We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory.Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights.Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking.This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers.In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results.The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar.Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena, such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck and the phase transition out of it (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.

Risultati in forma chiusa per deep learning nel limite di disaccoppiamento dei layer applicabili alle Residual Network


During the last years, a remarkable breakthrough has been made in AI domainthanks to artificial deep neural networks that achieved a great success in manymachine learning tasks in computer vision, natural language processing, speechrecognition, malware detection and so on.However, they are highly vulnerableto easily crafted adversarial examples.Many investigations have pointed out thisfact and different approaches have been proposed to generate attacks while addinga limited perturbation to the original data.The most robust known method so faris the so called C&W attack [1].Nonetheless, a countermeasure known as fea-ture squeezing coupled with ensemble defense showed that most of these attackscan be destroyed [6].In this paper, we present a new method we call CenteredInitial Attack (CIA) whose advantage is twofold : first, it insures by construc-tion the maximum perturbation to be smaller than a threshold fixed beforehand,without the clipping process that degrades the quality of attacks.Second, it isrobust against recently introduced defenses such as feature squeezing, JPEG en-coding and even against a voting ensemble of defenses.While its application isnot limited to images, we illustrate this using five of the current best classifierson ImageNet dataset among which two are adversarialy retrained on purpose tobe robust against attacks.With a fixed maximum perturbation of only 1.5% onany pixel, around 80% of attacks (targeted) fool the voting ensemble defense andnearly 100% when the perturbation is only 6%.While this shows how it is difficultto defend against CIA attacks, the last section of the paper gives some guidelinesto limit their impact.

In questo articolo, viene fornito un nuovo metodo che chiamiamo Centered Initial Attack (CIA). Esso assicura per costruzione che la perturbazione massima sia più piccola di una soglia fissata in anticipo, senza il processo di clipping.


Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task.Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query.However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point.Furthermore, prior work can only handle queries that use conjunctions ($\wedge$) and existential quantifiers ($\exists$).Handling queries with logical disjunctions ($\vee$) remains an open problem.Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\wedge$, $\vee$, and $\exists$ operators in massive and incomplete KGs.Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities.However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\wedge$, $\vee$, $\exists$ in a scalable manner.We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.

Rispondere a un'ampia classe di query logiche su knowledge graph con box embedding nello spazio vettoriale


Driven by the need for parallelizable hyperparameter optimization methods, this paper studies open loop search methods: sequences that are predetermined and can be generated before a single configuration is evaluated.Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.In particular, we propose the use of k-determinantal point processes in  hyperparameter optimization via random search.Compared to conventional uniform random search where hyperparameter settings are sampled independently, a k-DPP promotes diversity.  We describe an approach that transforms hyperparameter search spaces for efficient use with a k-DPP.In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from k-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure.Our experiments show significant benefits  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.

Affrontiamo l'ottimizzazione degli iperparametri completamente parallela con i Determinantal Point Process.


Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data.However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy.Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage.In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms.GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information.This fused graph is then repeatedly coarsened into a much smaller graph by merging nodes with high spectral similarities.GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs.We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks.Our experiments show that GraphZoom increases the classification accuracy and significantly reduces the run time compared to state-of-the-art unsupervised embedding methods.

Un approccio spettrale multilivello per migliorare la qualità e la scalabilità dell'embedding non supervisionata dei grafi.


Deep generative models have been enjoying success in modeling continuous data.However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures.How to generate both syntactically and semantically correct data still remains largely an open problem.Inspired by the theory of compiler where syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes.This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder.Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable.We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization.The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.

Un nuovo modello generativo per dati strutturati discreti. Lo stochastic lazy attribute proposto converte il controllo semantico offline in una guida online per la decodifica stocastica, che affronta efficacemente i vincoli nella sintassi e nella semantica, e raggiunge anche prestazioni superiori


Modeling informal inference in natural language is very challenging.With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance.Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data?If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it?In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge.We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset.

i modelli proposti con la conoscenza esterna migliorano ulteriormente lo stato dell'arte sul dataset SNLI.


High intra-class diversity and inter-class similarity is a characteristic of remote sensing scene image data sets currently posing significant difficulty for deep learning algorithms on classification tasks.To improve accuracy, post-classificationmethods have been proposed for smoothing results of model predictions.However, those approaches require an additional neural network to perform the smoothing operation, which adds overhead to the task.We propose an approach that involves learning deep features directly over neighboring scene images without requiring use of a cleanup model.Our approach utilizes a siamese network to improve the discriminative power of convolutional neural networks on a pairof neighboring scene images.It then exploits semantic coherence between this pair to enrich the feature vector of the image for which we want to predict a label.Empirical results show that this approach provides a viable alternative to existing methods.For example, our model improved prediction accuracy by 1 percentage point and dropped the mean squared error value by 0.02 over the baseline, on a disease density estimation task.These performance gains are comparable with results from existing post-classification methods, moreover without implementation overheads.

Approccio per migliorare l'accuratezza della predizione attraverso l'apprendimento di feature profonde sulle immagini di scena vicine nell'analisi delle immagini di scena del satellite.


Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems.The discretization error becomes even larger when the sparse data are irregularly distributed so that the data defined on an unstructured grid, making it hard to build deep learning models to handle physics-governing observations on the unstructured grid.In this paper, we propose a novel architecture named Physics-aware Difference Graph Networks (PA-DGN) that exploits neighboring information to learn finite differences inspired by physics equations.PA-DGN further leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given observations.We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from weather stations.

Proponiamo reti di grafi di differenza physics-aware, progettate per imparare efficacemente le differenze spaziali per modellare le dinamiche scarsamente osservate.


