Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks.Specifically, by altering a small set of training examples, an adversary is able to install a backdoor that can be used during inference to fully control the model’s behavior.While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be detected even by fairly rudimentary data filtering.In this paper, we introduce a new approach to executing backdoor attacks, utilizing adversarial examples and GAN-generated data.The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection.

Mostriamo come eseguire con successo attacchi backdoor senza cambiare le label di training.


Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance.However, the differences between the learned solutions of networks which generalize and those which do not remain unclear.Additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated.Here, we connect these lines of inquiry to demonstrate that a network’s reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyper- parameters, and over the course of training.While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units.Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.

Troviamo che le deep network che generalizzano male sono più dipendenti dalle singole direzioni rispetto a quelle che generalizzano bene, e valutiamo l'impatto del dropout e della batch normalization, così come la selettività di classe sulla dipendenza dalla singola direzione.


Typical amortized inference in variational autoencoders is specialized for a single probabilistic query.Here we propose an inference network architecture that generalizes to unseen probabilistic queries.Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries.We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function.The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem.Our network also marginalizes nuisance variables as required.  We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.

Invece di imparare i parametri di un graphical model dai dati, si impara una rete di inferenza che può rispondere alle stesse domande probabilistiche.


A plethora of computer vision tasks, such as optical flow and image alignment, can be formulated as non-linear optimization problems.Before the resurgence of deep learning, the dominant family for solving such optimization problems was numerical optimization, e.g, Gauss-Newton (GN).More recently, several attempts were made to formulate learnable GN steps as cascade regression architectures.In this paper, we investigate recent machine learning architectures, such as deep neural networks with residual connections, under the above perspective.To this end, we first demonstrate how residual blocks (when considered as discretization of ODEs) can be viewed as GN steps.Then, we go a step further and propose a new residual block, that is reminiscent of Newton's method in numerical optimization and exhibits faster convergence.We thoroughly evaluate the proposed Newton-ResNet by conducting experiments on image and speech classification and image generation, using 4 datasets.All the experiments demonstrate that Newton-ResNet requires less parameters to achieve the same performance with the original ResNet.

Dimostriamo come i residual block possono essere visti come step di Gauss-Newton; proponiamo un nuovo residual block che sfrutta le informazioni del secondo ordine.


In competitive situations, agents may take actions to achieve their goals that unwittingly facilitate an opponent’s goals.Weconsider a domain where three agents operate: (1) a user (human), (2) an attacker (human or a software) agent and (3) an observer (a software) agent.The user and the attacker compete to achieve different goals.When there is a disparity in the domain knowledge the user and the attacker possess, the attacker may use the user’s unfamiliarity with the domain toits advantage and further its own goal.In this situation, the observer, whose goal is to support the user may need to intervene, and this intervention needs to occur online, on-time and be accurate.We formalize the online plan intervention problem and propose a solution that uses a decision tree classifier to identify intervention points in situations where agents unwittingly facilitate an opponent’s goal.We trained a classifier using domain-independent features extracted from the observer’s decision space to evaluate the “criticality” of the current state.The trained model is then used in an online setting on IPC benchmarks to identify observations that warrant intervention.Our contributions lay a foundation for further work in the area of deciding when to intervene.

Introduciamo un modello di apprendimento automatico che utilizza feature indipendenti dal dominio per stimare la criticità dello stato corrente per causare uno stato indesiderato noto.


Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data.However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision.While previous work has focused on exploiting statistical independence to \textit{disentangle} latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations.Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models.This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.

Sviluppiamo un framework per trovare rappresentazioni interne modulari nei modelli generativi e manipolare poi per generare esempi controfattuali.


Catastrophic forgetting poses a grand challenge for continual learning systems, which prevents neural networks from protecting old knowledge while learning new tasks sequentially.We propose a Differentiable Hebbian Plasticity (DHP) Softmax layer which adds a fast learning plastic component to the slow weights of the softmax output layer.The DHP Softmax behaves as a compressed episodic memory that reactivates existing memory traces, while creating new ones.We demonstrate the flexibility of our model by combining it with existing well-known consolidation methods to prevent catastrophic forgetting.We evaluate our approach on the Permuted MNIST and Split MNIST benchmarks, and introduce Imbalanced Permuted MNIST — a dataset that combines the challenges of class imbalance and concept drift.Our model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.

I pesi plastici hebbiani possono comportarsi come un immagazzinamento di memoria episodica compressa nelle reti neurali; migliorando la loro capacità di alleviare la catastrophic forgetting nel continual learning.


While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation.Under the hypothesis that DNN are also organized in task-specific modules, in this paper we seek to dissect a hidden layer into disjoint groups of task-specific hidden neurons with the help of relatively well- studied neuron attribution methods.By saying task-specific, we mean the hidden neurons in the same group are functionally related for predicting a set of similar data samples, i.e. samples with similar feature patterns.We argue that such groups of neurons which we call Functional Modules can serve as the basic functional unit in DNN.We propose a preliminary method to identify Functional Modules via bi- clustering attribution scores of hidden neurons.We find that first, unsurprisingly, the functional neurons are highly sparse, i.e., only a small sub- set of neurons are important for predicting a small subset of data samples and, while we do not use any label supervision, samples corresponding to the same group (bicluster) show surprisingly coherent feature patterns.We also show that these Functional Modules perform a critical role in discriminating data samples through ablation experiment.

Sviluppiamo un approccio per parcellizzare un hidden layer in DNN in gruppi funzionalmente correlati, applicando il coclustering spettrale sui punteggi di attribuzione degli hidden neuron.


Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices.These chips are mainly used in computer vision applications.However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text.In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips.Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW.For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip.At the workshop, we have two system demonstrations for NLP tasks.The first demo classifies the input English Wikipedia sentence into one of the 14 classes.The second demo classifies the Chinese online-shopping review into positive or negative.

Distribuire applicazioni di text classification e sentiment analysis per l'inglese e il cinese su un chip acceleratore CNN da 300mW per scenari di applicazione on-device.


Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.To enable efficient comparison, we introduce an amortized variational inference framework that can perform fast and reliable posterior estimation across models of the same architecture.Our Any Parameter Encoder (APE) extends the encoder neural network common in amortized inference to take both a data feature vector and a model parameter vector as input.APE thus reduces posterior inference across unseen data and models to a single forward pass.In experiments comparing candidate topic models for synthetic data and product reviews, our Any Parameter Encoder yields comparable posteriors to more expensive methods in far less time, especially when the encoder architecture is designed in model-aware fashion.

