjunction with an episodic memory, they also provide additional robustness against adversarial perturbations.Finally, we address three important design choices for the episodic memory: memory size, dimensionality of the memories and the retrieval method.We show that to make the episodic memory more compact, it is preferable to reduce the number of memories by clustering them, instead of reducing their dimensionality.

studio sistematico di modelli di riconoscimento di immagini su larga scala basati sulla cache, concentrandosi in particolare sulle loro proprietà di robustezza

Questo articolo ha proposto di usare la cache per migliorare la robustezza contro gli esempi di adversarial image, e ha concluso che l'uso di una grande cache continua non è superiore all'hard attention.


Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups.Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory.Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory).In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups.In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra.This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions.The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important.In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.

L'articolo descrive un framework flessibile per costruire CNN che sono equivarianti a una grande classe di gruppi di trasformazioni.

Un framework per costruire CNN di gruppo con un gruppo di Lie arbitrario G, che mostra superiorità rispetto a una CNN nella classificazione dei tumori e nella localizzazione dei punti di riferimento.


Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization.Although alternative pooling methods exist (eg. max, lp norm, stochastic), the averaging operation is still the dominating global pooling scheme in popular models.As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy?We first ask: ``is there a difference between features learned by global average and max pooling?'' Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales.We then ask ``is there a single global feature pooling variant that's most suitable for fine-grained recognition?'' A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized pooling's performance increases almost monotonically as it changes from average to max.We finally ask: ``what's the best way to combine two heterogeneous pooling schemes?'' Common strategies struggle because of potential gradient conflict but the ``freeze-and-train'' trick works best.We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.

Un benchmark di nove schemi rappresentativi di pooling globale rivela alcuni risultati interessanti.

Per task di classificazione fine-grained, questo articolo ha convalidato che maxpooling incoraggia mappe di feature più sparse e supera avgpooling.


We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions.Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown.We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets.The improvements are greater when the training set is smaller or the task is more challenging.Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL.Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements.We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.

La self-supervision migliora il riconoscimento few-shot su dataset piccoli e impegnativi senza fare affidamento su dati extra; i dati extra aiutano solo quando provengono dallo stesso dominio o da uno simile.

Uno studio empirico di diversi metodi di apprendimento self-supervised (SSL), mostrando che SSL aiuta di più quando il dataset è più difficile, che il dominio conta per il training, e un metodo per scegliere sample da un dataset non annotato.


Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy.In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces.It is based on MDP homomorphisms, a structure-preserving mapping between MDPs.We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters.Our novel task transfer method beats a baseline based on a deep Q-network.

Creiamo modelli astratti di ambienti dall'esperienza e li usiamo per imparare più velocemente nuovi task.

Una metodologia che utilizza l'idea degli omomorfismi MDP per trasformare un MDP complesso con uno spazio di stato continuo in uno più semplice.