Solving long-horizon sequential decision making tasks in environments with sparse rewards is a longstanding problem in reinforcement learning (RL) research.Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.Despite the success of recent works in dealing with inherent nonstationarity and sample complexity, it remains difficult to generalize to unseen environments and to transfer different layers of the policy to other agents.In this paper, we propose a novel HRL architecture, Hierarchical Decompositional Reinforcement Learning (HiDe), which allows decomposition of the hierarchical layers into independent subtasks, yet allows for joint training of all layers in end-to-end manner.The main insight is to combine a control policy on a lower level with an image-based planning policy on a higher level.We evaluate our method on various complex continuous control tasks for navigation, demonstrating that generalization across environments and transfer of higher level policies can be achieved.See videos https://sites.google.com/view/hide-rl

Apprendimento di gerarchie funzionalmente decomposte per task di navigazione continua


Sound correspondence patterns play a crucial role for linguistic reconstruction.Linguistsuse them to prove language relationship, to reconstruct proto-forms, and for classicalphylogenetic reconstruction based on shared innovations.Cognate words which fail toconform with expected patterns can further point to various kinds of exceptions in soundchange, such as analogy or assimilation of frequent words.Here we present an automaticmethod for the inference of sound correspondence patterns across multiple languages basedon a network approach.The core idea is to represent all columns in aligned cognate sets asnodes in a network with edges representing the degree of compatibility between the nodes.The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique.The resulting partitions represent all correspondence patterns which can beinferred for a given dataset.By excluding those patterns which occur in only a few cognatesets, the core of regularly recurring sound correspondences can be inferred.Based on thisidea, the paper presents a method for automatic correspondence pattern recognition, whichis implemented as part of a Python library which supplements the paper.To illustrate theusefulness of the method, various tests are presented, and concrete examples of the outputof the method are provided.In addition to the source code, the study is supplemented bya short interactive tutorial that illustrates how to use the new method and how to inspectits results.

L'articolo descrive un nuovo algoritmo che permette di dedurre i pattern di corrispondenza sonora per più lingue.


We describe Kernel RNN Learning (KeRNL), a reduced-rank, temporal eligibility trace-based approximation to backpropagation through time (BPTT) for training recurrent neural networks (RNNs) that gives competitive performance to BPTT on long time-dependence tasks.The approximation replaces a rank-4 gradient learning tensor, which describes how past hidden unit activations affect the current state, by a simple reduced-rank product of a sensitivity weight and a temporal eligibility trace.In this structured approximation motivated by node perturbation, the sensitivity weights and eligibility kernel time scales are themselves learned by applying perturbations.The rule represents another step toward biologically plausible or neurally inspired ML, with lower complexity in terms of relaxed architectural requirements (no symmetric return weights), a smaller memory demand (no unfolding and storage of states over time), and a shorter feedback time.

Una regola di apprendimento biologicamente plausibile per il training di reti neurali ricorrenti


We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks.In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups.We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

Un nuovo metodo per unsupervised representation learning sui grafi, basato sulla massimizzazione dell'informazione reciproca tra le rappresentazioni locali e globali in un grafo. Risultati allo stato dell'arte, competitivi con l'apprendimento supervised.


In many environments only a tiny subset of all states yield high reward.  In these cases, few of the interactions with the environment provide a relevant learning signal.Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a \textit{backtracking model} that predicts the preceding states that terminate at a given high-reward state.  We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and samples which (state, action)-tuples may have led to that high value state.These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy.We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards.Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.

Un modello backward della precedente coppia (stato, azione) dato lo stato successivo, cioè P(s_t, a_t | s_{t+1}), può essere usato per simulare ulteriori traiettorie che terminano negli stati di interesse! Migliora l'efficienza dell'apprendimento RL.


Prepositions are among the most frequent words.Good prepositional representation  is of great syntactic and semantic interest  in computational linguistics.Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations.In this paper we use word-triple counts (one of the words is a preposition) to  capture the preposition's interaction with its head and children.Prepositional  embeddings are derived via tensor decompositions on a large unlabeled corpus.  We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs.Furthermore, our prepositional  embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation.We achieve comparable to or better results than state of the art on  multiple standardized datasets.

Questo lavoro riguarda il metodo basato sui tensori per preposition representation training.


A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network.   Likelihood-based training  of  these  models  requires  restricting  their  architectures  to  allow  cheap computation of Jacobian determinants.  Alternatively, the Jacobian trace can be used if the transformation is specified by an ordinary differential equation.In this paper, we use Hutchinson’s trace estimator to give a scalable unbiased estimate of the log-density.  The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.We demonstrate our approach on high-dimensional density  estimation,  image  generation,  and  variational  inference,  achieving  the state-of-the-art among exact likelihood methods with efficient sampling.

Usiamo dinamiche a tempo continuo per definire un modello generativo con verosimiglianze esatte e un campionamento efficiente che è parametrizzato da reti neurali non limitate.


We propose a non-adversarial feature matching-based approach to train generative models.Our approach, Generative Feature Matching Networks (GFMN), leverages pretrained neural networks such as autoencoders and ConvNet classifiers to perform feature extraction.We perform an extensive number of experiments with different challenging datasets, including ImageNet.Our experimental results demonstrate that, due to the expressiveness of the features from pretrained ImageNet classifiers, even by just matching first order statistics, our approach can achieve state-of-the-art results for challenging benchmarks such as CIFAR10 and STL10.

Un nuovo approccio non-adversariale basato sulla corrispondenza delle feature per addestrare modelli generativi che raggiunge risultati allo stato dell'arte.


We propose a novel architecture for k-shot classification on the Omniglot dataset.Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks.Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification.In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix.Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights.We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters.We report results consistent with state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime on the Omniglot dataset.We explore artificially down-sampling a fraction of images in the training set, which improves our performance.Our experiments therefore lead us to hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.

Una nuova architettura per la classificazione few shot in grado di gestire l'incertezza.


We show that the output of a (residual) CNN with an appropriate prior over the weights and biases is a GP in the limit of infinitely many convolutional filters, extending similar results for dense networks.For a CNN, the equivalent kernel can be computed exactly and, unlike "deep kernels", has very few parameters: only the hyperparameters of the original CNN.Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer.The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GP with a comparable number of parameters.

Mostriamo che le CNN e le ResNet con appropriati prior sui parametri sono processi gaussiani nel limite di infiniti filtri convoluzionali.


We present an end-to-end design methodology for efficient deep learning deployment.Unlike previous methods that separately optimize the neural network architecture, pruning policy, and quantization policy, we jointly optimize them in an end-to-end manner.To deal with the larger design space it brings, we train a quantization-aware accuracy predictor that fed to the evolutionary search to select the best fit.We first generate a large dataset of <NN architecture, ImageNet accuracy> pairs without training each architecture, but by sampling a unified supernet.Then we use these data to train an accuracy predictor without quantization, further using predictor-transfer technique to get the quantization-aware predictor, which reduces the amount of post-quantization fine-tuning time.Extensive experiments on ImageNet show the benefits of the end-to-end methodology: it maintains the same accuracy (75.1%) as ResNet34 float model while saving 2.2× BitOps comparing with the 8-bit model; we obtain the same level accuracy as MobileNetV2+HAQ while achieving 2×/1.3× latency/energy saving; the end-to-end optimization outperforms separate optimizations using ProxylessNAS+AMC+HAQ by 2.3% accuracy while reducing orders of magnitude GPU hours and CO2 emission.