Sviluppiamo VAE in cui l'encoder prende un vettore di parametri del modello come input, così possiamo fare un'inferenza rapida per molti modelli


The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents.Despite the apparent promises, transfer in RL is still an open and little exploited research area.In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample efficient.Our main contribution is Secret, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture.Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function.Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm.

Secret è un metodo di transfer per RL basato su credit assignment.


Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data.In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model.  While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions.The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method.Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound.  The generative frameworks are flexible enough to allow training under any prior distribution that permits a re-parametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters.Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations.Code publicly available on Github.

Lavorare verso modelli generativi di knowledge graph per stimare meglio l'incertezza predittiva nell'inferenza della conoscenza.


We present a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory.The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution.Training of the model amounts to solving an optimal control problem.The Monge-Amp\`ere flow has tractable likelihoods and supports efficient sampling and inference.One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions.We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point.This approach brings insights and techniques from Monge-Amp\`ere equation, optimal transport, and fluid dynamics into reversible flow-based generative models.

Un sistema dinamico basato sul flusso di gradiente per la modellazione generativa invertibile


Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs.We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling.We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory.The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks.We also show that the learned representations could correspond to chemical features in the molecule data.

Introduciamo un layer di memoria efficiente che può apprendere la rappresentazione e coartare i grafi di input simultaneamente senza fare affidamento sul message passing.


Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.To address this problem, we propose a novel encoding scheme by using {-1,+1} to decompose quantized neural networks (QNNs) into multi-branch binary networks, which can be efficiently implemented by bitwise operations (xnor and bitcount) to achieve model compression, computational acceleration and resource saving.Our method can achieve at most ~59 speedup and ~32 memory saving over its full-precision counterparts.Therefore, users can easily achieve different encoding precisions arbitrarily according to their requirements and hardware resources.Our mechanism is very suitable for the use of FPGA and ASIC in terms of data storage and computation, which provides a feasible idea for smart chips.We validate the effectiveness of our method on both large-scale image classification (e.g., ImageNet) and object detection tasks.

Un nuovo schema di codifica che utilizza {-1, +1} per decomporre le QNN in reti binarie multi-branch, in cui abbiamo usato operazioni bitwise (xnor e bitcount) per ottenere la compressione del modello, l'accelerazione computazionale e il risparmio di risorse.


We propose a novel method for incorporating conditional information into a generative adversarial network (GAN) for structured prediction tasks.This method is based on fusing features from the generated and conditional information in feature space and allows the discriminator to better capture higher-order statistics from the data.This method also increases the strength of the signals passed through the network where the real or generated data and the conditional data agree.The proposed method is conceptually simpler than the joint convolutional neural network - conditional Markov random field (CNN-CRF) models and enforces higher-order consistency without being limited to a very specific class of high-order potentials.Experimental results demonstrate that this method leads to improvement on a variety of different structured prediction tasks including image synthesis, semantic segmentation, and depth estimation.

Proponiamo un nuovo modo di incorporare le informazioni condizionali dell'immagine nel discriminatore di GANs usando la fusione delle feature che può essere usata per task di structured prediction.


Intelligent creatures can explore their environments and learn useful skills without supervision.In this paper, we propose ``Diversity is All You Need''(DIAYN), a method for learning useful skills without a reward function.Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy.On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward.We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks.Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.

Proponiamo un algoritmo per l'apprendimento di abilità utili senza una funzione di reward, e mostriamo come queste abilità possono essere utilizzate per risolvere downstream task.


Lexical ambiguity, i.e., the presence of two or more meanings for a single word, is an inherent and challenging problem for machine translation systems.Even though the use of recurrent neural networks and attention mechanisms are expected to solve this problem, machine translation systems are not always able to correctly translate lexically ambiguous sentences.In this work, I attempt to resolve the problem of lexical ambiguity in English--Japanese neural machine translation systems by combining a pretrained Bidirectional Encoder Representations from Transformer (BERT) language model that can produce contextualized word embeddings and a Transformer translation model, which is a state-of-the-art architecture for the machine translation task.These two proposed architectures have been shown to be more effective in translating ambiguous sentences than a vanilla Transformer model and the Google Translate system.Furthermore, one of the proposed models, the Transformer_BERT-WE, achieves a higher BLEU score compared to the vanilla Transformer model in terms of general translation, which is concrete proof that the use of contextualized word embeddings from BERT can not only solve the problem of lexical ambiguity, but also boost the translation quality in general.

L'articolo risolve un problema di ambiguità lessicale causato dall'omonimia nella traduzione neurale tramite BERT.


This paper focuses on the synthetic generation of human mobility data in urban areas.We present a novel and scalable application of Generative Adversarial Networks (GANs) for modeling and generating human mobility data.We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model.Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week.Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets.Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation.

Questo articolo si concentra sulla generazione sintetica di dati sulla mobilità umana nelle aree urbane utilizzando le GAN.


While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.Consequently, model size reduction has become an utmost goal in deep learning.Following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution.By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set.The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family.On benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance.

Questo articolo propone uno schema di codifica efficace per le reti neurali che codifica un insieme casuale di pesi da una distribuzione variazionale.


Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy image.The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image.Given such a generator network, or prior, a noisy image can be denoised by finding the closest image in the range of the prior.However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the networks parameters.In this paper we consider the problem of denoising an image from additive Gaussian noise, assuming the image is well described by a deep neural network with ReLu activations functions, mapping a k-dimensional latent space to an n-dimensional image.We state and analyze a simple gradient-descent-like iterative algorithm that minimizes a non-convex loss function, and provably removes a fraction of (1 - O(k/n)) of the noise energy.We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.

Analizzando un algoritmo che minimizza una loss non convessa, dimostriamo che tutto il rumore, tranne una piccola frazione, può essere rimosso da un'immagine utilizzando un prior generativo basato su una deep neural network.


Deep learning yields great results across many fields,from speech recognition, image classification, to translation.But for each problem, getting a deep model to work well involvesresearch into the architecture and a long period of tuning.We present a single model that yields good results on a numberof problems spanning multiple domains.In particular, this single modelis trained concurrently on ImageNet, multiple translation tasks,image captioning (COCO dataset), a speech recognition corpus,and an English parsing task. Our model architecture incorporates building blocks from multipledomains.It contains convolutional layers, an attention mechanism,and sparsely-gated layers.Each of these computational blocks is crucial for a subset ofthe tasks we train on.Interestingly, even if a block is notcrucial for a task, we observe that adding it never hurts performanceand in most cases improves it on all tasks.We also show that tasks with less data benefit largely from jointtraining with other tasks, while performance on large tasks degradesonly slightly if at all.

L'architettura multi-task su larga scala risolve ImageNet e la traduzione insieme e mostra il transfer learning.


