In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node.We here propose that node classes are also associated with topological features of the nodes.We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN.This accuracy is slightly less than the one that can be obtained using content based GCN.Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes.However,  adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.

Graph Convolutional Network (GCN) basata sulla topologia


Flies and mice are species separated by 600 million years of evolution, yet have evolved olfactory systems that share many similarities in their anatomic and functional organization.What functions do these shared anatomical and functional features serve, and are they optimal for odor sensing?In this study, we address the optimality of evolutionary design in olfactory circuits by studying artificial neural networks trained to sense odors.We found that artificial neural networks quantitatively recapitulate structures inherent in the olfactory system, including the formation of glomeruli onto a compression layer and sparse and random connectivity onto an expansion layer.Finally, we offer theoretical justifications for each result.Our work offers a framework to explain the evolutionary convergence of olfactory circuits, and gives insight and logic into the anatomic and functional structure of the olfactory system.

Le reti neurali artificiali hanno sviluppato le stesse strutture presenti nei sistemi olfattivi di mosche e topi dopo essere state addestrate a classificare gli odori


The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations.In this paper, we take a manifold view of the problem by introducing a smoothness term over the sample graph to attain harmonic functions to enforce consistent mappings during the translation.We develop HarmonicGAN to learn bi-directional translations between the source and the target domains.With the help of similarity-consistency, the inherent self-consistency property of samples can be maintained.Distance metrics defined on two types of features including histogram and CNN are exploited.Under an identical problem setting as CycleGAN, without additional manual inputs and only at a small training-time cost, HarmonicGAN demonstrates a significant qualitative and quantitative improvement over the state of the art, as well as improved interpretability.We show experimental results in a number of applications including medical imaging, object transfiguration, and semantic labeling.We outperform the competing methods in all tasks, and for a medical imaging task in particular our method turns CycleGAN from a failure to a success, halving the mean-squared error, and generating images that radiologists prefer over competing methods in 95% of cases.

La regolarizzazione smooth sul grafo del sample per la traduzione da immagine a immagine non accoppiata risulta in una coerenza significativamente migliorata


Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion.Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions.In this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches.Rather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the depth map from the cost volume.The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network.Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.

Una convolution neural network per stereo matching multi-view il cui design è ispirato dalle migliori pratiche degli approcci tradizionali basati sulla geometria


The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact.Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations.In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered.We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds.To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity.  On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned.

Arricchiamo l'apprendimento della policy model-free con funzioni di reward a livello di sequenza e un bonus di visita count-based e dimostriamo l'efficacia nel setting low-round con grandi batch visto nella progettazione di sequenze di DNA e proteine.


Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive.Consider a visual arithmetic task, where the goal is to carry out simple arithmetical algorithms on digits presented under natural conditions (e.g. hand-written, placed randomly).We propose a two-tiered architecture for tackling this kind of problem.The lower tier consists of a heterogeneous collection of information processing modules, which can include pre-trained deep neural networks for locating and extracting characters from the image, as well as modules performing symbolic transformations on the representations extracted by perception.The higher tier consists of a controller, trained using reinforcement learning, which coordinates the modules in order to solve the high-level task.For instance, the controller may learn in what contexts to execute the perceptual networks and what symbolic transformations to apply to their outputs.The resulting model is able to solve a variety of tasks in the visual arithmetic domain,and has several advantages over standard, architecturally homogeneous feedforward networks including improved sample efficiency.

Usiamo reinforcement learning per addestrare un agente a risolvere una serie di task aritmetici visivi usando moduli percettivi pre-addestrati e trasformazioni di rappresentazioni interne create da questi moduli.


Animals develop novel skills not only through the interaction with the environment but also from the influence of the others.In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers.Specifically, we first define a metric to measure the distance between policies then quantitatively derive the definition of uniqueness.Unlike previous precarious joint optimization approaches, the social uniqueness motivation in our work is imposed as a constraint to encourage the agent to learn a policy different from the existing agents while still solve the primal task.The resulting algorithm, namely Interior Policy Differentiation (IPD), brings about performance improvement as well as a collection of policies that solve a given task with distinct behaviors

Un nuovo algoritmo RL chiamato Interior Policy Differentiation è proposto per imparare una collezione di policy diverse per un dato task primario.


Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment.Because of this, while many GAN variants being proposed, we have relatively little understanding of their relative abilities.In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training.We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion.We also compare the proposed metrics to human perceptual scores.

Una valutazione empirica sulle generative adversarial network


Knowledge bases (KB) are often represented as a collection of facts in the form (HEAD, PREDICATE, TAIL), where HEAD and TAIL are entities while PREDICATE is a binary relationship that links the two.It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.However, though frequently ignored, these repositories also contain numerical facts.Numerical facts link entities to numerical values via numerical predicates; e.g., (PARIS, LATITUDE, 48.8).Likewise, numerical facts also suffer from the incompleteness problem.To address this issue, we introduce the numerical attribute prediction problem.This problem involves a new type of query where the relationship is a numerical predicate.Consequently, and contrary to link prediction, the answer to this query is a numerical value.We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base.Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes.An extensive set of experiments on benchmark versions of FREEBASE and YAGO show that our approaches largely outperform sensible baselines.We make the datasets available under a permissive BSD-3 license.

Previsione dei valori numerici degli attributi associati alle entità nelle knowledge base.


We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in analyzing and improving multilingual BERT.In particular, after our proposed alignment procedure, BERT exhibits significantly improved zero-shot performance on XNLI compared to the base model, remarkably matching pseudo-fully-supervised translate-train models for Bulgarian and Greek.Further, to measure the degree of alignment, we introduce a contextual version of word retrieval and show that it correlates well with downstream zero-shot transfer.Using this word retrieval task, we also analyze BERT and find that it exhibits systematic deficiencies, e.g. worse alignment for open-class parts-of-speech and word pairs written in different scripts, that are corrected by the alignment procedure.These results support contextual alignment as a useful concept for understanding large multilingual pre-trained models.

Proponiamo procedure per valutare e rafforzare l'allineamento degli embedding contestuali e mostriamo che entrambe migliorano il transfer XNLI zero shot di multilingual BERT e forniscono utili intuizioni sul modello.


Neural networks have reached outstanding performance for solving various ill-posed inverse problems in imaging.However, drawbacks of end-to-end learning approaches in comparison to classical variational methods are the requirement of expensive retraining for even slightly different problem statements and the lack of provable error bounds during inference.Recent works tackled the first problem by using networks trained for Gaussian image denoising as generic plug-and-play regularizers in energy minimization algorithms.Even though this obtains state-of-the-art results on many tasks, heavy restrictions on the network architecture have to be made if provable convergence of the underlying fixed point iteration is a requirement.More recent work has proposed to train networks to output descent directions with respect to a given energy function with a provable guarantee of convergence to a minimizer of that energy.However, each problem and energy requires the training of a separate network.In this paper we consider the combination of both approaches by projecting the outputs of a plug-and-play denoising network onto the cone of descent directions to a given energy.This way, a single pre-trained network can be used for a wide variety of reconstruction tasks.Our results show improvements compared to classical energy minimization methods while still having provable convergence guarantees.

