Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient. Largecamping  coefficients can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different damping coefficients. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive damping coefficients such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence with little to no tuning.

Introduciamo una semplice variante dell'ottimizzazione del momentum che è in grado di superare il momentum classico, Nesterov e Adam su task di deep learning con una sintonizzazione minima degli iperparametri.

Introduce una variante del momentum che aggrega diverse velocità con diversi coefficienti di smorzamento che diminuisce significativamente l'oscillazione

Proposto un metodo di momentum aggregato per l'ottimizzazione basata sul gradiente utilizzando più vettori di velocità con diversi fattori di smorzamento invece di un singolo vettore di velocità per migliorare la stabilità

Gli autori combinano diversi update step insieme per ottenere il momentum aggregato dimostrando anche che è più stabile degli altri metodi di momentum


Recurrent Neural Networks architectures excel at processing sequences by  modelling dependencies over different timescales. The recently introduced  Recurrent Weighted Average (RWA) unit captures long term dependencies  far better than an LSTM on several challenging tasks. The RWA achieves  this by applying attention to each input and computing a weighted average  over the full history of its computations. Unfortunately, the RWA cannot  change the attention it has assigned to previous timesteps, and so struggles  with carrying out consecutive tasks or tasks with changing requirements.  We present the Recurrent Discounted Attention (RDA) unit that builds on  the RWA by additionally allowing the discounting of the past.  We empirically compare our model to RWA, LSTM and GRU units on  several challenging tasks. On tasks with a single output the RWA, RDA and  GRU units learn much quicker than the LSTM and with better performance.  On the multiple sequence copy task our RDA unit learns the task three  times as quickly as the LSTM or GRU units while the RWA fails to learn at  all. On the Wikipedia character prediction task the LSTM performs best  but it followed closely by our RDA unit. Overall our RDA unit performs  well and is sample efficient on a large variety of sequence tasks.

Introduciamo la Recurrent Discounted Unit che applica l'attention a qualsiasi lunghezza di sequenza in tempo lineare

Questo articolo propone la Recurrent Discounted Attention (RDA), un'estensione della Recurrent Weighted Average (RWA) aggiungendo un fattore di sconto.

Estende la media dei pesi ricorrenti per superare la limitazione del metodo originale mantenendo il suo vantaggio e propone il metodo che usa le reti di Elman come RNN di base


Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different kind of stochastic layers. Each weight of a variance layer follows a zero-mean distribution and is only parameterized by its variance. It means that each object is represented by a zero-mean distribution in the space of the activations. We show that such layers can learn surprisingly well, can serve as an efficient exploration tool in reinforcement learning tasks and provide a decent defense against adversarial attacks. We also show that a number of conventional Bayesian neural networks naturally converge to such zero-mean posteriors. We observe that in these cases such zero-mean parameterization leads to a much better training objective than more flexible conventional parameterizations where the mean is being learned.

È possibile imparare una distribuzione gaussiana centrata sullo zero sui pesi di una rete neurale imparando solo le varianze, e funziona sorprendentemente bene.

Questo articolo studia gli effetti della media del posterior variazionale e propone il layer della varianza, che usa solo la varianza per memorizzare le informazioni

Studia le reti neurali a varianza che approssimano il posterior delle reti neurali bayesiane con distribuzioni gaussiane a media zero


Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data. At the same time, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper we propose a model, Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model achieves state-of-the-art performance on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.

Facciamo una rete di Graph Convolution Network, alimentando ciascuna con una potenza diversa della matrice di adiacenza, combinando tutta la loro rappresentazione in una sottorete di classificazione, raggiungendo lo stato dell'arte sulla classificazione semi-supervisionata dei nodi.

Propone una nuova rete di GCN con due approcci: un layer completamente connesso sopra le feature impilate e un meccanismo di attention che usa un peso scalare per GCN.

Presenta una rete di GCN che usa la statistica del cammino casuale per estrarre informazioni dai neighbor vicini e lontani nel grafo


Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy. However most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. In this paper we propose a cheap pruning algorithm based on difference of convex (DC) optimisation. We also provide theoretical analysis for the growth in the Generalisation Error (GE) of the new pruned network. Our method can be used with any convex regulariser and allows for a controlled degradation in classification accuracy while being orders of magnitude faster than competing approaches. Experiments on common feedforward neural networks show that for sparsity levels above 90% our method achieves 10% higher classification accuracy compared to Hard Thresholding.

Un algoritmo di pruning veloce per layer DNN fully connected con analisi teorica della degradazione dell'errore di generalizzazione.

Presenta un algoritmo di pruning economico per layer densi di DNN.

Propone una soluzione al problema del pruning delle DNN ponendo la funzione obiettivo Net-trim come una funzione Difference of convex (DC).


Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art.

Proponiamo una nuova rete temporale ibrida che raggiunge prestazioni allo stato dell'arte nella segmentazione di azioni video su tre dataset pubblici.

Discute il problema della segmentazione delle azioni in video lunghi, fino a 10 minuti, utilizzando un'architettura di encoder-decoder convoluzionale temporale

Propone una combinazione di rete temporale convoluzionale e ricorrente per la segmentazione di azioni video


Convolutional Neural Networks (CNNs) become deeper and deeper in recent years, making the study of model acceleration imperative. It is a common practice to employ a shallow network, called student, to learn from a deep one, which is termed as teacher. Prior work made many attempts to transfer different types of knowledge from teacher to student, however, there are two problems remaining unsolved. Firstly, the knowledge used by existing methods is highly dependent on task and dataset, limiting their applications. Secondly, there lacks an effective training scheme for the transfer process, leading to degradation of performance. In this work, we argue that feature is the most important knowledge from teacher. It is sufficient for student to just learn good features regardless of the target task. From this discovery, we further present an efficient learning strategy to mimic features stage by stage. Extensive experiments demonstrate the importance of features and show that the proposed approach significantly narrows down the gap between student and teacher, outperforming the state-of-the-art methods.

Questo articolo propone di trasferire la conoscenza dal deep model a quello shallow imitando le feature fase per fase.

Spiega un metodo di knowledge transfer fase per fase utilizzando diverse strutture di reti

Questo articolo propone di dividere una rete in più parti e distillare ogni parte in modo sequenziale per migliorare le prestazioni di distillazione nelle deep teacher network


We augment adversarial training (AT) with worst case adversarial training  (WCAT) which improves adversarial robustness by 11% over the current state-  of-the-art result in the `2-norm on CIFAR-10. We interpret adversarial training as  Total Variation Regularization, which is a fundamental tool in mathematical im-  age processing, and WCAT as Lipschitz regularization, which appears in Image  Inpainting. We obtain verifiable worst and average case robustness guarantees,  based on the expected and maximum values of the norm of the gradient of the  loss.

Miglioramenti all'adversarial robustness, così come garanzie di robustezza dimostrabili, sono ottenuti aumentando l'adversarial training con una regolarizzazione Lipschitz trattabile

Esplora l'arricchimento della loss di training con un termine aggiuntivo di regolarizzazione del gradiente per migliorare la robustezza dei modelli contro gli adversarial example

Utilizza un trucco per semplificare l'adversarial loss con una loss in cui l'adversarial perturbation appare in forma chiusa.


The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given \{\textit{passage, question}\} pair and select one of the $n$ given options. The current state of the art model for this task first computes a query-aware representation for the passage and then \textit{selects} the option which has the maximum similarity with this representation. However, when humans perform this task they do not just focus on option selection but use a combination of \textit{elimination} and \textit{selection}. Specifically, a human would first try to eliminate the most irrelevant option and then read the document again in the light of this new information (and perhaps ignore portions corresponding to the eliminated option). This process could be repeated multiple times till the reader is finally ready to select the correct option. We propose \textit{ElimiNet}, a neural network based model which tries to mimic this process. Specifically, it has gates which decide whether an option can be eliminated given the \{\textit{document, question}\} pair and if so it tries to make the document representation orthogonal to this eliminatedd option (akin to ignoring portions of the document corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the document representation and finally uses a selection module to pick the best option. We evaluate our model on the recently released large scale RACE dataset and show that it outperforms the current state of the art model on 7 out of the 13 question types in this dataset. Further we show that taking an ensemble of our \textit{elimination-selection} based method with a \textit{selection} based method gives us an improvement of 7\% (relative) over the best reported performance on this dataset.