Machine learning algorithms for controlling devices will need to learn quickly, with few trials.Such a goal can be attained with concepts borrowed from continental philosophy and formalized using tools from the mathematical theory of categories.Illustrations of this approach are presented on a cyberphysical system: the slot car game, and also on Atari 2600 games.

Approccio ispirato alla filosofia continentale per imparare da pochi dati.


Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation.In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio.WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation.Our experiments demonstrate that—without labels—WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano.We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.

Imparare a sintetizzare audio a forma d'onda grezza con le GAN


The difficulty of obtaining sufficient labeled data for supervised learning has motivated domain adaptation, in which a classifier is trained in one domain, source domain, but operates in another, target domain.Reducing domain discrepancy has improved the performance, but it is hampered by the embedded features that do not form clearly separable and aligned clusters.We address this issue by propagating labels using a manifold structure, and by enforcing cycle consistency to align the clusters of features in each domain more closely.Specifically, we prove that cycle consistency leads the embedded features distant from all but one clusters if the source domain is ideally clustered.We additionally utilize more information from approximated local manifold and pursue local manifold consistency for more improvement.Results for various domain adaptation scenarios show tighter clustering and an improvement in classification accuracy.

Un nuovo metodo di domain adaptation per allineare i manifold dai domini di origine e di destinazione utilizzando la propagazione delle label per una migliore accuratezza.


We propose a new method for training neural networks online in a bandit setting.Similar to prior work, we model the uncertainty only in the last layer of the network, treating the rest of the network as a feature extractor.This allows us to successfully balance between exploration and exploitation due to the efficient, closed-form uncertainty estimates available for linear models.To train the rest of the network, we take advantage of the posterior we have over the last layer, optimizing over all values in the last layer distribution weighted by probability.We derive a closed form, differential approximation to this objective and show empirically over various models and datasets that training the rest of the network in this fashion leads to both better online and offline performance when compared to other methods.

Questo articolo propone un nuovo metodo per l'apprendimento delle reti neurali nel setting dell'online bandit marginalizzando sull'ultimo layer


Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.	Our contributions in this paper are twofold.First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation.The aggregation is more robust and aligns better with the neural network than any single explanation method..	Second, we propose a new approach to evaluating explanation methods that circumvents the need for manual evaluation and is not reliant on the alignment of neural networks and humans decision processes.

Dimostriamo in teoria e in pratica che la combinazione di più metodi di spiegazione per DNN è utile per l'interpretabilità.


We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.  SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem.  To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme.  The target therefore gets to choose the source samples which are most informative for its own classification task.  Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting.  SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.

Imparare con dati di training limitati sfruttando istanze "utili" da una ricca fonte di dati.


We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components.To cleanly capture the set of neural architectures to which our method applies, we introduce the concept of a differential neural computational machine (∂NCM) and show that several existing architectures (e.g., NTMs, NRAMs) can be instantiated as a ∂NCM and can thus benefit from any amount of additional supervision over their interpretable components.Based on our method, we performed a detailed experimental evaluation with both, the NTM and NRAM architectures, and showed that the approach leads to significantly better convergence and generalization capabilities of the learning phase than when training using only input-output examples.

Aumentiamo la quantità di supervisione delle tracce possibile da utilizzare quando si addestrano architetture di macchine neurali completamente differenziabili.


Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.In this paper, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters.We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in quantized models but also reduces the variance in gradients estimation.We evaluate BQNs on MNIST, Fashion-MNIST and KMNIST classification datasets compared against bootstrap ensemble of QNNs (E-QNN).We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN (with less than 20% of the negative log-likelihood).

Proponiamo reti quantizzate bayesiane, per le quali impariamo una distribuzione posteriore sui loro parametri quantizzati.


Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks.To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy.Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training.We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained.We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation.During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial).Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks.We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.

Cascade adversarial training + low level similarity learning migliorano la robustezza sia contro gli attacchi white box che quelli black box.


Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities ``solve'' the exploding gradient problem, we show that this is not the case and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice.We explain why exploding gradients occur and highlight the {\it collapsing domain problem}, which can arise in architectures that avoid exploding gradients. ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks, which we show is a consequence of a surprising mathematical property.By noticing that {\it any neural network is a residual network}, we devise the {\it residual trick}, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.

Mostriamo che, a differenza di quello che si pensa, il problema dell'exploding gradient non è stato risolto e limita il livello a cui le MLP possono essere efficacemente addestrate. Mostriamo perché i gradienti esplodono e come ResNet li gestisce.


In this paper, we are interested in two seemingly different concepts: \textit{adversarial training} and \textit{generative adversarial networks (GANs)}.Particularly, how these techniques work to improve each other.To this end, we analyze the limitation of adversarial training as a defense method, starting from questioning how well the robustness of a model can generalize.Then, we successfully improve the generalizability via data augmentation by the ``fake'' images sampled from generative adversarial network.After that, we are surprised to see that the resulting robust classifier leads to a better generator, for free.We intuitively explain this interesting phenomenon and leave the theoretical analysis for future work.Motivated by these observations, we propose a system that combines generator, discriminator, and adversarial attacker together in a single network.After end-to-end training and fine tuning, our method can simultaneously improve the robustness of classifiers, measured by accuracy under strong adversarial attacks, and the quality of generators, evaluated both aesthetically and quantitatively.In terms of the classifier, we achieve better robustness than the state-of-the-art adversarial training algorithm proposed in (Madry \textit{et al.}, 2017), while our generator achieves competitive performance compared with SN-GAN (Miyato and Koyama, 2018).

Abbiamo trovato che l'adversarial training non solo accelera il training GAN ma aumenta anche la qualità dell'immagine


We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary.To obtain similar binary networks, existing methods rely on the sign activation function.This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible.To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient.We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function.Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation.This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization.Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets.

Un metodo per binarizzare sia i pesi che le attivazioni di una deep neural network che è efficiente nel calcolo e nell'uso della memoria e ha prestazioni migliori dello stato dell'arte.


In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints.In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global satisficing (i.e. "good-enough") model within a few communication rounds by carefully combining the space of local nodes' satisficing models.In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.

Presentiamo Good-Enough Model Spaces (GEMS), una framework per l'apprendimento di un modello aggregato su nodi distribuiti in un piccolo numero di round di comunicazione.


We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution.WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE).This regularizer encourages the encoded training distribution to match the prior.We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE).Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality.

Proponiamo un nuovo autoencoder basato sulla distanza di Wasserstein, che migliora le proprietà di campionamento del VAE.


In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.We leverage the sequential composition theory in DP, to establish a new connection between DP preservation and provable robustness.To address the trade-off among model utility, privacy loss, and robustness, we design an original, differentially private, adversarial objective function, based on the post-processing property in DP, to tighten the sensitivity of our model.An end-to-end theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of DP deep neural networks.