Usiamo le reti neurali addestrate per il denoising delle immagini come prior plug-and-play in algoritmi di minimizzazione dell'energia per problemi di ricostruzione delle immagini con convergenza dimostrabile.


Knowledge graph embedding research has overlooked the problem of probability calibration.We show popular embedding models are indeed uncalibrated.That means probability estimates associated to predicted triples are unreliable.We present a novel method to calibrate a model when ground truth negatives are not available, which is the usual case in knowledge graphs.We propose to use Platt scaling and isotonic regression alongside our method.Experiments on three datasets with ground truth negatives show our contribution leads to well calibrated models when compared to the gold standard of using negatives.We get significantly better results than the uncalibrated models from all calibration methods.We show isotonic regression offers the best the performance overall, not without trade-offs.We also show that calibrated models reach state-of-the-art accuracy without the need to define relation-specific decision thresholds.

Proponiamo un nuovo metodo per calibrare i modelli di embedding dei knowledge graph senza il bisogno di esempi negativi.


As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability.More recently, absorbing the idea of mining-based strategies is adopted to emphasize the misclassified samples and achieve promising results.However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issues.In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage.Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages.In each stage, different samples are assigned with different importance according to their corresponding difficultness.Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors.Code will be available upon publication.

Una nuova loss per Adaptive Curriculum Learning per il riconoscimento dei volti


Adversarial examples are perturbed inputs designed to fool machine learning models.Adversarial training injects such examples into training data to increase robustness.To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss.We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.The model thus learns to generate weak perturbations, rather than defend against strong ones.As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step.We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models.On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks.In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks.

L'adversarial training con metodi a step singolo si adatta troppo e rimane vulnerabile a semplici attacchi black-box e white-box. Mostriamo che includere adversarial example da più fonti aiuta a difendersi dagli attacchi black-box.


An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier.It theoretically sounds in term of it maximize conditional entropy between attribute and representation.However, as shown in this paper, the AFL often causes unstable behavior that slows down the convergence.We propose an {\em attribute perception matching} as an alternative approach, based on the reformulation of conditional entropy maximization as {\em pair-wise distribution matching}. Although the naive approach for realizing the pair-wise distribution matching requires the significantly large number of parameters, the proposed method requires the same number of parameters with AFL but has a better convergence property.Experiments on both toy and real-world dataset prove that our proposed method converges to better invariant representation significantly faster than AFL.

Questo articolo propone un nuovo approccio per incorporare l'invarianza desiderata in representation learning, basato sulle osservazioni che l'attuale stato dell'arte AFL ha problemi pratici.


We study the properties of common loss surfaces through their Hessian matrix.In particular, in the context of deep learning, we empirically show that the spectrum of the Hessian is composed of two parts: (1) the bulk centered near zero, (2) and outliers away from the bulk.We present numerical evidence and mathematical justifications to the following conjectures laid out by Sagun et.al. (2016): Fixing data, increasing the number of parameters merely scales the bulk of the spectrum; fixing the dimension and changing the data (for instance adding more clusters or making the data less separable) only affects the outliers.We believe that our observations have striking implications for non-convex optimization in high dimensions.First, the *flatness* of such landscapes (which can be measured by the singularity of the Hessian) implies that classical notions of basins of attraction may be quite misleading.And that the discussion of wide/narrow basins may be in need of a new perspective around over-parametrization and redundancy that are able to create *large* connected components at the bottom of the landscape.Second, the dependence of a small number of large eigenvalues to the data distribution can be linked to the spectrum of the covariance matrix of gradients of model outputs.With this in mind, we may reevaluate the connections within the data-architecture-algorithm framework of a model, hoping that it would shed light on the geometry of high-dimensional and non-convex spaces in modern applications.In particular, we present a case that links the two observations: small and large batch gradient descent appear to converge to different basins of attraction but we show that they are in fact connected through their flat region and so belong to the same basin.

La superficie della loss è *molto* degenerata, e non ci sono barriere tra le soluzioni a grandi batch e quelle a piccoli batch.


The allocation of computation resources in the backbone is a crucial issue in object detection.However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal.In order to reallocate the engaged computation resources in a more efficient way, we present CR-NAS (Computation Reallocation Neural Architecture Search) that can learn computation reallocation strategies across different feature resolution and spatial position diectly on the target detection dataset.A two-level reallocation space is proposed for both stage and spatial reallocation.A novel hierarchical search procedure is adopted to cope with the complex search space.We apply CR-NAS to multiple backbones and achieve consistent improvements.Our CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO AP respectively without any additional computation budget.The models discovered by CR-NAS can be equiped to other powerful detection neck/head and be easily transferred to other dataset, e.g. PASCAL VOC, and other vision tasks, e.g. instance segmentation.Our CR-NAS can be used as a plugin to improve the performance of various networks, which is demanding.

Proponiamo CR-NAS per riallocare le risorse di calcolo impegnate in diverse risoluzioni e posizioni spaziali.


Uncertainty is a very important feature of the intelligence and helps the brain become a flexible, creative and powerful intelligent system.The crossbar-based neuromorphic computing chips, in which the computing is mainly performed by analog circuits, have the uncertainty and can be used to imitate the brain.However, most of the current deep neural networks have not taken the uncertainty of the neuromorphic computing chip into consideration.Therefore, their performances on the neuromorphic computing chips are not as good as on the original platforms (CPUs/GPUs).In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process.The experimental results show that the neural networks can achieve comparable inference performances on the uncertain neuromorphic computing chip compared to the results on the original platforms, and much better than the performances without this training scheme.

Un metodo di training che può far funzionare meglio gli algoritmi di deep learning su chip di calcolo neuromorfo con incertezza


An important property of image classification systems in the real world is that they both accurately classify objects from target classes (``knowns'') and safely reject unknown objects (``unknowns'') that belong to classes not present in the training data.Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.As a result, simply using low-confidence detections as a way to detect unknowns does not work well.In this work, we propose an Unknown-aware Deep Neural Network (UDN for short) to solve this challenging problem.The key idea of UDN is to enhance existing CNNs to support a product operation that models the product relationship among the features produced by convolutional layers.This way, missing a single key feature of a target class will greatly reduce the probability of assigning an object to this class.UDN uses a learned ensemble of these product operations, which allows it to balance the contradictory requirements of accurately classifying known objects and correctly rejecting unknowns.To further improve the performance of UDN at detecting unknowns, we propose an information-theoretic regularization strategy that incorporates the objective of rejecting unknowns into the learning process of UDN.We experiment on benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN, adding unknowns by injecting one dataset into another.Our results demonstrate that UDN significantly outperforms state-of-the-art methods at rejecting unknowns by 25 percentage points improvement in accuracy, while still preserving the classification accuracy.