Un modello che combina eliminazione e selezione per rispondere a domande a scelta multipla

Dà un'elaborazione del Gated Attention Reader aggiungendo gate basati sull'eliminazione delle risposte nella multiple choice reading comprehension

Questo articolo propone l'uso di un gate di eliminazione in architetture di modelli per task di comprensione della lettura, ma non raggiunge risultati allo stato dell'arte

Questo articolo sostiene un nuovo modello di multi-choice reading comprehension  basato sull'idea che alcune opzioni dovrebbero essere eliminate per dedurre una migliore rappresentazione del passaggio o della domanda.


Humans are capable of attributing latent mental contents such as beliefs, or intentions to others. The social skill is critical in everyday life to reason about the potential consequences of their behaviors so as to plan ahead. It is known that humans use this reasoning ability recursively, i.e. considering what others believe about their own beliefs.   In this paper, we start  from level-$1$ recursion and introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning. Our hypothesis is that it is beneficial for each agent to account for how the opponents would react to its future behaviors. Under the PR2 framework, we adopt variational Bayes methods to approximate the opponents' conditional policy, to which each agent finds the  best response and then improve their own policy. We develop  decentralized-training-decentralized-execution  algorithms, PR2-Q and PR2-Actor-Critic, that are proved to converge in the self-play scenario when there is one Nash equilibrium. Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge. Our experiments show that it is critical to reason about how the opponents believe about what the agent believes. We expect our work to contribute a new idea of modeling the opponents to the multi-agent reinforcement learning community.

Abbiamo proposto un nuovo framework di ragionamento ricorsivo probabilistico (PR2) per task di deep reinforcement learning multi-agente.

Propone un nuovo approccio per il training completamente decentralizzato nel reinforcement learning multi-agente

Il paper affronta il problema di dotare gli agenti RL di capacità di ragionamento ricorsivo in un ambiente multi-agente basato sull'ipotesi che il ragionamento ricorsivo sia vantaggioso per loro per convergere verso equilibri non banali

L'articolo introduce un metodo di training decentralizzato per reinforcement learning multi-agente, dove gli agenti deducono le policy di altri agenti e usano i modelli dedotti per prendere decisioni.


Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance and prove that it can be obtained with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.

Un nuovo algoritmo per ridurre l'overhead di comunicazione del deep learning distribuito distinguendo i gradienti "non ambigui".

Propone un metodo di compressione del gradiente basato sulla varianza per ridurre l'overhead di comunicazione del deep learning distribuito

Propone un nuovo modo di comprimere gli aggiornamenti del gradiente per SGD distribuito al fine di accelerare l'esecuzione complessiva

Introduce un metodo di compressione del gradiente basato sulla varianza per un efficiente training distribuito delle reti neurali e per misurare l'ambiguità.


In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achieving an optimal alignment in practical cases, we adopt a more principled strategy which, differently from the current Euclidean approaches, deploys alignment along geodesics. Our pipeline can be implemented by adding to the standard classification loss (on the labeled source domain), a source-to-target regularizer that is weighted in an unsupervised and data-driven fashion. We provide extensive experiments to assess the superiority of our framework on standard domain and modality adaptation benchmarks.

Una nuova tecnica non supervisionata di deep domain adaptation che unifica in modo efficiente l'allineamento delle correlazioni e la minimizzazione dell'entropia

Migliora l'approccio di allineamento delle correlazioni al domain adaptation sostituendo la distanza euclidea con la distanza log-euclidea geodetica tra due matrici di covarianza, e selezionando automaticamente il costo di bilanciamento in base all'entropia sul dominio di destinazione.

Proposta per l'allineamento di correlazione a minima entropia, un algoritmo di domain adaptation unsupervised che collega insieme la minimizzazione dell'entropia e i metodi di allineamento della correlazione.


Catastrophic interference has been a major roadblock in the research of continual learning. Here we propose a variant of the back-propagation algorithm, "Conceptor-Aided Backprop" (CAB), in which gradients are shielded by conceptors against degradation of previously learned tasks. Conceptors have their origin in reservoir computing, where they have been previously shown to overcome catastrophic forgetting. CAB extends these results to deep feedforward networks. On the disjoint and permuted MNIST tasks, CAB outperforms two other methods for coping with catastrophic interference that have recently been proposed.

Proponiamo una variante dell'algoritmo di backpropagation, in cui i gradienti sono schermati dai conceptor per prevenire la degradazione dei task precedentemente appresi.

Questo articolo applica la nozione di conceptor, una forma di regolarizzatore, per prevenire il forgetting nel continual learning, in particolare nel training di reti neurali su task sequenziali.

Introduce un metodo per imparare nuovi task, senza interferire con i task precedenti, usando i conceptor.


Recent advances in neural Sequence-to-Sequence (Seq2Seq) models reveal a purely data-driven approach to the response generation task. Despite its diverse variants and applications, the existing Seq2Seq models are prone to producing short and generic replies, which blocks such neural network architectures from being utilized in practical open-domain response generation tasks. In this research, we analyze this critical issue from the perspective of the optimization goal of models and the specific characteristics of human-to-human conversational corpora. Our analysis is conducted by decomposing the goal of Neural Response Generation (NRG) into the optimizations of word selection and ordering. It can be derived from the decomposing that Seq2Seq based NRG models naturally tend to select common words to compose responses, and ignore the semantic of queries in word ordering. On the basis of the analysis, we propose a max-marginal ranking regularization term to avoid Seq2Seq models from producing the generic and uninformative responses. The empirical experiments on benchmarks with several metrics have validated our analysis and proposed methodology.

Analizzare la ragione per cui i modelli neurali che generano risposte preferiscono risposte universali; proponiamo un metodo per evitarlo.

Indaga il problema delle risposte universali che affligge i modelli di generazione neurale Seq2Seq

L'articolo cerca di migliorare il task di generazione di risposte neurali de-enfatizzando le risposte comuni utilizzando la modifica della loss e presentando le risposte comuni/universali durante la fase di training.


The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present code2seq: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding.  We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to 16M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as general state-of-the-art NMT models. An interactive online demo of our model is available at http://code2seq.org. Our code, data and trained models are available at http://github.com/tech-srl/code2seq.

Sfruttiamo la struttura sintattica del codice sorgente per generare sequenze di linguaggio naturale.

Presenta un metodo per generare sequenze dal codice analizzando e producendo un albero di sintassi

Questo articolo introduce una codifica basata su AST per il codice di programmazione e mostra la sua efficacia nei task di extreme code summarization e code captioning.

Questo articolo presenta un nuovo modello code-to-sequence che sfrutta la struttura sintattica dei linguaggi di programmazione per codificare frammenti di codice sorgente e poi decodificarli in linguaggio naturale


We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. The proposed mechanism reuses CNN feature activations to find the most informative parts of the image at different depths with the help of gating mechanisms and without part annotations. Thus, it can be used to augment any layer of a CNN to extract low- and high-level local information to be more discriminative.    Differently, from other approaches, the mechanism we propose just needs a single pass through the input and it can be trained end-to-end through SGD. As a consequence, the proposed mechanism is modular, architecture-independent, easy to implement, and faster than iterative approaches.   Experiments show that, when augmented with our approach, Wide Residual Networks systematically achieve superior performance on each of five different fine-grained recognition datasets: the Adience age and gender recognition benchmark, Caltech-UCSD Birds-200-2011, Stanford Dogs, Stanford Cars, and UEC Food-100, obtaining competitive and state-of-the-art scores.

Miglioriamo le CNN con un nuovo meccanismo di attention per il riconoscimento preciso. Ciò risulta in prestazioni superiori su 5 dataset.