Preservare la privacy differenziale nell'adversarial learning con robustezza dimostrabile agli adversarial example


In high-dimensional reinforcement learning settings with sparse rewards, performingeffective exploration to even obtain any reward signal is an open challenge.While model-based approaches hold promise of better exploration via planning, itis extremely difficult to learn a reliable enough Markov Decision Process (MDP)in high dimensions (e.g., over 10^100 states).In this paper, we propose learningan abstract MDP over a much smaller number of states (e.g., 10^5), which we canplan over for effective exploration.We assume we have an abstraction functionthat maps concrete states (e.g., raw pixels) to abstract states (e.g., agent position,ignoring other objects).In our approach, a manager maintains an abstractMDP over a subset of the abstract states, which grows monotonically through targetedexploration (possible due to the abstract MDP).Concurrently, we learn aworker policy to travel between abstract states; the worker deals with the messinessof concrete states and presents a clean abstraction to the manager.On three ofthe hardest games from the Arcade Learning Environment (Montezuma's,Pitfall!, and Private Eye), our approach outperforms the previousstate-of-the-art by over a factor of 2 in each game.In Pitfall!, our approach isthe first to achieve superhuman performance without demonstrations.

Costruiamo ed esploriamo automaticamente un piccolo processo decisionale astratto di Markov, che ci permette di raggiungere risultati allo stato dell'arte su Montezuma's Revenge, Pitfall e Private Eye con un margine significativo.


Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction.Thus, we develop a novel class of off-policy batch RL algorithms which use KL-control to penalize divergence from a pre-trained prior model of probable actions.This KL-constraint reduces extrapolation error, enabling effective offline learning, without exploration, from a fixed batch of data.We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning.This Way Off-Policy (WOP) algorithm is tested on both traditional RL tasks from OpenAI Gym, and on the problem of open-domain dialog generation; a challenging reinforcement learning problem with a 20,000 dimensional action space.WOP allows for the extraction of multiple different reward functions post-hoc from collected human interaction data, and can learn effectively from all of these.We test real-world generalization by deploying dialog models live to converse with humans in an open-domain setting, and demonstrate that WOP achieves significant improvements over state-of-the-art prior methods in batch deep RL.

Mostriamo che il controllo KL da un prior pre-addestrato può permettere ai modelli RL di imparare da un batch statico di dati raccolti, senza la possibilità di esplorare online nell'ambiente.


Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL).An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy.This modular approach enables integration of state-of-the-art algorithms for variational inference or RL.Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot.In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.

Trasferimento della policy di un singolo episodio in una famiglia di ambienti con dinamiche correlate, attraverso un probing ottimizzato per una rapida inferenza delle variabili latenti e l'esecuzione immediata di una policy universale.


Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz.,(i) the knowledge-base associated with the domain,(ii) the history of the conversation, which is a sequence of utterances and(iii) the current utterance for which the response needs to be generated.While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context.Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling and document dating, we propose a memory augmented GCN for goal-oriented dialogues.Our model exploits(i) the entity relation graph in a knowledge-base  and(ii) the dependency graph associated with an utterance to compute richer representations for words and entities.Further, we take cognizance of the fact that in certain situations, such as, when the conversation is in a code-mixed language, dependency parsers may not be available.We show that in such situations we could use the global word co-occurrence graph and use it to enrich the representations of utterances.We experiment with the modified DSTC2 dataset and its recently released code-mixed versions in four languages and show that our method outperforms existing state-of-the-art methods, using a wide range of evaluation metrics.

Proponiamo un modello encoder-decoder basato su una Graph Convolutional Network con attention sequenziale per sistemi di dialogo goal-oriented.


Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications.We achieve this by(i) first learning vector embeddings of single graph nodes and(ii) then composing them to compactly represent node sequences.Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding - for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions.We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets.Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important.We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.

Meccanismo di embedding di sequenze di nodi che cattura sia il grafo che le proprietà del testo.


Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.This paper aims to leverage good properties of SIFT to renovate CNN architectures towards better accuracy and robustness.We borrow the scale-space extreme value idea from SIFT, and propose EVPNet (extreme value preserving network) which contains three novel components to model the extreme values: (1) parametric differences of Gaussian (DoG) to extract extrema, (2) truncated ReLU to suppress non-stable extrema and (3) projected normalization layer (PNL) to mimic PCA-SIFT like feature normalization.Experiments demonstrate that EVPNets can achieve similar or better accuracy than conventional CNNs, while achieving much better robustness on a set of adversarial attacks (FGSM,PGD,etc) even without adversarial training.

Questo articolo mira a sfruttare le buone proprietà delle feature visive robuste come SIFT per rinnovare le architetture CNN verso una migliore accuratezza e robustezza.


Compressed representations generalize better (Shamir et al., 2010), which may be crucial when learning from limited or noisy labeled data.The Information Bottleneck (IB) method (Tishby et al. (2000)) provides an insightful and principled approach for balancing compression and prediction in representation learning.The IB objective I(X; Z) − βI(Y ; Z) employs a Lagrange multiplier β to tune this trade-off.However, there is little theoretical guidance for how to select β.There is also a lack of theoretical understanding about the relationship between β, the dataset, model capacity, and learnability.In this work, we show that if β is improperly chosen, learning cannot happen: the trivial representation P(Z|X) = P(Z) becomes the global minimum of the IB objective.We show how this can be avoided, by identifying a sharp phase transition between the unlearnable and the learnable which arises as β varies.This phase transition defines the concept of IB-Learnability.We prove several sufficient conditions for IB-Learnability, providing theoretical guidance for selecting β.We further show that IB-learnability is determined by the largest confident, typical, and imbalanced subset of the training examples.We give a practical algorithm to estimate the minimum β for a given dataset.We test our theoretical results on synthetic datasets, MNIST, and CIFAR10 with noisy labels, and make the surprising observation that accuracy may be non-monotonic in β.

La teoria prevede la transizione di fase tra valori di beta non apprendibili e apprendibili per l'obiettivo di Information Bottleneck


We consider a new class of \emph{data poisoning} attacks on neural networks, in which the attacker takes control of a model by making small perturbations to a subset of its training data.  We formulate the task of finding poisons as a bi-level optimization problem, which can be solved using methods borrowed from the meta-learning community.  Unlike previous poisoning strategies, the meta-poisoning can poison networks that are trained from scratch using an initialization unknown to the attacker and transfer across hyperparameters.Further we show that our attacks are more versatile: they can cause misclassification of the target image into an arbitrarily chosen class.Our results show above 50% attack success rate when poisoning just 3-10% of the training dataset.