Un'architettura CNN che può respingere efficacemente le incognite nei test object


Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain.However, naive applications of deep learning fall short in giving reliable confidence estimates and lack interpretability.We propose a new deep sequential latent variable model for dimensionality reduction and data imputation.Our modeling assumption is simple and interpretable: the high dimensional time series has a lower-dimensional representation which evolves smoothly in time according to a Gaussian process.The non-linear dimensionality reduction in the presence of missing data is achieved using a VAE approach with a novel structured variational approximation.We demonstrate that our approach outperforms several classical and deep learning-based data imputation methods on high-dimensional data from the domains of computer vision and healthcare, while additionally improving the smoothness of the imputations and providing interpretable uncertainty estimates.

Eseguiamo un'inferenza variazionale ammortizzata su un modello di processo gaussiano latente per ottenere prestazioni di imputazione superiori su serie temporali multivariate con dati mancanti.


In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models.In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations.Our experiments survey thousands of models with different architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets.We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the input-output Jacobian of the network, and that this correlates well with generalization.We further establish that factors associated with poor generalization -- such as full-batch training or using random labels -- correspond to higher sensitivity, while factors associated with good generalization  -- such as data augmentation and ReLU non-linearities -- give rise to more robust functions.Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.

Eseguiamo studi sperimentali massicci che caratterizzano le relazioni tra le norme Jacobiane, le regioni lineari e la generalizzazione.


Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space.An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors.Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples.Combining parameter noise with traditional RL methods allows to combine the best of both worlds.We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks.

Il rumore dello spazio dei parametri permette agli algoritmi di reinforcement learning di esplorare perturbando i parametri invece delle azioni, spesso portando a prestazioni di esplorazione significativamente migliorate.


Pre-trained deep neural network language models such as ELMo, GPT, BERT and XLNet have recently achieved state-of-the-art performance on a variety of language understanding tasks.However, their size makes them impractical for a number of scenarios, especially on mobile and edge devices.In particular, the input word embedding matrix accounts for a significant proportion of the model's memory footprint, due to the large input vocabulary and embedding dimensions.Knowledge distillation techniques have had success at compressing large neural network models, but they are ineffective at yielding student models with vocabularies different from the original teacher models.We introduce a novel knowledge distillation technique for training a student model with a significantly smaller vocabulary as well as lower embedding and hidden state dimensions.Specifically, we employ a dual-training mechanism that trains the teacher and student models simultaneously to obtain optimal word embeddings for the student vocabulary.We combine this approach with learning shared projection matrices that transfer layer-wise knowledge from the teacher model to the student model.Our method is able to compress the BERT-BASE model by more than 60x, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7MB.Experimental results also demonstrate higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques.

Presentiamo nuove tecniche di distillation che permettono di addestrare student model con diversi vocabolari e di comprimere BERT di 60 volte con un lieve calo di prestazioni.


Human reasoning involves recognising common underlying principles across many examples by utilising variables.The by-products of such reasoning are invariants that capture patterns across examples such as "if someone went somewhere then they are there" without mentioning specific people or places.Humans learn what variables are and how to use them at a young age, and the question this paper addresses is whether machines can also learn and use variables solely from examples without requiring human pre-engineering.We propose Unification Networks that incorporate soft unification into neural networks to learn variables and by doing so lift examples into invariants that can then be used to solve a given task.We evaluate our approach on four datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.

Apprendimento end-to-end di rappresentazioni invarianti con variabili attraverso gli esempi, ad esempio se qualcuno è andato da qualche parte allora è lì.


We propose a new learning-based approach to solve ill-posed inverse problems in imaging.We address the case where ground truth training samples are rare and the problem is severely ill-posed---both because of the underlying physics and because we can only get few measurements.This setting is common in geophysical imaging and remote sensing.We show that in this case the common approach to directly learn the mapping from the measured data to the reconstruction becomes unstable.Instead, we propose to first learn an ensemble of simpler mappings from the data to projections of the unknown image into random piecewise-constant subspaces.We then combine the projections to form a final reconstruction by solving a deconvolution-like problem.We show experimentally that the proposed method is more robust to measurement noise and corruptions not seen during training than a directly learned inverse.

Risolviamo problemi inversi mal posti con scarsa ground truth stimando un insieme di proiezioni casuali del modello invece del modello stesso.


Designing architectures for deep neural networks requires expert knowledge and substantial computation time.We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture.By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run.To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases.We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks.

Una tecnica per accelerare la selezione dell'architettura neurale approssimando i pesi di ogni architettura candidata invece di addestrarli individualmente.


Textual entailment (or NLI) data has proven useful as pretraining data for tasks requiring language understanding, even when building on an already-pretrained model like RoBERTa.The standard protocol for collecting NLI was not designed for the creation of pretraining data, and it is likely far from ideal for this purpose.With this application in mind we propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples.Using these alternatives and a simple MNLIbased baseline, we collect and compare five new 9k-example training sets.Our primary results are largely negative, with none of these new methods showing major improvements in transfer learning.However, we make several observations that should inform future work on NLI data, such as that the use of automatically provided seed sentences for inspiration improves the quality of the resulting data on most measures, and all of the interventions we investigated dramatically reduce previously observed issues with annotation artifacts.

Proponiamo quattro nuovi modi di raccogliere dati NLI. Alcuni aiutano leggermente come dati di pretraining, tutti aiutano a ridurre gli artefatti di annotazione.


Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging.Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world.Many existing methods tackle this problem by making simplifying assumptions about the environment.One common assumption is that the outcome is deterministic and there is only one plausible future.This can lead to low-quality predictions in real-world settings with stochastic dynamics.In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables.To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video.We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned.We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.Our SV2P implementation will be open sourced upon publication.

Previsione video variazionale e stocastica in setting del mondo reale.


In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents.However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures. In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies, which can recover agents' policies that can regenerate similar interactions.Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution.Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators and outperforms state-of-the-art multi-agent imitation learning methods.Our code is available at \url{https://github.com/apexrl/CoDAIL}.

Modellazione di complesse interazioni multi-agente in un framework di imitation learning multi-agente con modellazione esplicita di policy correlate approssimando le policy degli avversari.


Plagiarism and text reuse become more available with the Internet development.Therefore it is important to check scientific papers for the fact of cheating, especially in Academia.Existing systems of plagiarism detection show the good performance and have a huge source databases.Thus now it is not enough just to copy the text as is from the source document to get the original work.Therefore, another type of plagiarism become popular -- cross-lingual plagiarism.We present a CrossLang system for such kind of plagiarism detection for English-Russian language pair.

Un sistema cross-linguale per il rilevamento del plagio (inglese-russo)


Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes.  Since the synchronization of the local models or gradients can be a bottleneck for large-scale distributed training, compressing communication traffic has gained widespread attention recently.  Among several recent proposed compression algorithms, Residual Gradient Compression (RGC) is one of the most successful approaches---it can significantly compress the transmitting message size (0.1% of the gradient size) of each node and still preserve accuracy.However, the literature on compressing deep networks focuses almost exclusively on achieving good compression rate, while the efficiency of RGC in real implementation has been less investigated.In this paper, we develop an RGC method that achieves significant training time improvement in real-world multi-GPU systems.Our proposed RGC system design called RedSync, introduces a set of optimizations to reduce communication bandwidth while introducing limited overhead.We examine the performance of RedSync on two different multiple GPU platforms, including a supercomputer and a multi-card server.Our test cases include image classification on Cifar10 and ImageNet, and language modeling tasks on Penn Treebank and Wiki2 datasets.For DNNs featured with high communication to computation ratio, which has long been considered with poor scalability, RedSync shows significant performance improvement.