Descrive un nuovo meccanismo attenzionale applicato al riconoscimento preciso che migliora costantemente l'accuratezza del riconoscimento della baseline

Questo articolo propone un meccanismo di attention feed-forward per la classificazione fine-grained delle immagini

Questo articolo presenta un interessante meccanismo di attention per la classificazione fine-grained delle immagini.


Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearances. We propose a novel adaptive convolution method that learns the upsampling algorithm based on the local context at each location to address this problem. We modify a baseline GANs architecture by replacing normal convolutions with adaptive convolutions in the generator. Experiments on CIFAR-10 dataset show that our modified models improve the baseline model by a large margin. Furthermore, our models achieve state-of-the-art performance on CIFAR-10 and STL-10 datasets in the unsupervised setting.

Sostituiamo le normali convoluzioni con le convoluzioni adattive per migliorare il generatore delle GAN.

Propone di sostituire le convoluzioni nel generatore con un blocco di convoluzione adattiva che impara a generare pesi e bias di convoluzione delle operazioni di upsampling in modo adattivo rispetto alla posizione del pixel

Utilizza Adaptive Convolution nel contesto delle GAN con un blocco chiamato AdaConvBlock che sostituisce la normale Convoluzione, questo dà più contesto locale per kernel weight in modo da poter generare oggetti localmente flessibili.


Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this paper we explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters. Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. Crucially, we can still speed up training even after we have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent. Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. These predictions can come from a stale version of the other model so they can be safely computed using weights that only rarely get transmitted. Our second claim is that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible. We support our claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet, and the largest to-date dataset used for neural language modeling, containing $6\times 10^{11}$ tokens and based on the Common Crawl repository of web data.

Eseguiamo esperimenti su larga scala per dimostrare che una semplice variante online di distillation può aiutarci a scalare il training di reti neurali distribuite a più macchine.

Propone un metodo per scalare il training distribuito oltre i limiti attuali della mini-batch stochastic gradient descent

Proposta di un metodo di distillation online chiamato co-distillation, applicato su scala, in cui due diversi modelli sono addestrati per avvicinare le proprie predizioni a quelle dell'altro modello oltre a minimizzare la propria loss.

Viene introdotta una tecnica di distillation online per accelerare gli algoritmi tradizionali per il training di reti neurali distribuite su larga scala


Support Vector Machines (SVMs) are one of the most popular algorithms for classification and regression analysis. Despite their popularity, even efficient implementations have proven to be computationally expensive to train at a large-scale, especially in streaming settings. In this paper, we propose a novel coreset construction algorithm for efficiently generating compact representations of massive data sets to speed up SVM training. A coreset is a weighted subset of the original data points such that SVMs trained on the coreset are provably competitive with those trained on the original (massive) data set. We provide both lower and upper bounds on the number of samples required to obtain accurate approximations to the SVM problem as a function of the complexity of the input data. Our analysis also establishes sufficient conditions on the existence of sufficiently compact and representative coresets for the SVM problem. We empirically evaluate the practical effectiveness of our algorithm against synthetic and real-world data sets.

Presentiamo un algoritmo per accelerare il training SVM su massicci dataset costruendo rappresentazioni compatte che forniscono un'inferenza efficiente e approssimata in modo dimostrato.

Studia l'approccio del coreset per SVM e mira a campionare un piccolo insieme di punti ponderati tali che la loss valutata sui quei punti approssimi in modo dimostrabile quella sull'intero dataset

L'articolo suggerisce una costruzione di Coreset basata sull'importance sampling per rappresentare grandi moli di dati di training per SVM


The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates. Since signSGD carries out one-bit quantization of the gradients, it is extremely practical for distributed optimization where gradients need to be aggregated from different processors. For the first time, we establish convergence rates for signSGD on general non-convex functions under transparent conditions. We show that the rate of signSGD to reach first-order critical points matches that of SGD in terms of number of stochastic gradient calls, up to roughly a linear factor in the dimension. We carry out simple experiments to explore the behaviour of sign gradient descent (without the stochasticity) close to saddle points and show that it often helps completely avoid them without using either stochasticity or curvature information.

Dimostriamo un tasso di convergenza non convesso per il metodo del sign stochastic gradient. L'algoritmo ha collegamenti con algoritmi come Adam e Rprop, così come con schemi di quantizzazione del gradiente usati nell'apprendimento automatico distribuito.

Fornito un'analisi di convergenza dell'algoritmo Sign SGD per i casi non covessivi

L'articolo esplora un algoritmo che utilizza il segno dei gradienti invece dei gradienti effettivi per il training di deep model


Deep learning has found numerous applications thanks to its versatility and accuracy on pattern recognition problems such as visual object detection. Learning and inference in deep neural networks, however, are memory and compute intensive and so improving efficiency is one of the major challenges for frameworks such as PyTorch, Tensorflow, and Caffe. While the efficiency problem can be partially addressed with specialized hardware and its corresponding proprietary libraries, we believe that neural network acceleration should be transparent to the user and should support all hardware platforms and deep learning libraries.    To this end, we introduce a transparent middleware layer for neural network acceleration. The system is built around a compiler for deep learning, allowing one to combine device-specific libraries and custom optimizations while supporting numerous hardware devices. In contrast to other projects, we explicitly target the optimization of both prediction and training of neural networks. We present the current development status and some preliminary but encouraging results: on a standard x86 server, using CPUs our system achieves a 11.8x speed-up for inference and a 8.0x for batched-prediction (128); on GPUs we achieve a 1.7x and 2.3x speed-up respectively.

Introduciamo un middleware trasparente per l'accelerazione delle reti neurali, con un proprio motore di compilazione, raggiungendo fino a 11.8x di velocità sulle CPU e 2.3x sulle GPU.

Questo articolo propone un livello di middleware trasparente per l'accelerazione delle reti neurali e ottiene alcuni risultati di accelerazione su architetture CPU e GPU di base


Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. In particular, to aid convolutional neural networks in learning rotation invariance, we consider a simple, efficient conic convolutional scheme that encodes rotational equivariance, along with a method for integrating the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). We evaluated the efficacy of CFNet as compared to a standard CNN and group-equivariant CNN (G-CNN) for several different image classification tasks and demonstrated improved performance, including classification accuracy, computational efficiency, and its robustness to hyperparameter selection. Taken together, we believe CFNet represents a new scheme that has the potential to improve many imaging analysis applications.

Proponiamo la convoluzione conica e la 2D-DFT per codificare l'equivarianza di rotazione in una rete neurale.

Nel contesto della classificazione delle immagini, l'articolo propone un'architettura di rete neurale convoluzionale di feature map equivarianti alla rotazione che sono infine rese invarianti alla rotazione usando il modulo della trasformata discreta di Fourier 2D (DFT).

Gli autori forniscono una rete neurale invariante alla rotazione attraverso la combinazione di convoluzione conica e 2D-DFT


The problem of visual metamerism is defined as finding a family of perceptually  indistinguishable, yet physically different images. In this paper, we propose our  NeuroFovea metamer model, a foveated generative model that is based on a mixture  of peripheral representations and style transfer forward-pass algorithms. Our  gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder  which allows us to encode images in high dimensional space and interpolate  between the content and texture information with adaptive instance normalization  anywhere in the visual field. Our contributions include: 1) A framework for computing metamers that resembles a noisy communication system via a foveated feed-forward encoder-decoder network – We observe that metamerism arises as a byproduct of noisy perturbations that partially lie in the perceptual null space; 2) A perceptual optimization scheme as a solution to the hyperparametric nature of our metamer model that requires tuning of the image-texture tradeoff coefficients everywhere in the visual field which are a consequence of internal noise; 3) An  ABX psychophysical evaluation of our metamers where we also find that the rate  of growth of the receptive fields in our model match V1 for reference metamers  and V2 between synthesized samples. Our model also renders metamers at roughly  a second, presenting a ×1000 speed-up compared to the previous work, which now  allows for tractable data-driven metamer experiments.

Introduciamo un nuovo framework feed-forward per generare metameri visivi

Propone un modello NeuroFovea per la generazione di metameri point-of-fixation utilizzando un approccio di transfer di stile tramite un'architettura Encoder-Decoder

Un'analisi del metamerismo e un modello capace di produrre rapidamente metameri di valore per la psicofisica sperimentale e altri domini.