Generare immagini di training corrotte che sono impercettibili ma che cambiano il comportamento della CNN su un obiettivo durante ogni nuovo training.


We give a new algorithm for learning a two-layer neural network under a very general class of input distributions.Assuming there is a ground-truth two-layer network y = A \sigma(Wx) + \xi,where A, W are weight matrices, \xi represents noise, and the number of neurons in the hidden layer is no larger than the input or output,  our algorithm is guaranteed to recover the parameters A, W of the ground-truth network.The only requirement on the input x is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework and extends several results in tensor decompositions.We use spectral algorithms to avoid the complicated non-convex optimization in learning neural networks.Experiments show that our algorithm can robustly learn the ground-truth neural network with a small number of samples for many symmetric input distributions.

Diamo un algoritmo per l'apprendimento di una rete neurale a due layer con distribuzione simmetrica dell'input.


Teachers intentionally pick the most informative examples to show their students.However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable.We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies.We evaluate interpretability by (1) measuring the similarity of the teacher's emergent strategies to intuitive strategies in each domain and (2) conducting human experiments to evaluate how effective the teacher's strategies are at teaching humans.We show that the teacher network learns to select or generate interpretable, pedagogical examples to teach rule-based, probabilistic, boolean, and hierarchical concepts.

Mostriamo che il training di una rete di studenti e insegnanti in modo iterativo, piuttosto che congiunto, può produrre strategie di insegnamento emergenti e interpretabili.


Stochastic gradient descent (SGD), which  trades off noisy gradient updates for computational efficiency, is the de-facto optimization algorithm to solve large-scale machine learning problems.SGD can make rapid learning progress by performing updates using subsampled training data, but the noisy updates also lead to slow asymptotic convergence.   Several variance reduction algorithms, such as SVRG, introduce control variates to obtain a lower variance gradient estimate and faster convergence.  Despite their appealing asymptotic guarantees, SVRG-like algorithms have not been widely adopted in deep learning.The traditional asymptotic analysis in stochastic optimization provides limited insight into training deep learning models under a fixed number of epochs.In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem.Our primary focus is to compare the exact loss of SVRG to that of SGD at each iteration t.We show that the learning dynamics of our regression model closely matches with that of neural networks on MNIST and CIFAR-10 for both the underparameterized and the overparameterized models.Our analysis and experimental results suggest there is a trade-off between the computational cost and the convergence speed in underparametrized neural networks.SVRG outperforms SGD after a few epochs in this regime.However, SGD is shown to always outperform SVRG in the overparameterized regime.

Analisi non asintotica di SGD e SVRG, che mostra la forza di ogni algoritmo in termini di velocità di convergenza e costo computazionale, in entrambe i setting sotto-parametrizzate e sovra-parametrizzate.


Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models.Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained autoregressive model for better performance.Knowledge distillation is empirically useful, leading to large gains in accuracy for NAT models, but the reason for this success has, as of yet, been unclear.In this paper, we first design systematic experiments to investigate why knowledge distillation is crucial to NAT training.We find that knowledge distillation can reduce the complexity of data sets and help NAT to model the variations in the output data.Furthermore, a strong correlation is observed between the capacity of an NAT model and the optimal complexity of the distilled data for the best translation quality.Based on these findings, we further propose several approaches that can alter the complexity of data sets to improve the performance of NAT models.We achieve the state-of-the-art performance for the NAT-based models, and close the gap with the autoregressive baseline on WMT14 En-De benchmark.

Esaminiamo sistematicamente perché la knowledge distillation è cruciale per il training dei modelli di traduzione non autoregressiva (NAT), e proponiamo metodi per migliorare ulteriormente i dati distillati in modo che corrispondano meglio alla capacità di un modello NAT.


Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown breakthrough success in natural language processing (NLP), achieving state-of-the-art results in domains such as language modeling and machine translation.Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially-observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.In this work we demonstrate that the standard transformer architecture is difficult to optimize, which was previously observed in the supervised learning setting but becomes especially pronounced with RL objectives.We propose architectural modifications that substantially improve the stability and learning speed of the original Transformer and XL variant.The proposed architecture, the Gated Transformer-XL (GTrXL), surpasses LSTMs on challenging memory environments and achieves state-of-the-art results on the multi-task DMLab-30 benchmark suite, exceeding the performance of an external memory architecture.We show that the GTrXL, trained using the same losses, has stability and performance that consistently matches or exceeds a competitive LSTM baseline, including on more reactive tasks where memory is less critical.GTrXL offers an easy-to-train, simple-to-implement but substantially more expressive architectural alternative to the standard multi-layer LSTM ubiquitously used for RL agents in partially-observable environments.

Riusciamo a stabilizzare i trasformer per il training nel setting RL e dimostriamo un grande miglioramento rispetto agli LSTM su DMLab-30, abbinando un'architettura di memoria esterna.


Knowledge distillation is an effective model compression technique in which a smaller model is trained to mimic a larger pretrained model.However in order to make these compact models suitable for real world deployment, not only dowe need to reduce the performance gap but also we need to make them more robust to commonly occurring and adversarial perturbations.Noise permeates every level of the nervous system, from the perception of sensory signals to thegeneration of motor responses.We therefore believe that noise could be a crucial element in improving neural networks training and addressing the apparently contradictory goals of improving both the generalization and robustness of themodel.Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at either the input level or the supervision signals.Our results show that noise can improve both the generalization and robustness of the model.”Fickle Teacher” which uses dropout in teacher model as a source of response variation leads to significant generalization improvement.”Soft Randomization”, which matches the output distribution ofthe student model on the image with Gaussian noise to the output of the teacher on original image, improves the adversarial robustness manifolds compared to the student model trained with Gaussian noise.We further show the surprising effect of random label corruption on a model’s adversarial robustness.The study highlights the benefits of adding constructive noise in the knowledge distillation framework and hopes to inspire further work in the area.

Ispirati dalla variabilità trial-to-trial nel cervello che può risultare da più fonti di rumore, introduciamo la variabilità attraverso il rumore nel framework della knowledge distillation e studiamo il loro effetto sulla generalizzazione e la robustezza.


We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.Our clustering objective is based on optimizing normalized cuts, a criterion which measures both intra-cluster similarity as well as inter-cluster dissimilarity.We define a differentiable loss function equivalent to the expected normalized cuts.Unlike much of the work in unsupervised deep learning, our trained model directly outputs final cluster assignments, rather than embeddings that need further processing to be usable.Our approach generalizes to unseen datasets across a wide variety of domains, including text, and image.Specifically, we achieve state-of-the-art results on popular unsupervised clustering benchmarks (e.g., MNIST, Reuters, CIFAR-10, and CIFAR-100), outperforming the strongest baselines by up to 10.9%.Our generalization results are superior (by up to 21.9%) to the recent top-performing clustering approach with the ability to generalize.