Abbiamo proposto un'implementazione per accelerare il training data parallel di DNN riducendo il requisito di larghezza di banda della comunicazione.


Backpropagation is driving today's artificial neural networks.However, despite extensive research, it remains unclear if the brain implements this algorithm.Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative.However, the convergence rate of such learning scales poorly with the number of involved neurons.Here we propose a hybrid learning approach, in which each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide.We show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning on fully connected and convolutional networks.Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.

Le perturbazioni possono essere utilizzate per imparare i pesi di feedback su grandi reti fully connected e convoluzionali.


Most deep neural networks (DNNs) require complex models to achieve high performance.Parameter quantization is widely used for reducing the implementation complexities.Previous studies on quantization were mostly based on extensive simulation using training data.We choose a different approach and attempt to measure the per-parameter capacity of DNN models and interpret the results to obtain insights on optimum quantization of parameters.This research uses artificially generated data and generic forms of fully connected DNNs, convolutional neural networks, and recurrent neural networks.We conduct memorization and classification tests to study the effects of the number and precision of the parameters on the performance.The model and the per-parameter capacities are assessed by measuring the mutual information between the input and the classified output.We also extend the memorization capacity measurement results to image classification and language modeling tasks.To get insight for parameter quantization when performing real tasks, the training and test performances are compared.

Suggeriamo il numero sufficiente di bit per rappresentare i pesi delle DNN e i bit ottimali sono conservativi nella risoluzione dei problemi reali.


Inspired by the combination of feedforward and iterative computations in the visual cortex, and taking advantage of the ability of denoising autoencoders to estimate the score of a joint distribution, we propose a novel approach to iterative inference for capturing and exploiting the complex joint distribution of output variables conditioned on some input variables.This approach is applied to image pixel-wise segmentation, with the estimated conditional score used to perform gradient ascent towards a mode of the estimated conditional distribution.This extends previous work on score estimation by denoising autoencoders to the case of a conditional distribution, with a novel use of a corrupted feedforward predictor replacing Gaussian corruption.An advantage of this approach over more classical ways to perform iterative inference for structured outputs, like conditional random fields (CRFs), is that it is not any more necessary to define an explicit energy function linking the output variables.To keep computations tractable, such energy function parametrizations are typically fairly constrained, involving only a few neighbors of each of the output variables in each clique.We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.

Raffinare le proposte di segmentazione eseguendo l'inferenza iterativa con denoising conditional autoencoder.


Inspired by the success of self attention mechanism and Transformer architecturein sequence transduction and image generation applications, we propose novel selfattention-based architectures to improve the performance of adversarial latent code-based schemes in text generation.Adversarial latent code-based text generationhas recently gained a lot of attention due to their promising results.In this paper,we take a step to fortify the architectures used in these setups, specifically AAEand ARAE.We benchmark two latent code-based methods (AAE and ARAE)designed based on adversarial setups.In our experiments, the Google sentencecompression dataset is utilized to compare our method with these methods usingvarious objective and subjective measures.The experiments demonstrate theproposed (self) attention-based models outperform the state-of-the-art in adversarialcode-based text generation.

Proponiamo un'architettura GAN basata sulla self-attention per la generazione di testo incondizionato e miglioriamo i precedenti risultati basati su codici avversari.


Watermarks have been used for various purposes.Recently, researchers started to look into using them for deep neural networks.Some works try to hide attack triggers on their adversarial samples when attacking neural networks and others want to watermark neural networks to prove their ownership against plagiarism.Implanting a backdoor watermark module into a neural network is getting more attention from the community.In this paper, we present a general purpose encoder-decoder joint training method, inspired by generative adversarial networks (GANs).Unlike GANs, however, our encoder and decoder neural networks cooperate to find the best watermarking scheme given data samples.In other words, we do not design any new watermarking strategy but our proposed two neural networks will find the best suited method on their own.After being trained, the decoder can be implanted into other neural networks to attack or protect them (see Appendix for their use cases and real implementations).To this end, the decoder should be very tiny in order not to incur any overhead when attached to other neural networks but at the same time provide very high decoding success rates, which is very challenging.Our joint training method successfully solves the problem and in our experiments maintain almost 100\% encoding-decoding success rates for multiple datasets with very little modifications on data samples to hide watermarks.We also present several real-world use cases in Appendix.

Proponiamo nuove neural network encoder-decoder per la filigrana. Eseguono un gioco cooperativo per definire il proprio schema di watermarking. Le persone non hanno più bisogno di progettare metodi di watermarking.


We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.We show that our estimator can be derived as the Rao-Blackwellization of three different estimators.Combining our estimator with REINFORCE, we obtain a policy gradient estimator and we reduce its variance using a built-in control variate which is obtained without additional model evaluations.The resulting estimator is closely related to other gradient estimators.Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings.

Deriviamo uno stimatore di gradiente a bassa varianza e unbiased per le aspettazioni su variabili casuali discrete basate sul sampling senza rimpiazzamento


We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates.  Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks.  Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy.Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops.Training these networks thus implicitly involves discovery of suitable recurrent architectures.Though considering only the aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures.Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias.  Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.

Proponiamo un metodo che permette il folding della CNN per creare connessioni ricorrenti


Gradient clipping is a widely-used technique in the training of deep networks, and is generally motivated from an optimisation lens: informally, it controls the dynamics of iterates, thus enhancing the rate of convergence to a local minimum.This intuition has been made precise in a line of recent works, which show that suitable clipping  can yield significantly faster convergence than vanilla gradient descent.In this paper, we propose a new lens for studying gradient clipping, namely, robustness: informally, one expects clipping to provide robustness to noise, since one does not overly trust any single sample.Surprisingly, we prove that  for the common problem of label noise in classification, standard gradient clipping does not in general provide robustness.On the other hand, we show that  a simple variant of gradient clipping is provably robust, and corresponds to suitably modifying the underlying loss function.This yields a simple, noise-robust alternative to the standard cross-entropy loss which performs well empirically.

Il gradient clipping non conferisce robustezza al rumore delle label, ma una semplice variante basata sulla loss ne è capace.