Presentiamo una metodologia di progettazione end-to-end per l'implementazione efficiente del deep learning.


Distributed optimization is vital in solving large-scale machine learning problems.A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch.In such settings, slow nodes, called stragglers, can greatly slow progress.To mitigate the impact of stragglers, we propose an online distributed optimization method called Anytime Minibatch.In this approach, all nodes are given a fixed time to compute the gradients of as many data samples as possible.The result is a variable per-node minibatch size.Workers then get a fixed communication time to average their minibatch gradients via several rounds of consensus, which are then used to update primal variables via dual averaging.Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete.We present a convergence analysis and analyze the wall time performance.Our numerical results show that our approach is up to 1.5 times faster in Amazon EC2 and it is up to five times faster when there is greater variability in compute node performance.

Accelerare l'ottimizzazione distribuita sfruttando gli straggler.


Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification.Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm.This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached.Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene.As such, we propose a novel evaluation measure, parametric norm-balls, by directly perturbing physical parameters that underly image formation.One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry.Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.

Grazie a un nuovo renderer differenziabile, proponiamo una nuova metrica che ha implicazioni nel mondo reale per valutare gli algoritmi di adversarial machine learning, risolvendo la mancanza di realismo della metrica esistente basata sulle norme dei pixel.


Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences.In spite of their relevance, the computational properties of these alternatives have not yet been fully explored.We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016).We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data.In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete.Our study also reveals some minimal sets of elements needed to obtain these completeness results.

Mostriamo che l'architettura Transformer e la Neural GPU sono Turing complete.


It is usually hard for a learning system to predict correctly on the rare events, and there is no exception for segmentation algorithms.Therefore, we hope to build an alarm system to set off alarms when the segmentation result is possibly unsatisfactory.One plausible solution is to project the segmentation results into a low dimensional feature space, and then learn classifiers/regressors in the feature space to predict the qualities of segmentation results.In this paper, we form the feature space using shape feature which is a strong prior information shared among different data, so it is capable to predict the qualities of segmentation results given different segmentation algorithms on different datasets.The shape feature of a segmentation result is captured using the value of loss function when the segmentation result is tested using a Variational Auto-Encoder(VAE).The VAE is trained using only the ground truth masks, therefore the bad segmentation results with bad shapes become the rare events for VAE and will result in large loss value.By utilizing this fact, the VAE is able to detect all kinds of shapes that are out of the distribution of normal shapes in ground truth (GT).Finally, we learn the representation in the one-dimensional feature space to predict the qualities of segmentation results.We evaluate our alarm system on several recent segmentation algorithms for the medical segmentation task.The segmentation algorithms perform differently on different datasets, but our system consistently provides reliable prediction on the qualities of segmentation results.

Usiamo VAE per catturare la feature di forma per la valutazione automatica della segmentazione


The linear transformations in converged deep networks show fast eigenvalue decay.The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.We use a stochastic approximator to generate histograms of eigenvalues.This allows us to investigate layers with hundreds of thousands of dimensions.We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.

Indaghiamo gli autovalori dei layer lineari nelle deep network e mostriamo che le distribuzioni sviluppano un comportamento heavy-tail durante il training.


Capturing long-range feature relations has been a central issue on convolutional neural networks(CNNs).To tackle this, attempts to integrate end-to-end trainable attention module on CNNs are widespread.Main goal of these works is to adjust feature maps considering spatial-channel correlation inside a convolution layer.In this paper, we focus on modeling relationships among layers and propose a novel structure, 'Recurrent Layer Attention network,' which stores the hierarchy of features into recurrent neural networks(RNNs) that concurrently propagating with CNN and adaptively scales feature volumes of all layers.We further introduce several structural derivatives for demonstrating the compatibility on recent attention modules and the expandability of proposed network.For semantic understanding on learned features, we also visualize intermediate layers and plot the curve of layer scaling coefficients(i.e., layer attention).Recurrent Layer Attention network achieves significant performance enhancement requiring a slight increase on parameters in an image classification task with CIFAR and ImageNet-1K 2012 dataset and an object detection task with Microsoft COCO 2014 dataset.

Proponiamo un nuovo tipo di modulo di attention allenabile end-to-end, che applica equilibri di peso globali tra i layer utilizzando RNN co-propaganti con CNN.


We seek to auto-generate stronger  input features  for ML methods faced with limited training data.Biological neural nets (BNNs) excel at fast learning, implying that they extract highly informative features.In particular, the insect olfactory network  learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; randomized, sparse connectivity into a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights.In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator.Attached as a front-end pre-processor, MothNet's readout neurons provide new features, derived from the original features, for use by standard ML classifiers.These ``insect cyborgs'' (part BNN and part ML method) have significantly better performance than baseline ML methods alone on vectorized MNIST and Omniglot data sets, reducing test set error averages 20% to 55%.The MothNet feature generator also substantially out-performs other feature generating methods including PCA, PLS, and NNs.These results highlight the potential value of BNN-inspired feature generators in the ML context.

Le feature auto-generate dal modello bio-mimetico MothNet migliorano significativamente l'accuratezza di test dei metodi ML standard su MNIST vettorizzato. Le feature generate da MothNet superano anche i generatori di feature standard.


We present an approach for anytime predictions in deep neural networks (DNNs).For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted.Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets.In this work, we study a \emph{general} augmentation to feed-forward networks to form anytime neural networks (ANNs) via auxiliary predictions and losses.Specifically, we point out a blind-spot in recent studies in such ANNs: the importance of high final accuracy.In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level.We achieve such speed-up with simple weighting of anytime losses that oscillate during training.We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.

Concentrandosi maggiormente sulle predizioni finali nei predittori anytime (come le recentissime Multi-Scale-DenseNet), facciamo sì che i piccoli modelli anytime superino quelli grandi che non hanno questa focalizzazione.


Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks).However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units.In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.