L'articolo propone un metodo veloce per generare metameri visivi - immagini fisicamente diverse che non possono essere distinte da un originale - attraverso un transfer di stile foveated, veloce e arbitrario


Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective. We establish: 1) for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for deep networks. In the case of two-layer networks, an infinite-width neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.

Mostriamo che il training delle reti relu feedforward con un regolarizzatore debole risulta in un margine massimo e analizziamo le implicazioni di questo risultato.

Il paper studia la teoria del margine per gli insiemi neurali e mostra che il margine massimo è monotonicamente crescente nella dimensione della rete

Questo articolo studia il bias implicito dei minimizzatori di una cross-entropy loss regolarizzata di una rete a due layer con attivazioni ReLU, ottenendo un limite superiore di generalizzazione che non aumenta con la dimensione della rete.


We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.

Un'architettura distribuita per reinforcement learning profondo su scala, utilizzando la generazione di dati in parallelo per migliorare lo stato dell'arte sul benchmark Arcade Learning Environment in una frazione del tempo di training in wall-clock degli approcci precedenti.

Esamina un sistema di deep RL distribuito in cui le esperienze, piuttosto che i gradienti, sono condivisi tra i lavori paralleli e il learner centralizzato

Un approccio parallelo al training di DQN, basato sull'idea di avere più actor che raccolgono dati in parallelo mentre un singolo learner addestra il modello da esperienze campionate dalla memoria centrale di replay.

Questo articolo propone un'architettura distribuita per deep reinforcement learning su scala, concentrandosi sull'aggiunta di parallelizzazione nell'algoritmo dell'actor nel framework Prioritized Experience Replay


Designing neural networks for continuous-time stochastic processes is challenging, especially when observations are made irregularly. In this article, we analyze neural networks from a frame theoretic perspective to identify the sufficient conditions that enable smoothly recoverable representations of signals in L^2(R). Moreover, we show that, under certain assumptions, these properties hold even when signals are irregularly observed. As we converge to the family of (convolutional) neural networks that satisfy these conditions, we show that we can optimize our convolution filters while constraining them so that they effectively compute a Discrete Wavelet Transform. Such a neural network can efficiently divide the time-axis of a signal into orthogonal sub-spaces of different temporal scale and localization. We evaluate the resulting neural network on an assortment of synthetic and real-world tasks: parsimonious auto-encoding, video classification, and financial forecasting.

Architetture neurali che forniscono rappresentazioni di segnali osservati in modo irregolare e che permettono la ricostruzione del segnale.

Dimostra che le reti neurali convoluzionali con funzione di attivazione Leaky ReLU sono strutture non lineari, con risultati simili per serie temporali campionate in modo non uniforme

Questo articolo considera le reti neurali su serie temporali e mostra che i primi filtri convoluzionali possono essere scelti per rappresentare una trasformata wavelet discreta.


Most state-of-the-art neural machine translation systems, despite being different  in architectural skeletons (e.g., recurrence, convolutional), share an indispensable  feature: the Attention. However, most existing attention methods are token-based  and ignore the importance of phrasal alignments, the key ingredient for the success  of phrase-based statistical machine translation. In this paper, we propose  novel phrase-based attention methods to model n-grams of tokens as attention  entities. We incorporate our phrase-based attentions into the recently proposed  Transformer network, and demonstrate that our approach yields improvements of  1.3 BLEU for English-to-German and 0.5 BLEU for German-to-English translation  tasks, and 1.75 and 1.35 BLEU points in English-to-Russian and Russian-to-English translation tasks   on WMT newstest2014 using WMT’16 training data.

Meccanismi di attention basati sulla frase per assegnare l'attention sulle frasi, ottenendo allineamenti di attention token-to-phrase, phrase-to-token, phrase-to-phrase, oltre alle attention token-token esistenti.

L'articolo presenta un meccanismo di attention che calcola una somma pesata non solo su singoli token ma su ngrammi (frasi).


Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with unexpected test samples from an unknown distribution different from training. Unlike domain adaptation methods, we cannot gather an "unexpected dataset" prior to test, and unlike novelty detection methods, a best-effort original task prediction is still expected. We compare a number of methods from related fields such as calibration and epistemic uncertainty modeling, as well as two proposed methods that reduce overconfident errors of samples from an unknown novel distribution without drastically increasing evaluation time: (1) G-distillation, training an ensemble of classifiers and then distill into a single model using both labeled and unlabeled examples, or (2) NCR, reducing prediction confidence based on its novelty detection score. Experimentally, we investigate the overconfidence problem and evaluate our solution by creating "familiar" and "novel" test splits, where "familiar" are identically distributed with training and "novel" are not. We discover that calibrating using temperature scaling on familiar data is the best single-model method for improving novel confidence, followed by our proposed methods. In addition, some methods' NLL performance are roughly equivalent to a regularly trained model with certain degree of smoothing. Calibrating can also reduce confident errors, for example, in gender recognition by 95% on demographic groups different from the training data.

Le reti profonde hanno maggiori probabilità di sbagliare in modo consistente quando la fase di testing contiene dati inaspettati. Proponiamo una metodologia sperimentale per studiare il problema, e due metodi per ridurre gli errori aventi alta confidenza su distribuzioni di input sconosciute.

Propone due idee per ridurre le predizioni errate troppo sicure: "G-distillation" di un insieme con dati extra non supervisionati e Novelty Confidence Reduction con un novelty detector

Gli autori propongono due metodi per stimare la fiducia di classificazione su nuove distribuzioni di dati non visti. La prima idea è quella di utilizzare metodi di ensemble come approccio di base per aiutare a identificare i casi incerti e poi utilizzare metodi di distillation per ridurre l'ensemble in un singolo modello che imita il comportamento dell'ensemble. La seconda idea è di usare un classificatore novelty detector e pesare l'output della rete in base al punteggio di novelty.


Progress in deep learning is slowed by the days or weeks it takes to train large models. The natural solution of using more hardware is limited by diminishing returns, and leads to inefficient use of additional resources. In this paper, we present a large batch, stochastic optimization algorithm that is both faster than widely used algorithms for fixed amounts of computation, and also scales up substantially better as more computational resources become available. Our algorithm implicitly computes the inverse Hessian of each mini-batch to produce descent directions; we do so without either an explicit approximation to the Hessian or Hessian-vector products. We demonstrate the effectiveness of our algorithm by successfully training large ImageNet models (InceptionV3, ResnetV1-50, ResnetV1-101 and InceptionResnetV2) with mini-batch sizes of up to 32000 with no loss in validation error relative to current baselines, and no increase in the total number of steps. At smaller mini-batch sizes, our optimizer improves the validation error in these models by 0.8-0.9\%. Alternatively, we can trade off this accuracy to reduce the number of training steps needed by roughly 10-30\%. Our work is practical and easily usable by others -- only one hyperparameter (learning rate) needs tuning, and furthermore, the algorithm is as computationally cheap as the commonly used Adam optimizer.

Descriviamo un pratico algoritmo di ottimizzazione per deep neural network che funziona più velocemente e genera modelli migliori rispetto agli algoritmi ampiamente utilizzati.

Il paper propone un nuovo algoritmo in cui si afferma di usare implicitamente l'Hessiana e si usa una motivazione dalle serie di potenza

Presenta un nuovo algoritmo del 2° ordine che usa implicitamente l'informazione sulla curvatura e mostra l'intuizione dietro gli schemi di approssimazione negli algoritmi e convalida l'euristica in vari esperimenti.


Recent work has shown that performing inference with fast, very-low-bitwidth  (e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate  results. However, although 2-bit approximated networks have been shown to  be quite accurate, 1 bit approximations, which are twice as fast, have restrictively  low accuracy. We propose a method to train models whose weights are a mixture  of bitwidths, that allows us to more finely tune the accuracy/speed trade-off. We  present the “middle-out” criterion for determining the bitwidth for each value, and  show how to integrate it into training models with a desired mixture of bitwidths.  We evaluate several architectures and binarization techniques on the ImageNet  dataset. We show that our heterogeneous bitwidth approximation achieves superlinear  scaling of accuracy with bitwidth. Using an average of only 1.4 bits, we are  able to outperform state-of-the-art 2-bit architectures.