Introduciamo un nuovo approccio end-to-end per imparare a raggruppare in assenza di esempi annotati. Definiamo una loss differenziabile equivalente ai tagli normalizzati attesi.


We introduce the largest (among publicly available) dataset for Cyrillic Handwritten Text Recognition and the first dataset for Cyrillic Text in the Wild Recognition, as well as suggest a method for recognizing Cyrillic Handwritten Text and Text in the Wild.Based on this approach, we develop a system that can reduce the document processing time for one of the largest mathematical competitions in Ukraine by 12 days and the amount of used paper by 0.5 ton.

Introduciamo diversi dataset per l'OCR in cirillico e un metodo per il suo riconoscimento


Most deep learning for NLP represents each word with a single point or single-mode region in semantic space, while the existing multi-mode word embeddings cannot represent longer word sequences like phrases or sentences.We introduce a phrase representation (also applicable to sentences) where each phrase has a distinct set of multi-mode codebook embeddings to capture different semantic facets of the phrase's meaning.The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space.We propose an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence (e.g., a phrase or a sentence) during test time.We find that the per-phrase/sentence codebook embeddings not only provide a more interpretable semantic representation but also outperform strong baselines (by a large margin in some tasks) on benchmark datasets for unsupervised phrase similarity, sentence similarity, hypernym detection, and extractive summarization.

Proponiamo un modo unsupervised per imparare embedding multipli per sentence e phrase


We present a meta-learning approach for adaptive text-to-speech (TTS) with few data.During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker.The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system.Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.We introduce and benchmark three strategies:(i) learning the speaker embedding while keeping the WaveNet core fixed,(ii) fine-tuning the entire architecture with stochastic gradient descent, and(iii) predicting the speaker embedding with a trained neural network encoder.The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.

Algoritmi sample efficient per adattare un modello text-to-speech a un nuovo stile di voce con prestazioni allo stato dell'arte.


This paper introduces a probabilistic framework for k-shot image classification.The goal is to generalise from an initial large-scale classification task to a separate task comprising new  classes and small numbers of examples.The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer).The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning.We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin.Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.

Questo articolo introduce un framework probabilistico per la classificazione delle immagini k-shot che raggiunge risultati allo stato dell'arte


Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay.We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy.Using a single network architecture and fixed set of hyper-parameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and matches the state of the art on DMLab-30.It is the first agent to exceed human-level performance in 52 of the 57 Atari games.

Indagine sulla combinazione di reti neurali ricorrenti e experience replay che risulta in un agente allo stato dell'arte sia su Atari-57 che su DMLab-30 usando un singolo set di iper-parametri.


The current state-of-the-art end-to-end semantic role labeling (SRL) model is a deep neural network architecture with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL, suggesting that neural network models could see great improvements from explicit modeling of syntax.In this work, we present linguistically-informed self-attention (LISA): a new neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech, predicate detection and SRL.For example, syntax is incorporated by training one of the attention heads to attend to syntactic parents for each token.Our model can predict all of the above tasks, but it is also trained such that if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model.In experiments on the CoNLL-2005 SRL dataset LISA achieves an increase of 2.5 F1 absolute over the previous state-of-the-art on newswire with predicted predicates and more than 2.0 F1 on out-of-domain data.On ConLL-2012 English SRL we also show an improvement of more than 3.0 F1, a 13% reduction in error.

La nostra combinazione di apprendimento multi-task e self-attention, addestrando il modello ad occuparsi dei progenitori in un albero sintattico, raggiunge lo stato dell'arte dei risultati CoNLL-2005 e CoNLL-2012 SRL per i modelli che utilizzano predicati.


Bottleneck structures with identity (e.g., residual) connection are now emerging popular paradigms for designing deep convolutional neural networks (CNN), for processing large-scale features efficiently.In this paper, we focus on the information-preserving nature of identity connection and utilize this to enable a convolutional layer to have a new functionality of channel-selectivity, i.e., re-distributing its computations to important channels.In particular, we propose Selective Convolutional Unit (SCU), a widely-applicable architectural unit that improves parameter efficiency of various modern CNNs with bottlenecks.During training, SCU gradually learns the channel-selectivity on-the-fly via the alternative usage of(a) pruning unimportant channels, and(b) rewiring the pruned parameters to important channels.The rewired parameters emphasize the target channel in a way that selectively enlarges the convolutional kernels corresponding to it.Our experimental results demonstrate that the SCU-based models without any postprocessing generally achieve both model compression and accuracy improvement compared to the baselines, consistently for all tested architectures.

Proponiamo un nuovo modulo che migliora qualsiasi architettura simile a ResNet imponendo un comportamento "channel selective" ai layer convoluzionali


Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to machine learning applications.One example is the estimation of treatment effects from observational data, where a subtask is to predict the effect of a treatment on subjects that are systematically different from those who received the treatment in the data.A related kind of distributional shift appears in unsupervised domain adaptation, where we are tasked with generalizing to a distribution of inputs that is different from the one in which we observe labels.We pose both of these problems as prediction under a shift in design.Popular methods for overcoming distributional shift are often heuristic or rely on assumptions that are rarely true in practice, such as having a well-specified model or knowing the policy that gave rise to the observed data.Other methods are hindered by their need for a pre-specified metric for comparing observations, or by poor asymptotic properties.In this work, we devise a bound on the generalization error under design shift, based on integral probability metrics and sample re-weighting.We combine this idea with representation learning, generalizing and tightening existing results in this space.Finally, we propose an algorithmic framework inspired by our bound and verify is effectiveness in causal effect estimation.

Una teoria e un framework algoritmico per la predizione sotto distributional shift, compresa la stima dell'effetto causale e domain adaptation


Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty  and out-of-distribution robustness of deep learning models.While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well.Bayesian neural networks, which learn distributions over the parameters of the network, are theoretically well-motivated by Bayesian principles, but do not perform as well as deep ensembles in practice, particularly under dataset shift.One possible explanation for this gap between theory and practice is that popular scalable approximate Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space.We investigate this hypothesis by building on recent work on understanding the loss landscape of neural networks and adding our own exploration to measure the similarity of functions in the space of predictions.Our results show that random initializations explore entirely different modes, while functions along an optimization trajectory or sampled from the subspace thereof cluster within a single mode predictions-wise, while often deviating significantly in the weight space.We demonstrate that while low-loss connectors between modes exist, they are not connected in the space of predictions.Developing the concept of the diversity--accuracy plane, we show that the decorrelation power of random initializations is unmatched by popular subspace sampling methods.