Proponiamo una procedura di apprendimento efficiente in termini di memoria che sfrutta la reversibilità dei layer della rete per consentire la progettazione guidata dai dati per l'imaging computazionale su larga scala.


Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.This paper proposes reward distribution using {\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas:(i) inter-agent reward distribution and(ii) auction theory.Auction theory is introduced because inter-agent reward distribution is insufficient for optimization.Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents.NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents.Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.

Neuron as an Agent (NaaA) ci permette di allenare la comunicazione multi-agente senza una terza parte fidata.


Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation.To address these problems,  we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT.Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT.We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs.As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

Un nuovo metodo di pretraining che stabilisce nuovi risultati allo stato dell'arte sui benchmark GLUE, RACE e SQuAD pur avendo meno parametri rispetto a BERT-large.


Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey.Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data.The recent work of Super Characters method using two-dimensional word embeddings achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data.For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification.Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.

Deep learning su dati tabulari strutturati utilizzando word embedding bidimensionali con un modello CNN pretrained su ImageNet.


Predictive coding, within theoretical neuroscience, and variational autoencoders, within machine learning, both involve latent Gaussian models and variational inference.While these areas share a common origin, they have evolved largely independently.We outline connections and contrasts between these areas, using their relationships to identify new parallels between machine learning and neuroscience.We then discuss specific frontiers at this intersection: backpropagation, normalizing flows, and attention, with mutual benefits for both fields.

Connessioni tra predictive coding e VAE + nuove frontiere


Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates.Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates.Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.In our paper, we demonstrate that extreme learning rates can lead to poor performance.We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence.We further conduct experiments on various popular tasks and models, which is often insufficient in previous work.Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time.Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks.The implementation of the algorithm can be found at https://github.com/Luolc/AdaBound .

Nuove varianti di metodi di ottimizzazione che combinano i vantaggi dei metodi adattivi e non adattivi.


An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain.In many real-world applications, access to the underlying transition operator is limited to a fixed set of data that has already been collected, without additional interaction with the environment being available.We show that consistent estimation remains possible in this scenario, and that effective estimation can still be achieved in important applications.Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions, derived from fundamental properties of the stationary distribution, and exploiting constraint reformulations based on variational divergence minimization.The resulting algorithm, GenDICE, is straightforward and effective.We prove the consistency of the method under general conditions, provide a detailed error analysis, and demonstrate strong empirical performance on benchmark tasks, including off-line PageRank and off-policy policy evaluation.

In questo articolo, abbiamo proposto un nuovo algoritmo, GenDICE, per la stima della correzione della distribuzione stazionaria generale, che può gestire sia la valutazione scontata che la valutazione media off-policy su sample multipli behavior-agnostic.


The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.Numerous rigorous attempts have been made to explain generalization, but available bounds are still quite loose, and analysis does not always lead to true understanding.The goal of this work is to make generalization more intuitive.Using visualization methods, we discuss the mystery of generalization, the geometry of loss landscapes, and how the curse (or, rather, the blessing) of dimensionality causes optimizers to settle into minima that generalize well.

Un'esplorazione empirica e visiva intuitiva delle proprietà di generalizzazione delle deep neural network.


We argue that symmetry is an important consideration in addressing the problemof systematicity and investigate two forms of symmetry relevant to symbolic processes. We implement this approach in terms of convolution and show that it canbe used to achieve effective generalisation in three toy problems: rule learning,composition and grammar learning.

Usiamo la convoluzione per far sì che le reti neurali si comportino in modo più simile ai sistemi simbolici.


Key equatorial climate phenomena such as QBO and ENSO have never been adequately explained as deterministic processes.This in spite of recent research showing growing evidence of predictable behavior.This study applies the fundamental Laplace tidal equations with simplifying assumptions along the equator — i.e. no Coriolis force and a small angle approximation.The solutions to the partial differential equations are highly non-linear related to Navier-Stokes and only search approaches can be used to fit to the data.

Formulazione analitica dei fenomeni di onde stazionarie equatoriali: Applicazione a QBO e ENSO


In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks.Several solutions have been proposed to tackle this problem but they usually  assume that the user knows which of the tasks to perform at test time on a particular sample, or rely on small samples from previous data and most of them suffer of a substantial drop in accuracy when updated with batches of only one class at a time.In this article, we propose a new method, OvA-INN, which is able to learn one class at a time and without storing any of the previous data.To achieve this, for each class, we train a specific Invertible Neural Network to output the zero vector for its class.At test time, we can predict the class of a sample by identifying which network outputs the vector with the smallest norm.With this method, we show that we can take advantage of pretrained models by stacking an invertible network on top of a features extractor.This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets.In our experiments, we are reaching 72% accuracy on CIFAR-100 after training our model one class at a time.

Proponiamo di addestrare una rete neurale invertibile per ogni classe per eseguire continual learning classe per classe.


Human-computer conversation systems have attracted much attention in Natural Language Processing.Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems.Retrieval systems search a user-issued utterance (namely a query) in a large conversational repository and return a reply that best matches the query.Generative approaches synthesize new replies.Both ways have certain advantages but suffer from their own disadvantages.We propose a novel ensemble of retrieval-based and generation-based conversation system.The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information.The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output.Experimental results show that such an ensemble system outperforms each single module by a large margin.

Un nuovo ensemble basato sul recupero e sulla generazione per sistemi di conversazione open-domain.


Deep neural networks trained on a wide range of datasets demonstrate impressive transferability.Deep features appear general in that they are applicable to many datasets and tasks.Such property is in prevalent use in real-world applications.A neural network pretrained on large datasets, such as ImageNet, can significantly boost generalization and accelerate training if fine-tuned to a smaller target dataset.Despite its pervasiveness, few effort has been devoted to uncovering the reason of transferability in deep feature representations.This paper tries to understand transferability from the perspectives of improved generalization, optimization and the feasibility of transferability.We demonstrate that1) Transferred models tend to find flatter minima, since their weight matrices stay close to the original flat region of pretrained parameters when transferred to a similar target dataset;2) Transferred representations make the loss landscape more favorable with improved Lipschitzness, which accelerates and stabilizes training substantially.The improvement largely attributes to the fact that the principal component of gradient is suppressed in the pretrained parameters, thus stabilizing the magnitude of gradient in back-propagation.3) The feasibility of transferability is related to the similarity of both input and label.And a surprising discovery is that the feasibility is also impacted by the training stages in that the transferability first increases during training, and then declines.We further provide a theoretical analysis to verify our observations.