Introduciamo l'approssimazione della larghezza di bit frazionaria e mostriamo che ha dei vantaggi significativi.

Suggerisce un metodo per variare il grado di quantizzazione in una rete neurale durante la forward phase

Mantenere la precisione di una network a 2 bit mentre si usano meno di 2 bit per i pesi


Pruning units in a deep network can help speed up inference and training as well as reduce the size of the model. We show that bias propagation is a pruning technique which consistently outperforms the common approach of merely removing units,  regardless of the architecture and the dataset.   We also show how a simple adaptation to an existing scoring function allows us to select the best units to prune.   Finally,  we show that the units selected by the best performing scoring functions are somewhat consistent over the course of training, implying the dead parts of the network appear during the stages of training.

La sostituzione media è un metodo efficiente per migliorare la loss dopo il pruning e le funzioni di punteggio basate sull'approssimazione di Taylor funzionano meglio con i valori assoluti.

Propone un semplice miglioramento dei metodi per il pruning delle unità utilizzando la "sostituzione media".

Questo articolo presenta una strategia di pruning di mean-replacement e utilizza l'espansione di Taylor a valore assoluto come funzione di punteggio per il pruning


Due to the phenomenon of “posterior collapse,” current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires altering the training objective. We develop an alternative that utilizes the most powerful generative models as decoders, optimize the variational lower bound, and ensures that the latent variables preserve and encode useful information. Our proposed δ-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate our method’s efficacy at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet 32 × 32.

Evitare il posterior collapse limitando inferiormente il tasso.

Presenta un approccio per prevenire il posterior collapse nei VAE limitando la famiglia dell'approssimazione variazionale al posterior

Questo articolo introduce un vincolo sulla famiglia dei posterior variazionali in modo che il termine KL possa essere controllato per combattere il posterior collapse nei deep generative model come i VAE


Mini-batch gradient descent and its variants are commonly used in deep learning. The principle of mini-batch gradient descent is to use noisy gradient calculated on a batch to estimate the real gradient, thus balancing the computation cost per iteration and the uncertainty of noisy gradient. However, its batch size is a fixed hyper-parameter requiring manual setting before training the neural network. Yin et al. (2017) proposed a batch adaptive stochastic gradient descent (BA-SGD) that can dynamically choose a proper batch size as learning proceeds. We extend the BA-SGD to momentum algorithm and evaluate both the BA-SGD and the batch adaptive momentum (BA-Momentum) on two deep learning tasks from natural language processing to image classification. Experiments confirm that batch adaptive methods can achieve a lower loss compared with mini-batch methods after scanning the same epochs of data. Furthermore, our BA-Momentum is more robust against larger step sizes, in that it can dynamically enlarge the batch size to reduce the larger uncertainty brought by larger step sizes. We also identified an interesting phenomenon, batch size boom. The code implementing batch adaptive framework is now open source, applicable to any gradient-based optimization problems.

Abbiamo sviluppato un momentum batch adaptive che può raggiungere una loss inferiore rispetto ai metodi mini-batch dopo la scansione delle stesse epoche di dati, ed è più robusto verso grandi step size.

Questo articolo affronta il problema di sintonizzare automaticamente la batch size durante il training in deep learning, e sostiene di estendere l'SGD batch adaptive al momentum adattivo e di adottare gli algoritmi ai problemi delle reti neurali complesse.

L'articolo propone di generalizzare un algoritmo che esegue SGD con adaptive batch size aggiungendo momentum alla utility function


Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure.   Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.

Usiamo i meta-gradienti per attaccare la procedura di training delle deep neural network per i grafi.

Studia il problema dell'apprendimento di un migliore parametro per un grafo poisoned che può massimizzare la loss di una graph neural network.

Un algoritmo per alterare la struttura del grafo aggiungendo/eliminando archi in modo da degradare la performance globale della classificazione dei nodi, con l'idea di usare il meta-learning per risolvere il problema di ottimizzazione a due livelli.


Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn inappropriate layouts or parametrizations that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.

Mostriamo che i modelli strutturati modulari sono i migliori in termini di generalizzazione sistematica e che le loro versioni end-to-end non generalizzano altrettanto bene.

Questo articolo valuta la generalizzazione sistemica tra reti neurali modulari e modelli  generici attraverso l'introduzione di un nuovo dataset di spatial reasoning

Una valutazione empirica mirata della generalizzazione nei modelli per il ragionamento visivo, focalizzata sul problema del riconoscimento delle triple (oggetto, relazione, oggetto) in scene sintetiche con lettere e numeri.


The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what events mediate the intensity and valence of social interactions. Furthermore, we show that embedding RFM modules inside agents results in faster learning systems compared to non-augmented baselines.   As more and more of the autonomous systems we develop and interact with become multi-agent in nature, developing richer analysis tools for characterizing how and why agents make decisions is increasingly necessary. Moreover, developing artificial agents that quickly and safely learn to coordinate with one another, and with humans in shared environments, is crucial.

I Relational Forward Model per l'apprendimento multi-agente fanno predizioni accurate del comportamento futuro degli agenti, producono rappresentazioni intepretabili e possono essere utilizzati all'interno degli agenti.

Un modo per ridurre la varianza nell'apprendimento model-free avendo un modello esplicito, con un'architettura simile a graph convolutional network, delle azioni che gli altri agenti compiranno.

Predire il comportamento multi-agente usando un modello relazionale forward con una componente ricorrente, superando due baseline e due ablazioni


We show that gradient descent on an unregularized logistic regression  problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss functions with an infimum at infinity, and we also discuss a multi-class generalizations to the cross entropy loss. Furthermore,  we show this convergence is very slow, and only logarithmic in the  convergence of the loss itself. This can help explain the benefit  of continuing to optimize the logistic or cross-entropy loss even  after the training error is zero and the training loss is extremely  small, and, as we show, even if the validation loss increases. Our  methodology can also aid in understanding implicit regularization  in more complex models and with other optimization methods.

La soluzione normalizzata della gradient descent sulla regressione logistica (o una loss che decade in modo simile) converge lentamente alla soluzione L2 max margin su dati separabili.

L'articolo offre una prova formale che la gradient descent sulla loss logistica converge molto lentamente alla soluzione SVM hard nel caso in cui i dati siano linearmente separabili.

Questo articolo si concentra sulla caratterizzazione del comportamento della minimizzazione della log-loss su dati linearmente separabili, e mostra che la log-loss, minimizzata con la discesa del gradiente, porta a convergere alla soluzione di max-margin.


Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superficial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classifier. Building on previous work on domain generalization, we hope to produce a classifier that will generalize to previously unseen domains, even when domain identifiers are not available during training. This setting is challenging because the model may extract many distribution-specific (superficial) signals together with distribution-agnostic (semantic) signals. To overcome this challenge, we incorporate the gray-level co-occurrence matrix (GLCM) to extract patterns that our prior knowledge suggests are superficial: they are sensitive to the texture but unable to capture the gestalt of an image. Then we introduce two techniques for improving our networks' out-of-sample performance. The first method is built on the reverse gradient method that pushes our model to learn representations from which the GLCM representation is not predictable. The second method is built on the independence introduced by projecting the model's representation onto the subspace orthogonal to GLCM representation's.  We test our method on the battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.

Basandoci sul lavoro precedente sulla generalizzazione del dominio, speriamo di produrre un classificatore che generalizzi a domini precedentemente non visti, anche quando gli identificatori di dominio non sono disponibili durante il training.

Un approccio di generalizzazione del dominio per rivelare informazioni semantiche basate su uno schema di proiezione lineare da CNN e layer di uscita NGLCM.