Among deep generative models, flow-based models, simply referred as \emph{flow}s in this paper, differ from other models in that they provide tractable likelihood.Besides being an evaluation metric of synthesized data, flows are supposed to be robust against out-of-distribution~(OoD) inputs since they do not discard any information of the inputs.However, it has been observed that flows trained on FashionMNIST assign higher likelihoods to OoD samples from MNIST.This counter-intuitive observation raises the concern about the robustness of flows' likelihood.In this paper, we explore the correlation between flows' likelihood and image semantics.We choose two typical flows as the target models: Glow, based on coupling transformations, and pixelCNN, based on autoregressive transformations.Our experiments reveal surprisingly weak correlation between flows' likelihoods and image semantics: the predictive likelihoods of flows can be heavily affected by trivial transformations that keep the image semantics unchanged, which we call semantic-invariant transformations~(SITs).We explore three SITs~(all small pixel-level modifications): image pixel translation, random noise perturbation, latent factors zeroing~(limited to flows using multi-scale architecture, e.g. Glow).These findings, though counter-intuitive, resonate with the fact that the predictive likelihood of a flow is the joint probability of all the image pixels.So flows' likelihoods, modeling on pixel-level intensities, is not able to indicate the existence likelihood of the high-level image semantics.We call for attention that it may be \emph{abuse} if we use the predictive likelihoods of flows for OoD samples detection.

Mostrano prove sperimentali sulla debole correlazione tra le likelihood dei flussi e la semantica delle immagini.


This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier.Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images.The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses.Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods.

Applichiamo una strategia di difesa indipendente dal modello contro gli adversarial example e raggiungiamo il 60% di precisione white-box e il 90% di precisione black-box contro i principali algoritmi di attacco.


In this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD).We prove A2BCD converges linearly to a solution of the convex minimization problem at the same rate as NU_ACDM, so long as the maximum delay is not too large.This is the first asynchronous Nesterov-accelerated algorithm that attains any provable speedup.Moreover, we then prove that these algorithms both have optimal complexity.Asynchronous algorithms complete much faster iterations, and A2BCD has optimal complexity.Hence we observe in experiments that A2BCD is the top-performing coordinate descent algorithm, converging up to 4-5x faster than NU_ACDM on some data sets in terms of wall-clock time.To motivate our theory and proof techniques, we also derive and analyze a continuous-time analog of our algorithm and prove it converges at the same rate.

Mostriamo la prima prova di convergenza in assoluto di un algoritmo accelerato asincrono che raggiunge un aumento di velocità.


A framework for efficient Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation.Its strength lies in both ease of implementation and automatically tuning sampler parameters to speed up mixing time.Several strategies to approximate the evidence lower bound (ELBO) computation are introduced, including a rewriting of the ELBO objective.Experimental evidence is shown by performing experiments on an unconditional VAE on density estimation tasks; solving an influence diagram in a high-dimensional space with a conditional variational autoencoder (cVAE) as a deep Bayes classifier; and state-space models for time-series data.

Incorporiamo i sampler SG-MCMC in un'approssimazione variazionale


The point estimates of ReLU classification networks, arguably the most widely used neural network architecture, have recently been shown to have arbitrarily high confidence far away from the training data.This architecture is thus not robust, e.g., against out-of-distribution data.Approximate Bayesian posteriors on the weight space have been empirically demonstrated to improve predictive uncertainty in deep learning.The theoretical analysis of such Bayesian approximations is limited, including for ReLU classification networks.We present an analysis of approximate Gaussian posterior distributions on the weights of ReLU networks.We show that even a simplistic (thus cheap), non-Bayesian Gaussian distribution fixes the asymptotic overconfidence issue.Furthermore, when a Bayesian method, even if a simple one, is employed to obtain the Gaussian, the confidence becomes better calibrated.This theoretical result motivates a range of Laplace approximations along a fidelity-cost trade-off.We validate these findings empirically via experiments using common deep ReLU networks.

Noi sosteniamo teoricamente che assumendo semplicemente che i pesi di una rete ReLU siano distribuiti in modo gaussiano (senza nemmeno un formalismo bayesiano) si potrebbe risolvere questo problema; per un'incertezza più calibrata, un semplice metodo bayesiano potrebbe già essere sufficiente.


Word alignments are useful for tasks like statistical and neural machine translation (NMT) and annotation projection.Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT.However, most approaches require parallel training data and quality decreases as less training data is available.We propose word alignment methods that require little or no parallel data.The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment.Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries.We find that traditional statistical aligners are outperformed by contextualized embeddings – even in scenarios with abundant parallel data.For example, for a set of 100k parallel sentences, contextualized embeddings achieve a word alignment F1 that is more than 5% higher (absolute) than eflomal.

Usiamo rappresentazioni addestrate senza dati paralleli per creare allineamenti di parole.


Recent studies have shown the vulnerability of reinforcement learning (RL) models in noisy settings.The sources of noises differ across scenarios.For instance, in practice, the observed reward channel is often subject to noise (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible as a result.Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors.In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix.We call such observed rewards as perturbed rewards.We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards.Our framework draws upon approaches for supervised learning with noisy data.The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards.We prove the convergence and sample complexity of our approach.Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines.For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.

Un nuovo approccio per l'apprendimento con reward rumorose nel reinforcement learning


Training recurrent neural networks (RNNs) on long sequences using backpropagation through time (BPTT) remains a fundamental challenge. It has been shown that adding a local unsupervised loss term into the optimization objective makes the training of RNNs on long sequences more effective. While the importance of an unsupervised task can in principle be controlled by a coefficient in the objective function, the gradients with respect to the unsupervised loss term still influence all the hidden state dimensions, which might cause important information about the supervised task to be degraded or erased. Compared to existing semi-supervised sequence learning methods, this paper focuses upon a traditionally overlooked mechanism -- an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task.We achieve this by dividing RNN hidden space into a private space for the supervised task and a shared space for both the supervised and unsupervised tasks.We present extensive experiments with the proposed framework on several long sequence modeling benchmark datasets.Results indicate that the proposed framework can yield performance gains in RNN models where long term dependencies are notoriously challenging to deal with.

Questo articolo si concentra su un meccanismo tradizionalmente trascurato: un'architettura con hidden unit private e condivise esplicitamente progettate per mitigare l'influenza negativa della loss ausiliaria non supervisionata sul task principale supervised.


We investigate multi-task learning approaches which use a shared feature representation for all tasks.To better understand the transfer of task information, we study an architecture with a shared module for all tasks and a separate output module for each task.We study the theory of this setting on linear and ReLU-activated models.Our key observation is that whether or not tasks' data are well-aligned can significantly affect the performance of multi-task learning.We show that misalignment between task data can cause negative transfer (or hurt performance) and provide sufficient conditions for positive transfer.Inspired by the theoretical insights, we show that aligning tasks' embedding layers leads to performance gains for multi-task training and transfer learning on the GLUE benchmark and sentiment analysis tasks; for example, we obtained a 2.35% GLUE score average improvement on 5 GLUE tasks over BERT LARGE using our alignment method.We also design an SVD-based task re-weighting scheme and show that it improves the robustness of multi-task training on a multi-label image dataset.

Uno studio teorico del multi-task learning con implicazioni pratiche per migliorare il multi-task training e il transfer learning


We review three limitations of BLEU and ROUGE – the most popular metricsused to assess reference summaries against hypothesis summaries, come up withcriteria for what a good metric should behave like and propose concrete ways toassess the performance of a metric in detail and show the potential of Transformers-based Language Models to assess reference summaries against hypothesis summaries.