Capire la trasferibilità dal punto di vista di una migliore generalizzazione, dell'ottimizzazione e della fattibilità della trasferibilità.


We address the following question: How redundant is the parameterisation of ReLU networks?Specifically, we consider transformations of the weight space which leave the function implemented by the network intact.Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights.In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations.For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling.The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest.

Dimostriamo che esistono reti ReLU i cui parametri sono determinati quasi unicamente dalla funzione che implementano.


A general problem that received considerable recent attention is how to perform multiple tasks in the same network, maximizing both efficiency and prediction accuracy.A popular approach consists of a multi-branch architecture on top of ashared backbone, jointly trained on a weighted sum of losses.However, in many cases, the shared representation results in non-optimal performance, mainly due to an interference between conflicting gradients of uncorrelated tasks.Recent approaches address this problem by a channel-wise modulation of the feature-maps along the shared backbone, with task specific vectors, manually or dynamically tuned.Taking this approach a step further, we propose a novel architecture whichmodulate the recognition network channel-wise, as well as spatial-wise, with an efficient top-down image-dependent computation scheme.Our architecture uses no task-specific branches, nor task specific modules.Instead, it uses a top-down modulation network that is shared between all of the tasks.We show the effectiveness of our scheme by achieving on par or better results than alternative approaches on both correlated and uncorrelated sets of tasks.We also demonstrate our advantages in terms of model size, the addition of novel tasks and interpretability. Code will be released.

Proponiamo una rete di modulazione top-down per applicazioni di multi-task learning con diversi vantaggi rispetto agli schemi attuali.


Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis.The performance of a clustering algorithm depends on the features extracted from the data.However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations.In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method.Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.

Nuovo algoritmo di clustering di dati di serie temporali basato sulle feature del sistema dinamico.


Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by regularizing the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior.Previous work has taken important steps to connect these topics through various forms of gradient regularization.We find, however, that existing methods that use attributions to align a model's behavior with human intuition are ineffective.We develop an efficient and theoretically grounded feature attribution method, expected gradients, and a novel framework, attribution priors, to enforce prior expectations about a model's behavior during training.We demonstrate that attribution priors are broadly applicable by instantiating them on three different types of data: image data, gene expression data, and health care data.Our experiments show that models trained with attribution priors are more intuitive and achieve better generalization performance than both equivalent baselines and existing methods to regularize model behavior.

Un metodo per incoraggiare l'attribuzione assiomatica delle feature di un deep model che corrisponda all'intuizione umana.


Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data.However, they are both complex and memory intensive due to their recursive nature.These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources.To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs.As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing significant benefits to custom hardware in terms of silicon area and power consumption.On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) and gated recurrent units (GRUs) on various sequential models including sequence classification and language modeling.We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime.On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights.Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12x memory saving and 10x inference speedup compared to the full-precision hardware implementation design.

Proponiamo LSTM ad alte prestazioni con pesi binari/ternari, che possono ridurre notevolmente la complessità di implementazione


This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled and do not share classes with labeled support images for few-shot recognition in testing.We use a new GAN-like deep architecture aimed at unsupervised learning of an image representation which will encode latent object parts and thus generalize well to unseen classes in our few-shot recognition task.Our unsupervised training integrates adversarial, self-supervision, and deep metric learning.We make two contributions.First, we extend the vanilla GAN with reconstruction loss to enforce the discriminator capture the most relevant characteristics of "fake" images generated from randomly sampled codes.Second, we compile a training set of triplet image examples for estimating the triplet loss in metric learning by using an image masking procedure suitably designed to identify latent object parts.Hence, metric learning ensures that the deep representation of images showing similar object classes which share some parts are closer than the representations of images which do not have common parts.Our results show that we significantly outperform the state of the art, as well as get similar performance to the common episodic training for fully-supervised few-shot learning on the Mini-Imagenet and Tiered-Imagenet datasets.

Affrontiamo il problema dell'unsupervised few-shot object recognition, dove tutte le immagini di allenamento sono prive di label e non condividono le classi con le immagini di test.


Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models.In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability.We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input.An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs.We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks.We also apply the Gradient Estimation attacks successfully against a real-world content moderation classiﬁer hosted by Clarifai.Furthermore, we evaluate black-box attacks against state-of-the-art defenses.We show that the Gradient Estimation attacks are very effective even against these defenses.

Attacchi black-box basati su query alle deep neural network con tassi di successo adversarial che corrispondono agli attacchi white-box


We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks.The graph image representation is based on 2D image embeddings of adjacency matrices.We use this image representation in two modes.First, as the input to a machine learning algorithm.Second, as the input to a pure transfer learner.Our conclusions from multiple datasets are that1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and,2. pure transfer learning works effectively with minimum interference from the user and is robust against small data.

Convertiamo i sottografi in immagini strutturate e li classifichiamo usando 1. deep learning e 2. transfer learning (Caffe) e otteniamo risultati sorprendenti.


This paper explores the use of self-ensembling for visual domain adaptation problems.Our technique is derived from the mean teacher variant (Tarvainen et. al 2017) of temporal ensembling (Laine et al. 2017), a technique that achieved state of the art results in the area of semi-supervised learning.We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness.Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge.In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.

Algoritmo basato sul self-ensembling per visual domain adaptation, risultati allo stato dell'arte, ha vinto la challenge VisDA-2017 di image classification domain adaptation.


It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before.We call the ability to create images of novel semantic concepts visually grounded imagination.In this paper, we show how we can modify variational auto-encoders to perform this task.Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way.We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C’s).Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et al., 2017 and the BiVCCA method of Wang et al., 2016) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset (Liu et al., 2015).

Una variante VAE che può creare diverse immagini corrispondenti a nuovi "concetti" concreti o astratti descritti usando vettori di attributi.


We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS).In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.The new Q-estimates are then used in combination with real experience to update the prior.This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search.SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari.SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets.By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.

Proponiamo un metodo basato sul modello chiamato "Search with Amortized Value Estimates" (SAVE) che sfrutta sia l'esperienza reale che quella pianificata combinando Q-learning con Monte-Carlo Tree Search, ottenendo buone prestazioni con budget di ricerca molto piccoli.