L'articolo propone un approccio unsupervised per identificare le feature delle immagini che non sono significative per i task di classificazione delle immagini


In this paper, we conduct an intriguing experimental study about the physical adversarial attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide vehicles from being detected by state-of-the-art convolutional neural network based detectors. Our approach alternates between two threads. In the first, we train a neural approximation function to imitate how a simulator applies a camouflage to vehicles and how a vehicle detector performs given images of the camouflaged vehicles. In the second, we minimize the approximated detection score by searching for the optimal camouflage. Experiments show that the learned camouflage can not only hide a vehicle from the image-based detectors under many test cases but also generalizes to different environments, vehicles, and object detectors.

Proponiamo un metodo per imparare la mimetizzazione fisica del veicolo per attaccare in modo adversarial gli object detector in natura. Troviamo il nostro camuffamento efficace e trasferibile.

Gli autori studiano il problema dell'apprendimento di un modello di mimetizzazione che, quando applicato a un veicolo simulato, impedirà a un rilevatore di oggetti di rilevarlo.

Questo articolo mira all'adversarial learning per interferire nel rilevamento di auto apprendendo modelli di mimetizzazione


As deep learning-based classifiers are increasingly adopted in real-world applications, the importance of understanding how a particular label is chosen grows. Single decision trees are an example of a simple, interpretable classifier, but are unsuitable for use with complex, high-dimensional data. On the other hand, the variational autoencoder (VAE) is designed to learn a factored, low-dimensional representation of data, but typically encodes high-likelihood data in an intrinsically non-separable way.   We introduce the differentiable decision tree (DDT) as a modular component of deep networks and a simple, differentiable loss function that allows for end-to-end optimization of a deep network to compress high-dimensional data for classification by a single decision tree.   We also explore the power of labeled data in a  supervised VAE (SVAE) with a Gaussian mixture prior, which leverages label information to produce a high-quality generative model with improved bounds on log-likelihood.   We combine the SVAE with the DDT to get our classifier+VAE (C+VAE), which is competitive in both classification error and log-likelihood, despite optimizing both simultaneously and using a very simple encoder/decoder architecture.

Combiniamo alberi decisionali differenziabili con autoencoder variazionali supervisionati per migliorare l'interpretabilità della classificazione.

Questo articolo propone un modello ibrido di un autoencoder variazionale composto con un albero decisionale differenziabile, e uno schema di training di accompagnamento, con esperimenti che dimostrano le prestazioni di classificazione dell'albero, le prestazioni di negative log likelihood e l'interpretabilità dello spazio latente.

L'articolo cerca di costruire un classificatore interpretabile e accurato impilando un VAE supervisionato e un albero decisionale differenziabile


We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier on the weighted source samples. We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class. To the best of our knowledge, this is the first generalization bound for the label-shift problem where the labels in the target domain are not available. Based on this bound, we propose a regularized estimator for the small-sample regime which accounts for the uncertainty in the estimated weights. Experiments on the CIFAR-10 and MNIST datasets show that RLLS improves classification accuracy, especially in the low sample and large-shift regimes, compared to previous methods.

Un approccio pratico e garantito per il training di classificatori efficienti in presenza di spostamenti di label tra i dataset di origine e di destinazione

Gli autori propongono un nuovo algoritmo per migliorare la stabilità della procedura di stima dell'importanza di una classe con una procedura a due fasi.

Gli autori considerano il problema dell'apprendimento sotto label-shift, dove le proporzioni delle label differiscono mentre le condizioni sono uguali, e propongono uno stimatore migliorato con regolarizzazione.


The statistics of the real visual world presents a long-tailed distribution: a few classes have significantly more training instances than the remaining classes in a dataset. This is because the real visual world has a few classes that are common while others are rare. Unfortunately, the performance of a convolutional neural network is typically unsatisfactory when trained using a long-tailed dataset. To alleviate this issue, we propose a method that discriminatively learns an embedding in which a simple Bayesian classifier can balance the class-priors to generalize well for rare classes. To this end, the proposed approach uses a Gaussian mixture model to factor out class-likelihoods and class-priors in a long-tailed dataset. The proposed method is simple and easy-to-implement in existing deep learning frameworks. Experiments on publicly available datasets show that the proposed approach improves the performance on classes with few training instances, while maintaining a comparable performance to the state-of-the-art on classes with abundant training examples.

Approccio per migliorare la precisione di classificazione sulle classi nella coda della distribuzione.

L'obiettivo principale di questo articolo è quello di imparare un classificatore basato su ConvNet che si comporta meglio per le classi nella coda della distribuzione delle occorrenze delle classi.

Proposta di un framework bayesiano con un modello di mistura gaussiana per affrontare un problema nelle applicazioni di classificazione, cioè che il numero di dati di training da classi diverse è sbilanciato.


As deep reinforcement learning is being applied to more and more tasks, there is a growing need to better understand and probe the learned agents. Visualizing and understanding the decision making process can be very valuable to comprehend and identify problems in the learned behavior. However, this topic has been relatively under-explored in the reinforcement learning community. In this work we present a method for synthesizing states of interest for a trained agent. Such states could be situations (e.g. crashing or damaging a car) in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved (e.g. risky states) are often interesting to understand the situational awareness of the system. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insightful visualizations for a variety of environments and reinforcement learning methods. We explore these issues in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify significant decision scenarios with this technique, we believe this general approach could serve as an important tool for AI safety applications.

Presentiamo un metodo per sintetizzare gli stati di interesse per gli agenti di reinforcement learning al fine di analizzare il loro comportamento.

Questo articolo propone un modello generativo di osservazioni visive in RL che è in grado di generare osservazioni di interesse.

Un approccio per la visualizzazione degli stati di interesse che coinvolge un autoencoder variazionale che impara a ricostruire lo spazio degli stati e una fase di ottimizzazione che trova i parametri di condizionamento per generare immagini sintetiche.


We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training. This allows the  DNN to abstain on confusing or difficult-to-learn examples while improving performance on the non-abstained samples. We show that such deep abstaining classifiers can: (i) learn representations for structured noise -- where noisy training labels or confusing examples are correlated with underlying features -- and then learn to abstain based on such features; (ii) enable robust learning in the presence of arbitrary or unstructured noise by identifying noisy samples; and (iii) be used as an effective out-of-category detector that learns to reliably abstain when presented with samples from  unknown classes. We provide analytical results on loss function behavior that enable automatic tuning of accuracy and coverage, and demonstrate the utility of the deep abstaining classifier using multiple image benchmarks, Results indicate significant improvement in learning in the presence of label noise.

Una deep neural network di astensione addestrata con una nuova loss che impara rappresentazioni per quando astenersi permettendo un apprendimento robusto in presenza di diversi tipi di rumore.

Una nuova loss per il training di una deep neural network che può astenersi, con prestazioni esaminate da angoli diversi in presenza di rumore strutturato e non strutturato, e rilevamento dell'open world.

Questo manoscritto introduce classificatori deep con astensione che modificano la cross-entropy loss multiclasse con una loss di astensione, che viene poi applicata a task di classificazione di immagini perturbate


Temporal Difference learning with function approximation has been widely used recently and has led to several successful results.   However, compared with the original tabular-based methods, one major drawback of temporal difference learning with neural networks and other function approximators is that they tend to over-generalize across temporally successive states, resulting in slow convergence and even instability. In this work, we propose a novel TD learning method, Hadamard product Regularized TD (HR-TD), that reduces over-generalization and thus leads to faster convergence. This approach can be easily applied to both linear and nonlinear function approximators.   HR-TD is evaluated on several linear and nonlinear benchmark domains, where we show improvement in learning behavior and performance.

Una tecnica di regolarizzazione per TD learning che evita la sovrageneralizzazione temporale, specialmente nelle deep network

Una variazione su TD learning per il caso dell'approssimazione della funzione che tenta di risolvere il problema della sovra-generalizzazione attraverso stati temporalmente successivi.

L'articolo introduce HR-TD, una variante dell'algoritmo TD(0), intesa a migliorare il problema della sovrageneralizzazione nel TD convenzionale


We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.   To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly lower number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we present (1) a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and (2) we show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters.

Presentiamo un nuovo kernel CNN per griglie non strutturate per segnali sferici, e mostriamo una precisione significativa e un guadagno di efficienza dei parametri su task come la classificazione 3D e la segmentazione omnidirezionale delle immagini.