Studiamo i deep ensemble osservando la landscape della loss e lo spazio delle predizioni, dimostrando che il potere di decorrelazione delle inizializzazioni casuali è ineguagliato dal subspace sampling che esplora solo un singolo modo.


Existing deep learning approaches for learning visual features tend to extract more information than what is required for the task at hand.From a privacy preservation perspective, the input visual information is not protected from the model; enabling the model to become more intelligent than it is trained to be.Existing approaches for suppressing additional task learning assume the presence of ground truth labels for the tasks to be suppressed during training time.In this research, we propose a three-fold novel contribution:(i) a novel metric to measure the trust score of a trained deep learning model,(ii) a model-agnostic solution framework for trust score improvement by suppressing all the unwanted tasks, and(iii) a simulated benchmark dataset, PreserveTask, having five different fundamental image classification tasks to study the generalization nature of models.In the first set of experiments, we measure and improve the trust scores of five popular deep learning models: VGG16, VGG19, Inception-v1, MobileNet, and DenseNet and demonstrate that Inception-v1 is having the lowest trust score.Additionally, we show results of our framework on color-MNIST dataset and practical applications of face attribute preservation in Diversity in Faces (DiF) and IMDB-Wiki dataset.

Possiamo fidarci dei nostri modelli di deep learning? Un framework per misurare e migliorare l'affidabilità di un modello di deep learning durante il training.


Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training.In this work, we propose to train a policy while explicitly penalizing the mismatch between these two distributions over a fixed time horizon.We do this by using a learned model of the environment dynamics which is unrolled for multiple time steps, and training a policy network to minimize a differentiable cost over this rolled-out trajectory.This cost contains two terms: a policy cost which represents the objective the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on.We propose to measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks.We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.

Un approccio RL basato sul modello che utilizza una penalità di incertezza differenziabile per imparare le policy di guida da dati puramente osservativi.


Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application.In this paper we show that hierarchical multi-task dynamical systems (MTDSs) provide direct user control over sequence generation, via use of a latent  code z that specifies the customization to theindividual data sequence.This enables style transfer, interpolation and morphing within generated sequences.We show the MTDS can improve predictions via latent code interpolation, and avoid the long-term performance degradation of standard RNN approaches.

Personalizzare le predizioni dei modelli di sequenza (come LDS e RNN) attraverso un codice latente esplicito.


By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models.However, most existing adversarial training approaches are based on a specific type of adversarial attack.It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks.Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets.This work is mainly focused on the adversarial training yet efficient FGSM adversary.In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain.To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method.Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples.The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain.Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets.To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably.

Proponiamo un nuovo adversarial training con un metodo di domain adaptation che migliora significativamente la capacità di generalizzazione su adversarial example di diversi attacchi.


Character-level language modeling is an essential but challenging task in Natural Language Processing. Prior works have focused on identifying long-term dependencies between characters and have built deeper and wider networks for better performance.However, their models require substantial computational resources, which hinders the usability of character-level language models in applications with limited resources.In this paper, we propose a lightweight model, called Group-Transformer, that reduces the resource requirements for a Transformer, a promising method for modeling sequence with long-term dependencies.Specifically, the proposed method partitions linear operations to reduce the number of parameters and computational cost.As a result, Group-Transformer only uses 18.2\% of parameters compared to the best performing LSTM-based model, while providing better performance on two benchmark tasks, enwik8 and text8.When compared to Transformers with a comparable number of parameters and time complexity, the proposed model shows better performance.The implementation code will be available.

Questo articolo propone un nuovo Transformer leggero per character-level language modeling, utilizzando operazioni di gruppo.


Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to an unlabeled or label-scarce target domain.Recently domain-adversarial training (DAT) has shown promising capacity to learn a domain-invariant feature space by reversing the gradient propagation of a domain classifier.However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversarial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space.In this paper, we propose a novel Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial Reconstruction Network (ARN).The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures.Furthermore, ARN demonstrates strong robustness to a wide range of hyper-parameters settings, greatly alleviating the task of model selection.Extensive empirical results validate that our approach outperforms other state-of-the-art domain alignment methods.Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition.

Un approccio di training domain-adversarial stabile per una domain adaptation robusta e completa


We present an approach for expanding taxonomies with synonyms, or aliases.We target large shopping taxonomies, with thousands of nodes.A comprehensive set of entity aliases is an important component of identifying entities in unstructured text such as product reviews or search queries.Our method consists of two stages: we generate synonym candidates from WordNet and shopping search queries, then use a binary classiﬁer to ﬁlter candidates.We process taxonomies with thousands of synonyms in order to generate over 90,000 synonyms.We show that using the taxonomy to derive contextual features improves classiﬁcation performance over using features from the target node alone.We show that our approach has potential for transfer learning between diﬀerent taxonomy domains, which reduces the need to collect training data for new taxonomies.

Usiamo l'apprendimento automatico per generare sinonimi per grandi tassonomie di shopping.


Relational reasoning, the ability to model interactions and relations between objects, is valuable for robust multi-object tracking and pivotal for trajectory prediction.In this paper, we propose MOHART, a class-agnostic, end-to-end multi-object tracking and trajectory prediction algorithm, which explicitly accounts for permutation invariance in its relational reasoning.We explore a number of permutation invariant architectures and show that multi-headed self-attention outperforms the provided baselines and better accounts for complex physical interactions in a challenging toy experiment.We show on three real-world tracking datasets that adding relational reasoning capabilities in this way increases the tracking and trajectory prediction performance, particularly in the presence of ego-motion, occlusions, crowded scenes, and faulty sensor inputs.To the best of our knowledge, MOHART is the first fully end-to-end multi-object tracking from vision approach applied to real-world data reported in the literature.

MOHART utilizza un meccanismo di self-attention per eseguire il ragionamento relazionale nel multi-object tracking.


We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges:(a) efficient actor-critic learning with experience replay(b) stability of very off-policy learning.We employ those insights to accelerate hyper-parameter sweeps in which all participating agents run concurrently and share their experience via a common replay module.To this end we analyze the bias-variance tradeoffs in V-trace, a form of importance sampling for actor-critic methods.Based on our analysis, we then argue for mixing experience sampled from replay with on-policy experience, and propose a new trust region scheme that scales effectively to data distributions where V-trace becomes unstable.We provide extensive empirical validation of the proposed solution.We further show the benefits of this setup by demonstrating state-of-the-art data efficiency on Atari among agents trained up until 200M environment frames.