Two main families of reinforcement learning algorithms, Q-learning and policy gradients, have recently been proven to be equivalent when using a softmax relaxation on one part, and an entropic regularization on the other.We relate this result to the well-known convex duality of Shannon entropy and the softmax function.Such a result is also known as the Donsker-Varadhan formula.This provides a short proof of the equivalence.We then interpret this duality further, and use ideas of convex analysis to prove a new policy inequality relative to soft Q-learning.

Una breve dimostrazione dell'equivalenza del soft Q-learning e dei policy gradient.


Computer simulation provides an automatic and safe way for training robotic controlpolicies to achieve complex tasks such as locomotion.However, a policytrained in simulation usually does not transfer directly to the real hardware dueto the differences between the two environments.Transfer learning using domainrandomization is a promising approach, but it usually assumes that the target environmentis close to the distribution of the training environments, thus relyingheavily on accurate system identification.In this paper, we present a differentapproach that leverages domain randomization for transferring control policies tounknown environments.The key idea that, instead of learning a single policy inthe simulation, we simultaneously learn a family of policies that exhibit differentbehaviors.When tested in the target environment, we directly search for the bestpolicy in the family based on the task performance, without the need to identifythe dynamic parameters.We evaluate our method on five simulated robotic controlproblems with different discrepancies in the training and testing environmentand demonstrate that our method can overcome larger modeling errors comparedto training a robust policy or an adaptive policy.

Proponiamo un algoritmo di transfer della policy che può superare grandi e impegnative discrepanze nella dinamica del sistema come la latenza, l'errore di modellazione degli attuatori, ecc.


We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations.Discretization enables the direct application of algorithms from the NLP community which require discrete inputs.Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.

Impara a quantizzare il segnale del discorso e ad applicare algoritmi che richiedono input discreti ai dati audio come BERT.


Deep Reinforcement Learning algorithms lead to agents that can solve difficult decision making problems in complex environments.However, many difficult multi-agent competitive games, especially real-time strategy games are still considered beyond the capability of current deep reinforcement learning algorithms, although there has been a recent effort to change this \citep{openai_2017_dota, vinyals_2017_starcraft}.Moreover, when the opponents in a competitive game are suboptimal, the current \textit{Nash Equilibrium} seeking, self-play algorithms are often unable to generalize their strategies to opponents that play strategies vastly different from their own.This suggests that a learning algorithm that is beyond conventional self-play is necessary.We develop Hierarchical Agent with Self-play (HASP), a learning approach for obtaining hierarchically structured policies that can achieve higher performance than conventional self-play on competitive games through the use of a diverse pool of sub-policies we get from Counter Self-Play (CSP).We demonstrate that the ensemble policy generated by HASP can achieve better performance while facing unseen opponents that use sub-optimal policies.On a motivating iterated Rock-Paper-Scissor game and a partially observable real-time strategic game (http://generals.io/), we are led to the conclusion that HASP can perform better than conventional self-play as well as achieve 77% win rate against FloBot, an open-source agent which has ranked at position number 2 on the online leaderboards.

Sviluppiamo Hierarchical Agent with Self-play (HASP), un approccio di apprendimento per ottenere policy strutturate gerarchicamente che possono raggiungere alte prestazioni rispetto al convenzionale self-play su giochi strategici competitivi real-time.


We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity.There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units.We perform a systematic comparison of popular choices for a self-attentional architecture.Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters.On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity.

Word embedding a capacità variabile e SOTA su WikiText-103, benchmark Billion Word.


In this paper, we consider the problem of detecting object under occlusion.Most object detectors formulate bounding box regression as a unimodal task (i.e., regressing a single set of bounding box coordinates independently).However, we observe that the bounding box borders of an occluded object can have multiple plausible configurations.Also, the occluded bounding box borders have correlations with visible ones.Motivated by these two observations, we propose a deep multivariate mixture of Gaussians model for bounding box regression under occlusion.The mixture components potentially learn different configurations of an occluded part, and the covariances between variates help to learn the relationship between the occluded parts and the visible ones.Quantitatively, our model improves the AP of the baselines by 3.9% and 1.2% on CrowdHuman and MS-COCO respectively with almost no computational or memory overhead.Qualitatively, our model enjoys explainability since we can interpret the resulting bounding boxes via the covariance matrices and the mixture components.

Una deep multivariate mixture di modelli gaussiani per la regressione bounding box sotto occlusione


Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models.  Adversarial training, one of the most successful empirical defenses to adversarial examples, refers to training on adversarial examples generated within a geometric constraint set.The most commonly used geometric constraint is an $L_p$-ball of radius $\epsilon$ in some norm.We introduce adversarial training with Voronoi constraints, which replaces the $L_p$-ball constraint with the Voronoi cell for each point in the training set.We show that adversarial training with Voronoi constraints produces robust models which significantly improve over the state-of-the-art on MNIST and are competitive on CIFAR-10.

Sostituiamo il vincolo della sfera Lp con le celle di Voronoi dei training data per produrre modelli più robusti.


Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog.However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments.In order to close the gap between seen and unseen environments, we aim at learning a generalizable navigation model from two novel perspectives:(1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks;(2) we propose to learn environment-agnostic representations for navigation policy that are invariant among environments, thus generalizing better on unseen environments.Extensive experiments show that our environment-agnostic multitask navigation model significantly reduces the performance gap between seen and unseen environments and outperforms the baselines on unseen environments by 16% (relative measure on success rate) on VLN and 120% (goal progress) on NDH, establishing the new state of the art for NDH task.

Proponiamo di imparare una policy più generalizzata per task di navigazione basati sul linguaggio naturale attraverso un multitask learning indipendente dall'ambiente.


In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings.Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models.We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation.These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.

Proponiamo di usare i baricentri di Wasserstein per l'ensemble di modelli semantici


While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a challenge.The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combination of multitask learning and self-supervised learning on unlabeled data.We trained an end-to-end audio feature extractor based on WaveNet that feeds into simple, yet versatile task-specific neural networks.We describe several easily implemented self-supervised learning tasks that can operate on any large, unlabeled audio corpus.We demonstrate that, in scenarios with limited labeled training data, one can significantly improve the performance of three different supervised classification tasks individually by up to 6% through simultaneous training with these additional self-supervised tasks.We also show that incorporating data augmentation into our multitask setting leads to even further gains in performance.