Un metodo efficiente che permette il deep learning su dati sferici che raggiunge numeri competitivi/stato dell'arte con molti meno parametri rispetto agli approcci popolari.

L'articolo propone un nuovo kernel di convoluzione per CNN sulle grid non strutturate e formula la convoluzione con una combinazione lineare di operatori differenziali.


Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks---while we cannot predict the precise trajectory of a robot arm between being at rest and holding an object up, we can be certain that it must have picked the object up. To exploit this, we decouple visual prediction from a rigid notion of time. While conventional approaches predict frames at regularly spaced temporal intervals, our time-agnostic predictors (TAP) are not tied to specific times so that they may instead discover predictable "bottleneck" frames no matter when they occur. We evaluate our approach for future and intermediate frame prediction across three robotic manipulation tasks. Our predictions are not only of higher visual quality, but also correspond to coherent semantic subgoals in temporally extended tasks.

Nei task di predizione visiva, lasciare che il modello predittivo scelga quali tempi predire fa due cose: (i) migliora la qualità della predizione e (ii) porta a predizioni semanticamente coerenti del "bottleneck state", che sono utili per la pianificazione.

Un metodo per la predizione dei frame in un video, l'approccio comprende che la predizione dell'obiettivo è galleggiante, risolto da un minimo sull'errore di predizione.

Riformula il task di predizione/interpolazione video in modo che un predittore non sia costretto a generare frame a intervalli di tempo fissi, ma sia invece addestrato a generare frame che accadono in qualsiasi punto del futuro.


In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.

Abbiamo usato una LSTM per rilevare quando uno smartphone entra in un edificio. Poi abbiamo previsto il piano in cui si trova il dispositivo utilizzando i dati dei sensori a bordo dello smartphone.

L'articolo introduce un sistema per stimare il livello di un piano di un edificio attraverso i dati del sensore del loro dispositivo mobile utilizzando una LSTM e i cambiamenti nella pressione barometrica

Proposta di un metodo in due fasi per determinare su quale piano si trova un telefono cellulare all'interno di un edificio alto.


Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failure experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, but we also found that little amount of hindsight advice is sufficient for the learning to take off, showing the practical aspect of the method.

Combinare la rappresentazione degli obiettivi linguistici con i replay dell'esperienza a posteriori.

Questo articolo considera l'assunzione implicita nell'experience replay a posteriori, cioè che vi sia accesso a una mappatura dagli stati agli obiettivi, e propone una rappresentazione degli obiettivi in linguaggio naturale.

Questa presentazione utilizza il framework Hindsight Experience Replay con obiettivi in linguaggio naturale per migliorare la sample efficiency dei modelli instruction-following.


Learning rich and compact representations is an open topic in many fields such as word embedding, visual question-answering, object recognition or image retrieval. Although deep neural networks (convolutional or not) have made a major breakthrough during the last few years by providing hierarchical, semantic and abstract representations for all of these tasks, these representations are not necessary as rich as needed nor as compact as expected. Models using higher order statistics, such as bilinear pooling, provide richer representations at the cost of higher dimensional features. Factorization schemes have been proposed but without being able to reach the original compactness of first order models, or at a heavy loss in performances. This paper addresses these two points by extending factorization schemes to codebook strategies, allowing compact representations with the same dimensionality as first order representations, but with second order performances. Moreover, we extend this framework with a joint codebook and factorization scheme, granting a reduction both in terms of parameters and computation cost. This formulation leads to state-of-the-art results and compact second-order models with few additional parameters and intermediate representations with a dimension similar to that of first-order statistics.

Proponiamo un codebook congiunto e uno schema di fattorizzazione per migliorare il pooling di secondo ordine.

Questo articolo presenta un modo per combinare le rappresentazioni fattorizzate di secondo ordine esistenti con un'assegnazione hard in stile codebook.

Proposta di una nuova rappresentazione bilineare basata su un modello codebook, e una formulazione efficiente in cui le proiezioni basate su codebook sono fattorizzate tramite proiezione condivisa per ridurre ulteriormente il numero dei parametri.


Natural language understanding research has recently shifted towards complex Machine Learning and Deep Learning algorithms. Such models often outperform their simpler counterparts significantly. However, their performance relies on the availability of large amounts of labeled data, which are rarely available. To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision. We apply this methodology on biomedical relation extraction, a task where training datasets are excessively time-consuming and expensive to create, yet has a major impact on downstream applications such as drug discovery. We demonstrate in two small-scale controlled experiments that our method consistently enhances the performance of an LSTM network, with performance improvements comparable to hand-labeled training data. Finally, we discuss the optimal setting for applying weak supervision using this methodology.

Proponiamo e applichiamo una metodologia di meta-learning basata sulla weak supervision, per combinare l'apprendimento semi-supervisionato ed ensemble sul task di estrazione di relazioni biomediche.

Un metodo semi-supervised per la classificazione delle relazioni, che addestra più learner di base usando un piccolo dataset annotato e applica alcuni di loro per annotare esempi non annotati per l'apprendimento semi-supervised.

Questo articolo affronta il problema della generazione di dati di training per l'estrazione di relazioni biologiche, e utilizza le predizioni dei dati annotati da classificatori deboli come dati di training aggiuntivi per un algoritmo di meta learning.

Questo articolo propone una combinazione di semi-supervised e ensemble learning per l'estrazione di informazioni, con esperimenti condotti su un task di estrazione di relazioni biomediche


We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.

Una classe di reti che generano modelli semplici al volo (chiamati explanation) che agiscono come un regolarizzatore e permettono una diagnostica coerente del modello e l'interpretabilità.

Gli autori sostengono che lavori precedenti integrino direttamente le reti neurali nei graphical model come componenti, il che rende i modelli non interpretabili.

Proposta di una combinazione di reti neurali e graphical model utilizzando una deep neural network per prevedere i parametri di un graphical model.


The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous control tasks. However, GAIL requires a huge number of interactions with environment during training. We believe that IL algorithm could be more applicable to the real-world environments if the number of interactions could be reduced. To this end, we propose a model free, off-policy IL algorithm for continuous control. The keys of our algorithm are two folds: 1) adopting deterministic policy that allows us to derive a novel type of policy gradient which we call deterministic policy imitation gradient (DPIG), 2) introducing a function which we call state screening function (SSF) to avoid noisy policy updates with states that are not typical of those appeared on the expert’s demonstrations. Experimental results show that our algorithm can achieve the goal of IL with at least tens of times less interactions than GAIL on a variety of continuous control tasks.

Proponiamo un algoritmo di imitation learning senza modello che è in grado di ridurre il numero di interazioni con l'ambiente rispetto all'algoritmo di imitation learning allo stato dell'arte, cioè GAIL.

Propone di estendere l'algoritmo deterministico del policy gradient per imparare dalle dimostrazioni, mentre viene combinato con un tipo di stima della densità dell'esperto.

Questo articolo considera il problema dell'imitation learning model-free e propone un'estensione dell'algoritmo di generative adversarial imitation learning, sostituendo la policy stocastica del learner con una deterministica.

L'articolo combina IRL, adversarial training e idee dai policy gradient deterministici con l'obiettivo di diminuire la sample complexity


Convolution acts as a local feature extractor in convolutional neural networks (CNNs). However, the convolution operation is not applicable when the input data is supported on an irregular graph such as with social networks, citation networks, or knowledge graphs. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph-structured data and provides a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution, replacing the square filter for the grid-structured data in traditional CNNs. The outputs are the weighted sum of these filters’ outputs, extraction of both vertex features and strength of correlation between vertices. It  can be used with both directed and undirected graphs. The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing. Further, as no approximation to the convolution is needed, TAGCN exhibits better performance than existing graph-convolution-approximation methods on a number  of data sets. As only the polynomials of degree two of the adjacency matrix are used, TAGCN is also computationally simpler than other recent methods.