Indaghiamo e proponiamo soluzioni per due sfide nel reinforcement learning: (a) apprendimento efficiente dell'actor-critic con experience replay (b) stabilità dell'apprendimento molto off-policy.


Stochastic gradient descent (SGD) is the workhorse of modern machine learning.Sometimes, there are many different potential gradient estimators that can be used.When so, choosing the one with the best tradeoff between cost and variance is important.This paper analyzes the convergence rates of SGD as a function of time, rather than iterations.This results in a simple rule to select the estimator that leads to the best optimization convergence guarantee.This choice is the same for different variants of SGD, and with different assumptions about the objective (e.g. convexity or smoothness).Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given.Then, we extend to infinite pools of estimators, where each one is indexed by control variate weights.This is enabled by a reduction to a mixed-integer quadratic program.Empirically, automatically choosing an estimator performs comparably to the best estimator chosen with hindsight.

Proponiamo un algoritmo di selezione dello stimatore del gradiente con l'obiettivo di migliorare l'efficienza dell'ottimizzazione.


We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design.The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs.In this work, we give new results on the benefits of multi-generator architecture of GANs.We show that the minimax gap shrinks to \epsilon as the number of generators increases with rate O(1/\epsilon).This improves over the best-known result of O(1/\epsilon^2).At the core of our techniques is a novel application of Shapley-Folkman lemma to the generic minimax problem, where in the literature the technique was only known to work when the objective function is restricted to the Lagrangian function of a constraint optimization problem.Our proposed Stackelberg GAN performs well experimentally in both synthetic and real-world datasets, improving Frechet Inception Distance by 14.61% over the previous multi-generator GANs on the benchmark datasets.

Studiamo il problema di alleviare il problema dell'instabilità nella procedura di training GAN attraverso un nuovo design dell'architettura, con garanzie teoriche.


Nesterov SGD is widely used for training modern neural networks and other machine learning models.Yet, its advantages over SGD have not been theoretically clarified.Indeed, as we show  in this paper, both theoretically and empirically, Nesterov SGD with any parameter selection does not in general provide acceleration over ordinary SGD.Furthermore, Nesterov SGD may diverge for step sizes that ensure convergence of ordinary SGD.This is in contrast to the classical results in the deterministic setting, where the same step size ensures accelerated convergence of the Nesterov's method over optimal gradient descent.To address the non-acceleration issue, we  introduce a compensation term to Nesterov SGD.The resulting  algorithm, which we call MaSS, converges  for same step sizes as SGD.We prove that MaSS obtains an accelerated convergence rates over SGD for any mini-batch size in the linear setting.  For full batch, the convergence rate of MaSS matches the well-known accelerated rate of the Nesterov's method. We also analyze the  practically important question of the dependence of the convergence rate and  optimal hyper-parameters on the mini-batch size, demonstrating three distinct regimes: linear scaling, diminishing returns and saturation.Experimental evaluation of MaSS for several standard  architectures of deep networks, including ResNet and convolutional networks, shows improved performance over SGD, Nesterov SGD  and Adam.

Questo lavoro dimostra la non accelerazione di Nesterov SGD con qualsiasi iper-parametro, e propone un nuovo algoritmo che accelera in modo dimostrabile SGD nel setting iper-parametrizzato.


We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate the inductive bias of structured smoothness into a deep learning model.FGL achieves this goal by connecting nodes across layers based on spatial similarity.The use of structured smoothness, as implemented by FGL, is motivated by applications to structured spatial data, which is, in turn, motivated by domain knowledge.The proposed model architecture outperforms conventional neural network architectures across a variety of simulated and real datasets with structured smoothness.

Un layer feedforward per incorporare la smoothness strutturata in un modello di deep learning


Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity.To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation.This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field.Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the *effective* receptive field (ERF), which reflects how much each pixel contributes.It is thus natural to design other approaches to adapt the ERF directly during runtime.In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched.At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects.This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values.We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories.Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime.In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.

Non deformare le tue convoluzioni, deforma i tuoi kernel.


Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology.Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function.Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins.We newly approach this problem through the lens of representation learning.We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information.We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from(i) global structural similarity between proteins and(ii) pairwise residue contact maps for individual proteins.To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them.Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences.We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal.Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.

Presentiamo un metodo per l'apprendimento di modelli di embedding di sequenze proteiche utilizzando informazioni strutturali sotto forma di somiglianza strutturale globale tra le proteine e all'interno di contatti residuo-residuo di proteine.


Inspired by the adaptation phenomenon of biological neuronal firing, we propose regularity normalization: a reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space.By considering the neural network optimization process as a model selection problem, the implicit space is constrained by the normalizing factor, the minimum description length of the optimal universal code.We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization.The preliminary results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision task.As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform.Further research is proposed to discover these scenarios and explore the behaviors among different variants.

Considerando il processo di ottimizzazione delle reti neurali come un problema di selezione del modello, introduciamo un metodo di normalizzazione ispirato dalla biologia che estrae la regolarità statistica sotto il principio MDL per affrontare il problema dei dati unbalanced e limitati.


In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances.We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution.In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution.We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Autoencoders (WAE) and Variational Autoencoders (VAE), while benefiting from an embarrassingly simple implementation.

"Modellazione generativa senza bisogno di adversarial training"


The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations.However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs.IL in such situations can be challenging, especially when the level of demonstrators' expertise is unknown.We propose a new IL paradigm called Variational Imitation Learning with Diverse-quality demonstrations (VILD), where we explicitly model the level of demonstrators' expertise with a probabilistic graphical model and estimate it along with a reward function.We show that a naive estimation approach is not suitable to large state and action spaces, and fix this issue by using a variational approach that can be easily implemented using existing reinforcement learning methods.Experiments on continuous-control benchmarks demonstrate that VILD outperforms state-of-the-art methods.Our work enables scalable and data-efficient IL under more realistic settings than before.

Proponiamo un metodo di imitation learning per imparare da dimostrazioni di diversa qualità raccolte da dimostratori con diversi livelli di competenza.


With a growing number of available services, each having slightly different parameters, preconditions and effects, automated planning on general semantic services become highly relevant.However, most exiting planners only consider PDDL, or if they claim to use OWL-S, they usually translate it to PDDL, losing much of the semantics on the way.In this paper, we propose a new domain-independent heuristic based on a semantic distance that can be used by generic planning algorithms such as A* for automated planning of semantic services described with OWL-S.For the heuristic to include more relevant information we calculate the heuristic at runtime.Using this heuristic, we are able to produce better results (fewer expanded states) in less time than with established techniques.

Descrivere un'euristica semantica che si basa su una descrizione di servizio OWL-S e usa misure di distanza di parole e frasi per valutare l'utilità dei servizi per un dato obiettivo.