Classificazione audio label-efficient attraverso multi-task learning e self-supervision


Recent neural network and language models have begun to rely on softmax distributions with an extremely large number of categories.In this context calculating the softmax normalizing constant is prohibitively expensive.This has spurred a growing literature of efficiently computable but biased estimates of the softmax.In this paper we present the first two unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and does not require extra work at the end of each epoch).We compare our unbiased methods' empirical performance to the state-of-the-art on seven real world datasets, where they comprehensively outperform all competitors.

Proponiamo i primi metodi per ottimizzare esattamente la distribuzione softmax usando lo stochastic gradient con un tempo di esecuzione indipendente dal numero di classi o dai datapoint.


Crafting adversarial examples on discrete inputs like text sequences is fundamentally different from generating such examples for continuous inputs like images.This paper tries to answer the question: under a black-box setting, can we create adversarial examples automatically to effectively fool deep learning classifiers on texts by making imperceptible changes?Our answer is a firm yes.Previous efforts mostly replied on using gradient evidence, and they are less effective either due to finding the nearest neighbor word (wrt meaning) automatically is difficult or relying heavily on hand-crafted linguistic rules.We, instead, use Monte Carlo tree search (MCTS) for finding the most important few words to perturb and perform homoglyph attack by replacing one character in each selected word with a symbol of identical shape.  Our novel algorithm, we call MCTSBug, is black-box and extremely effective at the same time.Our experimental results indicate that MCTSBug can fool deep learning classifiers at the success rates of 95% on seven large-scale benchmark datasets, by perturbing only a few characters.  Surprisingly, MCTSBug, without relying on gradient information at all, is more effective than the gradient-based white-box baseline.Thanks to the nature of homoglyph attack, the generated adversarial perturbations are almost imperceptible to human eyes.

Utilizzare Monte carlo Tree Search e Homoglyphs per generare adversarial sample indistinguibili su dati di testo


We introduce a model that learns to convert simple hand drawings  into graphics programs written in a subset of \LaTeX.~The model  combines techniques from deep learning and program synthesis.  We  learn a convolutional neural network that proposes plausible drawing  primitives that explain an image.These drawing primitives are like  a trace of the set of primitive commands issued by a graphics  program.We learn a model that uses program synthesis techniques to  recover a graphics program from that trace.These programs have  constructs like variable bindings, iterative loops, or simple kinds  of conditionals.With a graphics program in hand, we can correct  errors made by the deep network and extrapolate drawings.  Taken  together these results are a step towards agents that induce useful,  human-readable programs from perceptual input.

Imparare a convertire uno schizzo disegnato a mano in un programma ad alto livello


Adversarial examples remain an issue for contemporary neural networks.This paper draws on Background Check (Perello-Nieto et al., 2016), a technique in model calibration, to assist two-class neural networks in detecting adversarial examples, using the one dimensional difference between logit values as the underlying measure.This method interestingly tends to achieve the highest average recall on image sets that are generated with large perturbation vectors, which is unlike the existing literature on adversarial attacks (Cubuk et al., 2017).The proposed method does not need knowledge of the attack parameters or methods at training time, unlike a great deal of the literature that uses deep learning based methods to detect adversarial examples, such as Metzen et al. (2017), imbuing the proposed method with additional flexibility.

Questo documento utilizza i principi del campo della calibrazione nell'apprendimento automatico sui logit di una rete neurale per difendersi dagli adversarial attack


We present a novel multi-task training approach to learning multilingual distributed representations of text.Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model.We construct sentence embeddings by processing word embeddings with an LSTM and by taking an average of the outputs.Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone.Our model shows competitive performance in a standard cross-lingual document classification task.We also show the effectiveness of our method in a low-resource scenario.

Addestriamo congiuntamente un modello skip-gram multilingue e un modello di cross-lingual sentence similarity per imparare embedding di testo multilingue di alta qualità che si comportano bene nello scenario low resource.


Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling.However, they are often hard to train, and learning of GANs often becomes unstable.Wasserstein GAN (WGAN) is a promising framework to deal with the instability problem as it has a good convergence property.One drawback of the WGAN is that it evaluates the Wasserstein distance in the dual domain, which requires some approximation, so that it may fail to optimize the true Wasserstein distance.In this paper, we propose evaluating the exact empirical optimal transport cost efficiently in the primal domain and performing gradient descent with respect to its derivative to train the generator network.Experiments on the MNIST dataset show that our method is significantly stable to converge, and achieves the lowest Wasserstein distance among the WGAN variants at the cost of some sharpness of generated images.Experiments on the 8-Gaussian toy dataset show that better gradients for the generator are obtained in our method.In addition, the proposed method enables more flexible generative modeling than WGAN.

Abbiamo proposto un modello generativo flessibile che impara in modo stabile minimizzando direttamente la distanza empirica esatta di Wasserstein.


Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012.Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue.While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all.As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others.Our first contribution is a benchmark of 8 NAS methods on 5 datasets.To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols.Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline.We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline.These experiments highlight that:(i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures;(ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings;(iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and(iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures.To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods.Thecode used is available at https://github.com/antoyang/NAS-Benchmark.

Uno studio di come i diversi componenti della pipeline NAS contribuiscano alla precisione finale. Inoltre, un benchmark di 8 metodi su 5 dataset.


Identifying analogies across domains without supervision is a key task for artificial intelligence.Recent advances in cross domain image mapping have concentrated on translating images across domains.Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain.In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques.We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function.The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.

Trovare le corrispondenze tra i domini eseguendo iterazioni di corrispondenza/mappatura


Effectively inferring discriminative and coherent latent topics of short texts is a critical task for many real world applications.Nevertheless, the task has been proven to be a great challenge for traditional topic models due to the data sparsity problem induced by the characteristics of short texts.Moreover, the complex inference algorithm also become a bottleneck for these traditional models to rapidly explore variations.In this paper, we propose a novel model called Neural Variational Sparse Topic Model (NVSTM) based on a sparsity-enhanced topic model named Sparse Topical Coding (STC).In the model, the auxiliary word embeddings are utilized to improve the generation of representations.The Variational Autoencoder (VAE) approach is applied to inference the model efficiently, which makes the model easy to explore extensions for its black-box inference process.Experimental results onWeb Snippets, 20Newsgroups, BBC and Biomedical datasets show the effectiveness and efficiency of the model.