Nuovo metodo per valutare la qualità dei valutatori di similarità e mostrare il potenziale dei language model basati su Transformer nel sostituire BLEU e ROUGE.


In this paper, we explore meta-learning for few-shot text classification.Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks.However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another.Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns.Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words.We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).

I metodi di meta-learning utilizzati per computer vision, applicati direttamente a NLP, hanno prestazioni peggiori dei nearest neighbors su nuove classi; possiamo fare meglio con le firme distributive.


The description of neural computations in the field of neuroscience relies on two competing views:(i) a classical single-cell view that relates the activity of individual neurons to sensory or behavioural variables, and focuses on how different cell classes map onto computations;(ii) a more recent population view that instead characterises computations in terms of collective neural trajectories, and focuses on the dimensionality of these trajectories as animals perform tasks.How the two key concepts of cell classes and low-dimensional trajectories interact to shape neural computations is however currently not understood.Here we address this question by combining machine-learning tools for training RNNs with reverse-engineering and theoretical analyses of network dynamics.We introduce a novel class of theoretically tractable recurrent networks: low-rank, mixture of Gaussian RNNs.In these networks, the rank of the connectivity controls the dimensionality of the dynamics, while the number of components in the Gaussian mixture corresponds to the number of cell classes.Using back-propagation, we determine the minimum rank and number of cell classes needed to implement neuroscience tasks of increasing complexity.We then exploit mean-field theory to reverse-engineer the obtained solutions and identify the respective roles of dimensionality and cell classes.We show that the rank determines the phase-space available for dynamics that implement input-output mappings, while having multiple cell classes allows networks to flexibly switch between different types of dynamics in the available phase-space.Our results have implications for the analysis of neuroscience experiments and the development of explainable AI.

Un'analisi teorica di una nuova classe di RNN, addestrata su task di neuroscienze, ci permette di identificare il ruolo della dimensionalità dinamica e delle classi di cellule nelle computazioni neurali.


We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation.Much like commonly used convolutional neural network - conditional Markov random field (CNN-CRF) models, the proposed method is able to enforce higher-order consistency in the model, but without being limited to a very specific class of potentials.The method is conceptually simple and flexible, and our experimental results demonstrate improvement on several diverse structured prediction tasks.

Proponiamo il fusion discriminator, una nuova architettura per incorporare informazioni condizionali nel discriminatore delle GAN per task di structured prediction.


Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks.Recent work in MBRL has mostly focused on using more advanced function approximators and planning schemes, leaving the general framework virtually unchanged since its conception.In this paper, we identify a fundamental issue of the standard MBRL framework -- what we call the objective mismatch issue.Objective mismatch arises when one objective is optimized in the hope that a second, often uncorrelated, metric will also be optimized.In the context of MBRL, we characterize the objective mismatch between training the forward dynamics model w.r.t. the likelihood of the one-step ahead prediction, and the overall goal of improving performance on a downstream control task.For example, this issue can emerge with the realization that dynamics models effective for a specific task do not necessarily need to be globally accurate, and vice versa globally accurate models might not be sufficiently accurate locally to obtain good control performance on a specific task.In our experiments, we study this objective mismatch issue and demonstrate that the likelihood of the one-step ahead prediction is not always correlated with downstream control performance.This observation highlights a critical flaw in the current MBRL framework which will require further research to be fully understood and addressed.We propose an initial method to mitigate the mismatch issue by re-weighting dynamics model training.Building on it, we conclude with a discussion about other potential directions of future research for addressing this issue.

Definiamo, esploriamo e cominciamo ad affrontare il problema della mancata corrispondenza degli obiettivi nel reinforcement learning basato su modelli.


There has recently been a heated debate (e.g. Schwartz-Ziv & Tishby (2017), Saxe et al. (2018), Noshad et al. (2018), Goldfeld et al. (2018)) about measuring the information flow in Deep Neural Networks using techniques from information theory.It is claimed that Deep Neural Networks in general have good generalization capabilities since they not only learn how to map from an input to an output but also how to compress information about the training data input (Schwartz-Ziv & Tishby, 2017).That is, they abstract the input information and strip down any unnecessary or over-specific information.If so, the message compression method, Information Bottleneck (IB), could be used as a natural comparator for network performance, since this method gives an optimal information compression boundary.This claim was then later denounced as well as reaffirmed (e.g.  Saxe et al. (2018), Achille et al. (2017), Noshad et al. (2018)), as the employed method of mutual information measuring is not actually measuring information but clustering of the internal layer representations (Goldfeld et al. (2018)).In this paper, we will present a detailed explanation of the development in the Information Plain (IP), which is a plot-type that compares mutual information to judge compression (Schwartz-Ziv & Tishby (2017)), when noise is retroactively added (using binning estimation).  We also explain why different activation functions show different trajectories on the IP.Further, we have looked into the effect of clustering on the network loss through early and perfect stopping using the Information Plane and how clustering can be used to help network pruning.

Diamo una spiegazione dettagliata delle traiettorie nel piano dell'informazione e studiamo il suo utilizzo per la progettazione di reti neurali (pruning)


Formal understanding of the inductive bias behind deep convolutional networks, i.e. the relation between the network's architectural features and the functions it is able to model, is limited.In this work, we establish a fundamental connection between the fields of quantum physics and deep learning, and use it for obtaining novel theoretical observations regarding the inductive bias of convolutional networks.Specifically, we show a structural equivalence between the function realized by a convolutional arithmetic circuit (ConvAC) and a quantum many-body wave function, which facilitates the use of quantum entanglement measures as quantifiers of a deep network's expressive ability to model correlations.Furthermore, the construction of a deep ConvAC in terms of a quantum Tensor Network is enabled.This allows us to perform a graph-theoretic analysis of a convolutional network, tying its expressiveness to a min-cut in its underlying graph.We demonstrate a practical outcome in the form of a direct control over the inductive bias via the number of channels (width) of each layer.We empirically validate our findings on standard convolutional networks which involve ReLU activations and max pooling.The description of a deep convolutional network in well-defined graph-theoretic tools and the structural connection to quantum entanglement, are two interdisciplinary bridges that are brought forth by this work.

Impiegare misure di entanglement quantistico per quantificare le correlazioni nel deep learning, e utilizzare la connessione per adattare l'architettura della deep network alle correlazioni nei dati.


Deep learning algorithms are increasingly used in modeling chemical processes.However, black box predictions without rationales have limited used in practical applications, such as drug design.To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity).The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task.We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function.We evaluate the approach on two benchmark toxicity datasets.We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales.Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.

Usiamo un reinforcement learning su grafi molecolari per generare motivazioni per la predizione di proprietà molecolari interpretabili.