Graph CNN a bassa complessità computazionale (senza approssimazione) con una migliore accuratezza di classificazione

Propone un nuovo approccio CNN alla classificazione dei grafi usando un filtro basato su cammini in uscita di lunghezza crescente per incorporare informazioni da vertici più distanti in un solo passo di propagazione.

Proposta di una nuova architettura di rete neurale per la classificazione semi-supervisionata dei grafi, basandosi sui filtri polinomiali dei grafi e utilizzandoli su layer successivi di una rete neurale con funzioni di attivazione ReLU.

L'articolo introduce Topology Adaptive GCN per generalizzare le reti di convoluzione ai dati strutturati a grafo


Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ``forgetting event'' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.

Mostriamo che la catastrophic forgetting si verifica all'interno di quello che è considerato un singolo task e troviamo che gli esempi che non sono inclini alla dimenticanza possono essere rimossi dal training set senza perdita di generalizzazione.

Il paper studia il comportamento di forgetting degli esempi di training durante SGD, e mostra che esistono "esempi di supporto" nel training delle reti neurali attraverso diverse architetture di rete.

Questo articolo analizza la misura in cui le reti imparano a classificare correttamente esempi specifici e poi dimenticano questi esempi nel corso del training.

L'articolo studia se alcuni esempi nel training delle reti neurali sono più difficili da imparare di altri. Tali esempi sono dimenticati e ri-appresi più volte attraverso l'apprendimento.


Discovering objects and their attributes is of great importance for autonomous agents to effectively operate in human environments. This task is particularly challenging due to the ubiquitousness of objects and all their nuances in perceptual and semantic detail. In this paper we present an unsupervised approach for learning disentangled representations of objects entirely from unlabeled monocular videos. These continuous representations are not biased by or limited by a discrete set of labels determined by human labelers. The proposed representation is trained with a metric learning loss, where objects with homogeneous features are pushed together, while those with heterogeneous features are pulled apart. We show these unsupervised embeddings allow to discover object attributes and can enable robots to self-supervise in previously unseen environments. We quantitatively evaluate performance on a large-scale synthetic dataset with 12k object models, as well as on a real dataset collected by a robot and show that our unsupervised object understanding generalizes to previously unseen objects. Specifically, we demonstrate the effectiveness of our approach on robotic manipulation tasks, such as pointing at and grasping of objects. An interesting and perhaps surprising finding in this approach is that given a limited set of objects, object correspondences will naturally emerge when using metric learning without requiring explicit positive pairs.

Un approccio unsupervised per l'apprendimento di rappresentazioni disentangled di oggetti interamente da video monoculari senza label.

Progetta una rappresentazione delle feature da sequenze video catturate da una scena da diversi punti di vista.

Proposta di un metodo di representation learning unsupervised per gli input visivi che incorpora un approccio di metric learning che avvicina le coppie di patch di immagini che sono nearest neighbor nello spazio di embedding mentre spinge a parte altre coppie.

Questo articolo esplora il self-supervised learning di rappresentazioni di oggetti, con l'idea principale di incoraggiare gli oggetti con feature simili ad essere ulteriormente "attratti" l'uno dall'altro.


Learning from a scalar reward in continuous action space environments is difficult and often requires millions if not billions of interactions.   We introduce state aligned vector rewards, which are easily defined in metric state spaces and allow our deep reinforcement learning agent to tackle the curse of dimensionality.   Our agent learns to map from action distributions to state change distributions implicitly defined in a quantile function neural network.    We further introduce a new reinforcement learning technique inspired by quantile regression which does not limit agents to explicitly parameterized action distributions.   Our results in high dimensional state spaces show that training with vector rewards allows our agent to learn multiple times faster than an agent training with scalar rewards.

Si esegue il training di un agente che predice i cambiamenti di stato dalle distribuzioni delle azioni tramite un vettore di reward allineato allo stato, usando una nuova tecnica di reinforcement learning ispirata alla regressione quantile.

Presenta un algoritmo che mira ad accelerare il reinforcement learning in situazioni in cui la reward è allineata con lo spazio di stato.

Questo articolo affronta il RL nello spazio d'azione continuo, utilizzando una policy ri-parametrizzata e un nuovo obiettivo di training basato su vettori.

Questo lavoro propone di mescolare il RL distributivo con una rete incaricata di modellare l'evoluzione del mondo in termini di quantili, sostenendo miglioramenti nella sample efficiency.


We propose Episodic Backward Update - a new algorithm to boost the performance of a deep reinforcement learning agent by fast reward propagation. In contrast to the conventional use of the replay memory with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state into its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate effectively throughout the sampled episode. We evaluate our algorithm on 2D MNIST Maze Environment and 49 games of the Atari 2600 Environment and show that our agent improves sample efficiency with a competitive computational cost.

Proponiamo Episodic Backward Update, un nuovo algoritmo di deep reinforcement learning che campiona le transizioni episodio per episodio e aggiorna i valori in modo ricorsivo all'indietro per ottenere un apprendimento veloce e stabile.

Propone una nuova DQN in cui gli obiettivi sono calcolati su un episodio completo con un aggiornamento all'indietro (dalla fine all'inizio) per una propagazione più veloce delle reward dalla fine dell'episodio.

Gli autori propongono di modificare l'algoritmo DQN applicando l'operatore max di Bellman ricorsivamente su una traiettoria con un certo decadimento per evitare di accumulare errori con il max innestato.

Nelle deep-Q network, si aggiornano i valori Q a partire dalla fine dell'episodio per facilitare la propagazione rapida delle reward lungo l'episodio.


Survival Analysis (time-to-event analysis) in the presence of multiple possible adverse events, i.e., competing risks, is a challenging, yet very important problem in medicine, finance, manufacturing, etc. Extending classical survival analysis to competing risks is not trivial since only one event (e.g. one cause of death) is observed and hence, the incidence of an event of interest is often obscured by other related competing events. This leads to the nonidentifiability of the event times’ distribution parameters, which makes the problem significantly more challenging. In this work we introduce Siamese Survival Prognosis Network, a novel Siamese Deep Neural Network architecture that is able to effectively learn from data in the presence of multiple adverse events. The Siamese Survival Network is especially crafted to issue pairwise concordant time-dependent risks, in which longer event times are assigned lower risks. Furthermore, our architecture is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics of cross-entropy etc., and which are not able to capture the unique requirements of survival analysis with competing risks. Our results show consistent performance improvements on a number of publicly available medical datasets over both statistical and deep learning state-of-the-art methods.

In questo lavoro introduciamo una nuova architettura di deep neural network siamese che è in grado di imparare efficacemente dai dati in presenza di più eventi avversi.

Questo articolo introduce le reti neurali siamesi nel framework dei rischi concorrenti ottimizzando direttamente l'indice c

Gli autori affrontano i problemi di stima del rischio in un contesto di analisi della sopravvivenza con rischi concorrenti e propongono di ottimizzare direttamente l'indice di discriminazione dipendente dal tempo utilizzando una siamese survival network


The digitization of data has resulted in making datasets available to millions of users in the form of relational databases and spreadsheet tables. However, a majority of these users come from diverse backgrounds and lack the programming expertise to query and analyze such tables. We present a system that allows for querying data tables using natural language questions, where the system translates the question into an executable SQL query. We use a deep sequence to sequence model in wich the decoder uses a simple type system of SQL expressions to structure the output prediction. Based on the type, the decoder either copies an output token from the input question using an attention-based copying mechanism or generates it from a fixed vocabulary. We also introduce a value-based loss function that transforms a distribution over locations to copy from into a distribution over the set of input tokens to improve training of our model. We evaluate our model on the recently released WikiSQL dataset and show that our model trained using only supervised learning significantly outperforms the current state-of-the-art Seq2SQL model that uses reinforcement learning.

Presentiamo un modello di pointer network type-based insieme a un metodo di loss basato sul valore per addestrare efficacemente un modello neurale per tradurre il linguaggio naturale in SQL.

L'articolo sostiene di sviluppare un nuovo metodo per mappare le query del linguaggio naturale in SQL utilizzando una grammatica per guidare la decodifica e utilizzando una nuova loss per il meccanismo di pointer/copy