Un topic model neurale con sparsità basato su VAE


Neural Tangents is a library for working with infinite-width neural networks.It provides a high-level API for specifying complex and hierarchical neural network architectures.These networks can then be trained and evaluated either at finite-width as usual, or in their infinite-width limit.For the infinite-width networks, Neural Tangents performs exact inference either via Bayes' rule or gradient descent, and generates the corresponding Neural Network Gaussian Process and Neural Tangent kernels.Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks. The entire library runs out-of-the-box on CPU, GPU, or TPU.All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. In addition to the repository below, we provide an accompanying interactive Colab notebook athttps://colab.sandbox.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb


Symbolic logic allows practitioners to build systems that perform rule-based reasoning which is interpretable and which can easily be augmented with prior knowledge.However, such systems are traditionally difficult to apply to problems involving natural language due to the large linguistic variability of language.Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.We propose to reap the benefits of both approaches by applying a combination of neural networks and logic programming to natural language question answering.We propose to employ an external, non-differentiable Prolog prover which utilizes a similarity function over pretrained sentence encoders.We fine-tune these representations via Evolution Strategies with the goal of multi-hop reasoning on natural language.  This allows us to create a system that can apply rule-based reasoning to natural language and induce domain-specific natural language rules from training data.We evaluate the proposed system on two different question answering tasks, showing that it complements two very strong baselines – BIDAF (Seo et al., 2016a) and FASTQA (Weissenborn et al.,2017) – and outperforms both when used in an ensemble.

Introduciamo NLProlog, un sistema che esegue ragionamenti basati su regole sul linguaggio naturale sfruttando pretrained sentence embedding e il fine-tuning con strategie di evoluzione, e lo applichiamo a due task di question answering multi-hop.


Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.This creates a fundamental quality-versus-quantity trade-off in the learning process.  Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data?We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds.   To this end, we introduce “fidelity-weighted learning” (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.

Proponiamo il Fidelity-weighted Learning, un approccio semi-supervised teacher-student per il training delle reti neurali usando dati weakly-labeled.


This paper is focused on investigating and demystifying an intriguing robustness phenomena in over-parameterized neural network training.In particular we provide empirical and theoretical evidence that first order methods such as gradient descent are provably robust to noise/corruption on a constant fraction of the labels despite over-parameterization under a rich dataset model.In particular:i) First, we show that in the first few iterations where the updates are still in the vicinity of the initialization these algorithms only fit to the correct labels essentially ignoring the noisy labels.ii) Secondly, we prove that to start to overfit to the noisy labels these algorithms must stray rather far from from the initial model which can only occur after many more iterations.Together, these show that gradient descent with early stopping is provably robust to label noise and shed light on empirical robustness of deep networks as well as commonly adopted early-stopping heuristics.

Dimostriamo che il gradient descent è robusto alla corruzione delle label nonostante la sovra-parametrizzazione sotto un rich dataset model.


Weight pruning has been introduced as an efficient model compression technique.Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information.Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently.As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested.Decoding non-zero weights, however, is still sequential in Viterbi-based pruning.In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix.The proposed sparse matrix is constructed by combining pruning and weight quantization.For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19x using the proposed sparse matrix format compared to the baseline model.Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders.Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.

Presentiamo un nuovo schema di codifica dei pesi che permette un alto rapporto di compressione e una veloce conversione della matrice da sparsa a densa.


The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys, capable of learning the statistical properties of the seismic wavelets.The usage of validating and performance testing of compressive sensing are three steps.First, the existence of a sparse representation with different compression rates for seismic surveys is studied.Then, non-uniform samplings are studied, using the proposed methodology.Finally, recommendations for non-uniform seismic survey grid, based on the evaluation of reconstructed seismic images and metrics, is proposed.The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition, with less loss in imaging quality.Along these lines, a compressive sensing design of a non-uniform grid over an asset in Gulf of Mexico, versus a traditional seismic survey grid which collects data uniformly at every few feet, is suggested, leveraging the proposed method.

Ha migliorato una rete di inpainting dei pixel basata su GAN per il recupero di immagini sismiche compresse e ha proposto una survey reccomendation a sampling non uniforme, che può essere facilmente applicata al settore medico e ad altri domini per la tecnica di compressive sensing.


In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota.One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum.In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative.In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap.In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions.Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.However, when done naively, this randomness will inherently make their actions less informative to others during training.We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase.During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates.By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.

Sviluppiamo Simplified Action Decoder, un semplice algoritmo MARL che batte il precedente SOTA su Hanabi con un grande margine nei giochi da 2 a 5 giocatori.


We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents.It is based on predicting a two-dimensional character grid ('chargrid') representation of a document image as a semantic segmentation task.To identify individual character instances from the chargrid, we regard characters as objects and use object detection techniques from computer vision.We demonstrate experimentally that our method outperforms previous state-of-the-art approaches in accuracy while being easily parallelizable on GPU (thereby being significantly faster), as well as easier to train.

Optical Character Recognition addestrabile end-to-end su documenti stampati; raggiungiamo risultati allo stato dell'arte, battendo Tesseract4 su dataset di riferimento sia in termini di precisione che di tempo di esecuzione, utilizzando un approccio basato esclusivamente su computer vision.


We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW. Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.

NovoGrad - un metodo SGD adattivo con normalizzazione del gradiente a layer e decoupled weight decay.


Most generative models of audio directly generate samples in one of two domains: time or frequency.While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived.A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods.In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods.Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks.Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources.In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning.The library will is available at https://github.com/magenta/ddsp and we encourage further contributions from the community and domain experts.

Migliore sintesi audio combinando il DSP interpretabile con l'apprendimento end-to-end.


Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification.In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size.Current GCNs also restrict graphs to have at most one edge between any pair of nodes.To this end, we propose a novel multigraph network that learns from multi-relational graphs.We explicitly model different types of edges: annotated edges, learned edges with abstract meaning, and hierarchical edges.We also experiment with different ways to fuse the representations extracted from different edge types.This restriction is sometimes implied from a dataset, however, we relax this restriction for all kinds of datasets.We achieve state-of-the-art results on a variety of chemical, social, and vision graph classification benchmarks.

Un nuovo approccio alla classificazione dei grafi basato su graph convolutional neural network spettrali e la sua estensione a multigrafi con relazioni apprendibili e struttura gerarchica. Mostriamo risultati allo stato dell'arte su dataset chimici, sociali e di immagini.