In recent years, substantial progress has been made on graph convolutional networks (GCN).In this paper, for the first time, we theoretically analyze the connections between GCN and matrix factorization (MF), and unify GCN as matrix factorization with co-training and unitization.Moreover, under the guidance of this theoretical analysis, we propose an alternative model to GCN named Co-training and Unitized Matrix Factorization (CUMF).The correctness of our analysis is verified by thorough experiments.The experimental results show that CUMF achieves similar or superior performances compared to GCN.In addition, CUMF inherits the benefits of MF-based methods to naturally support constructing mini-batches, and is more friendly to distributed computing comparing with GCN.The distributed CUMF on semi-supervised node classification significantly outperforms distributed GCN methods.Thus, CUMF greatly benefits large scale and complex real-world applications.

Unifichiamo le graph convolutional network come co-training e la unitized matrix factorization.


Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime.Inspired by the recent progress in deep generative models, in this paper we propose a flow-based autoregressive model for graph generation called GraphAF.GraphAF combines the advantages of both autoregressive and flow-based approaches and enjoys: (1) high model flexibility for data density estimation; (2) efficient parallel computation for training; (3) an iterative sampling process, which allows leveraging chemical domain knowledge for valency checking.Experimental results show that GraphAF is able to generate 68\% chemically valid molecules even without chemical knowledge rules and 100\% valid molecules with chemical rules.The training process of GraphAF is two times faster than the existing state-of-the-art approach GCPN.After fine-tuning the model for goal-directed property optimization with reinforcement learning, GraphAF achieves state-of-the-art performance on both chemical property optimization and constrained property optimization.

Un modello autoregressivo basato sul flusso per la generazione di grafi molecolari. Raggiunge risultati allo stato dell'arte nella generazione di molecole e nell'ottimizzazione delle proprietà.


We study two types of preconditioners and preconditioned stochastic gradient descent (SGD) methods in a unified framework.We call the first one the Newton type due to its close relationship to the Newton method, and the second one the Fisher type as its preconditioner is closely related to the inverse of Fisher information matrix.Both preconditioners can be derived from one framework, and efficiently estimated on any matrix Lie groups designated by the user using natural or relative gradient descent minimizing certain preconditioner estimation criteria.Many existing preconditioners and methods, e.g., RMSProp, Adam, KFAC, equilibrated SGD, batch normalization, etc., are special cases of or closely related to either the Newton type or the Fisher type ones.Experimental results on relatively large scale machine learning  problems are reported for performance study.

Proponiamo una nuova framework per l'apprendimento dei precondizionatori, deriviamo nuove forme di precondizionatori e metodi di apprendimento, e riveliamo la relazione con metodi come RMSProp, Adam, Adagrad, ESGD, KFAC, batch normalization, ecc.


We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion.On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks.EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data.We also performed extensive ablation studies and suggest parameters for practical use.

Semplici tecniche di text augmentation possono aumentare significativamente le prestazioni nei task di classificazione del testo, specialmente per piccoli dataset.


We propose a model that is able to perform physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available.Existing physical scene understanding methods require either object state supervision, or do not integrate with differentiable physics to learn interpretable system parameters and states.We address this problem through a \textit{physics-as-inverse-graphics} approach that brings together vision-as-inverse-graphics and differentiable physics engines, where objects and explicit state and velocity representations are discovered by the model.This framework allows us to perform long term extrapolative video prediction, as well as vision-based model-predictive control.Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias.We further show the value of this tight vision-physics integration by demonstrating data-efficient learning of vision-actuated model-based control for a pendulum system.We also show that the controller's interpretability provides unique capabilities in goal-driven control and physical reasoning for zero-data adaptation.

Proponiamo un modello che è in grado di eseguire la stima dei parametri fisici dei sistemi da video, dove le equazioni differenziali che governano la dinamica della scena sono note, ma gli stati o gli oggetti annotati non sono disponibili.


This paper proposes ASAL, a new pool based active learning method that generates high entropy samples.Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training.Hence, the quality of new samples is high and annotations are reliable.  ASAL is particularly suitable for large data sets because it achieves a better run-time complexity (sub-linear) for sample selection than traditional uncertainty sampling (linear).We present a comprehensive set of experiments on two data sets and show that ASAL outperforms similar methods and clearly exceeds the established baseline (random sampling).  In the discussion section we analyze in which situations ASAL performs best and why it is sometimes hard to outperform random sample selection.To the best of our knowledge this is the first adversarial active learning technique that is applied for multiple class problems using deep convolutional classifiers and demonstrates superior performance than random sample selection.

ASAL è un metodo di active learning basato su pool che genera sample ad alta entropia e recupera i sample corrispondenti dal pool in tempo sub-lineare.


We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws.Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling.Reporting the best single model performance does not appropriately address this stochasticity.We propose a normalized expected best-out-of-n performance (Boo_n) as a way to correct these problems.

Evidenziamo importanti problemi con la pratica comune di utilizzare le migliori prestazioni del singolo modello per confrontare le architetture di deep learning, e proponiamo un metodo che corregge questi difetti.


Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain.One popular approach to this problem is to learn domain-invariant embeddings for both domains.In this work, we study, theoretically and empirically, the effect of the embedding complexity on generalization to the target domain.In particular, this complexity affects an upper bound on the target risk; this is reflected in experiments, too.Next, we specify our theoretical framework to multilayer neural networks.As a result, we develop a strategy that mitigates sensitivity to the embedding complexity, and empirically achieves performance on par with or better than the best layer-dependent complexity tradeoff.

Studiamo l'effetto della complessità di embedding nell'apprendimento di rappresentazioni invarianti al dominio e sviluppiamo una strategia che mitiga la sensibilità verso di essa.


We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are proposed to explore effective feature fusion between high and low resource.To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD).Additionally, adversarial training is adopted to boost model generalization.We examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data.Without augmenting any additional hand-crafted features, we achieve new state-of-the-art performances on CoNLL and Twitter NER---88.16% F1 for Spanish, 53.43% F1 for WNUT-2016, and 42.83% F1 for WNUT-2017.

Proponiamo una nuova architettura denominata Dual Adversarial Transfer Network (DATNet) per affrontare Named Entity Recognition (NER) low-resource e raggiungere nuove prestazioni allo stato dell'arte su CoNLL e Twitter NER.


Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images.Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution.We introduce Coulomb GANs, which pose the GAN learning problem as a potential field, where generated samples are attracted to training set samples but repel each other.The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field.Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes.We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.We show the efficacy of Coulomb GANs on LSUN bedrooms, CelebA faces, CIFAR-10 and the Google Billion Word text generation.

Le GAN di Coulomb possono apprendere in modo ottimale una distribuzione ponendo il problema di apprendimento della distribuzione come ottimizzazione di un campo potenziale


Predicting properties of nodes in a graph is an important problem with applications in a variety of domains.Graph-based Semi Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds, and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph.Recently, Graph Convolutional Networks (GCNs) have achieved impressive performance on the graph-based SSL task.In addition to label scores, it is also desirable to have a confidence score associated with them.Unfortunately, confidence estimation in the context of GCN has not been previously explored.We fill this important gap in this paper and propose ConfGCN, which estimates labels scores along with their confidences jointly in GCN-based setting.ConfGCN uses these estimated confidences to determine the influence of one node on another during neighborhood aggregation, thereby acquiring anisotropic capabilities.Through extensive analysis and experiments on standard benchmarks, we find that ConfGCN is able to significantly outperform state-of-the-art baselines.We have made ConfGCN’s source code available to encourage reproducible research.

Proponiamo una Graph Convolutional Network basata sulla fiducia per l'apprendimento semi-supervised.


In this paper we establish rigorous benchmarks for image classifier robustness.Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications.Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations.We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers.Afterward we discover ways to enhance corruption and perturbation robustness.We even find that a bypassed adversarial defense provides substantial common perturbation robustness.Together our benchmarks may aid future work toward networks that robustly generalize.

Proponiamo ImageNet-C per misurare la robustezza alla corruzione del classificatore e ImageNet-P per misurare la robustezza alla perturbazione


The paper explores a novel methodology in source code obfuscation through the application of text-based recurrent neural network network (RNN) encoder-decoder models in ciphertext generation and key generation.Sequence-to-sequence modelsare incorporated into the model architecture to generate obfuscated code, generate the deobfuscation key, and live execution.Quantitative benchmark comparison to existing obfuscation methods indicate significant improvement in stealth and execution cost for the proposed solution, and experiments regarding the model’s properties yield positive results regarding its character variation, dissimilarity to the original codebase, and consistent length of obfuscated code.

Offuscare il codice usando le reti seq2seq, ed eseguire usando il codice offuscato e la coppia di chiavi


We propose a method for joint image and per-pixel annotation synthesis with GAN.We demonstrate that GAN has good high-level representation of target data that can be easily projected to semantic segmentation masks.This method can be used to create a training dataset for teaching separate semantic segmentation network.Our experiments show that such segmentation network successfully generalizes on real data.Additionally, the method outperforms supervised training when the number of training samples is small, and works on variety of different scenes and classes.The source code of the proposed method will be publicly available.

Metodo basato su GAN per la sintesi congiunta di immagini e annotazioni per-pixel


Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic unknown environments with natural language instructions.Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions.  The ultimate goal of VLN, however, is to serve people speaking arbitrary languages.Towards multilingual VLN with numerous languages, we collect a cross-lingual R2R dataset, which extends the original benchmark with corresponding Chinese instructions.But it is time-consuming and expensive to collect large-scale human instructions for every existing language.Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages.We first explore the possibility of building a cross-lingual agent when no training data of the target language is available.The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and a visually grounded cross-lingual alignment module to align textual representations of different languages.Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions.In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data.Our methods and dataset demonstrate the potentials of building a cross-lingual agent to serve speakers with different languages.

Introduciamo un nuovo task e un nuovo dataset sulla navigazione vision-language e cross-lingual, e proponiamo un framework generale VLN cross-lingual per il task.


Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored.Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes.We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end.We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model's latent representations.We test our model's discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature.

Classificazione non supervisionata tramite deep generative modeling con apprendimento di feature controllabili valutato in un difficile task del mondo reale


Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability.However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage.Two fundamental questions arise in learning over dynamic graphs:(i) How to elegantly model dynamical processes over graphs?(ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations?We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes).Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes.This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics.Our unified framework is trained using an efficient unsupervised procedure and has capability to generalize over unseen nodes.We demonstrate that DyRep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework.

Modelli di rappresentazione dell'apprendimento su grafi dinamici come processo latente nascosto che collega due processi osservati di Evoluzione topologica di e Interazioni su grafi dinamici.


With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games.We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions.SM-games codify a common design pattern in machine learning that includes some GANs, adversarial training, and other recent algorithms.We show that SM-games are amenable to analysis and optimization using first-order methods.

Introduciamo una classe di giochi a n giocatori adatti ai metodi basati sul gradiente.


While counter machines have received little attention in theoretical computer science since the 1960s, they have recently achieved a newfound relevance to the field of natural language processing (NLP).Recent work has suggested that some strong-performing recurrent neural networks utilize their memory as counters.Thus, one potential way to understand the sucess of these networks is to revisit the theory of counter computation.Therefore, we choose to study the abilities of real-time counter machines as formal grammars.We first show that several variants of the counter machine converge to express the same class of formal languages.We also prove that counter languages are closed under complement, union, intersection, and many other common set operations.Next, we show that counter machines cannot evaluate boolean expressions, even though they can weakly validate their syntax.This has implications for the interpretability and evaluation of neural network systems: successfully matching syntactic patterns does not guarantee that a counter-like model accurately represents underlying semantic structures.Finally, we consider the question of whether counter languages are semilinear.This work makes general contributions to the theory of formal languages that are of particular interest for the interpretability of recurrent neural networks.

Studiamo la classe dei linguaggi formali accettabili dai controautomi in tempo reale, un modello di calcolo legato ad alcuni tipi di reti neurali ricorrenti.


Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences.For longer documents and summaries however these models often include repetitive and incoherent phrases.We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training.However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable.We evaluate this model on the CNN/Daily Mail and New York Times datasets.Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models.Human evaluation also shows that our model produces higher quality summaries.

Un modello di summarization che combina un nuovo metodo di intra-attention learning e reinforcement learning per aumentare i punteggi ROUGE e la qualità del riassunto per lunghe sequenze.


Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the teacher) into another model (the student), where typically, the teacher has a greater capacity (e.g., more parameters or higher bit-widths).To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.Due to the difference in model capacities, the student may not benefit fully from the same data points on which the teacher is trained.On the other hand, a human teacher may demonstrate a piece of knowledge with individualized examples adapted to a particular student, for instance, in terms of her cultural background and interests.Inspired by this behavior, we design data augmentation agents with distinct roles to facilitate knowledge distillation.Our data augmentation agents generate distinct training data for the teacher and student, respectively.We focus specifically on KD when the teacher network has greater precision (bit-width) than the student network.We find empirically that specially tailored data points enable the teacher's knowledge to be demonstrated more effectively to the student.We compare our approach with existing KD methods on training popular neural architectures and demonstrate that role-wise data augmentation improves the effectiveness of KD over strong prior approaches.The code for reproducing our results will be made publicly available.

Studiamo se e come l'adaptive data augmentation e la knowledge distillation possono essere sfruttati simultaneamente in modo sinergico per un migliore training delle reti di student.


Neural network models have shown excellent fluency and performance when applied to abstractive summarization.Many approaches to neural abstractive summarization involve the introduction of significant inductive bias, such as pointer-generator architectures, coverage, and partially extractive procedures, designed to mimic human summarization.We show that it is possible to attain competitive performance by instead directly viewing summarization as language modeling.We introduce a simple procedure built upon pre-trained decoder-transformers to obtain competitive ROUGE scores using a language modeling loss alone, with no beam-search or other decoding-time optimization, and instead rely on efficient nucleus sampling and greedy decoding.

Introduciamo una semplice procedura per riproporre language model pre-trained basati su transformer per eseguire bene l'abstractive summarization.