Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing popularity of external memory mechanisms to handle long-term dependencies in deep learning systems, we propose a novel algorithm which uses a reservoir sampling procedure to maintain an external memory consisting of a fixed number of past states. The algorithm allows a deep reinforcement learning agent to learn online to preferentially remember those states which are found to be useful to recall later on. Critically this method allows for efficient online computation of gradient estimates with respect to the write process of the external memory. Thus unlike most prior mechanisms for external memory it is feasible to use in an online reinforcement learning setting.

Memoria esterna per reinforcement learning online basato sulla stima dei gradienti su una nuova tecnica di reservoir sampling.

L'articolo propone un approccio modificato alla RL, dove una "memoria episodica" aggiuntiva è mantenuta dall'agente e usa una "query network" che si basa sullo stato attuale.


We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. Our decomposition leads to an interesting phenomenon that the variance does not necessarily increase when more parameters are included in Boltzmann machines, while the bias always decreases. Our result gives a theoretical evidence of the generalization ability of deep learning architectures because it provides the possibility of increasing the representation power with avoiding the variance inflation.

Deduciamo la decomposizione bias-varianza per le macchine di Boltzmann usando una formulazione geometrica dell'informazione.

L'obiettivo di questo articolo è analizzare l'efficacia e la generalizzabilità del deep learning presentando un'analisi teorica della decomposizione bias-varianza per i modelli gerarchici, in particolare le macchine di Boltzmann

L'articolo arriva alla conclusione principale che è possibile ridurre sia il bias sia la varianza in un modello gerarchico.


Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network.   Following recent work in simplifying these networks with model pruning and a novel mapping of work onto GPUs, we design an efficient implementation for sparse RNNs.   We investigate several optimizations and tradeoffs: Lamport timestamps, wide memory loads, and a bank-aware weight layout.   With these optimizations, we achieve speedups of over 6x over the next best algorithm for a hidden layer of size 2304, batch size of 4, and a density of 30%.   Further, our technique allows for models of over 5x the size to fit on a GPU for a speedup of 2x, enabling larger networks to help advance the state-of-the-art.   We perform case studies on NMT and speech recognition tasks in the appendix, accelerating their recurrent layers by up to 3x.

Combinando il pruning della rete e i kernel persistenti in un'implementazione della rete pratica, veloce e accurata.

Questo articolo introduce le RNN sparse e persistenti, un meccanismo per aggiungere il pruning al lavoro esistente di memorizzazione dei pesi delle RNN su un chip.


Weight pruning has proven to be an effective method in reducing the model size and computation cost while not sacrificing the model accuracy. Conventional sparse matrix formats, however, involve irregular index structures with large storage requirement and sequential reconstruction process, resulting in inefficient use of highly parallel computing resources. Hence, pruning is usually restricted to inference with a batch size of one, for which an efficient parallel matrix-vector multiplication method exists. In this paper, a new class of sparse matrix representation utilizing Viterbi algorithm that has a high, and more importantly, fixed index compression ratio regardless of the pruning rate, is proposed. In this approach, numerous sparse matrix candidates are first generated by the Viterbi encoder, and then the one that aims to minimize the model accuracy degradation is selected by the Viterbi algorithm. The model pruning process based on the proposed Viterbi encoder and Viterbi algorithm is highly parallelizable, and can be implemented efficiently in hardware to achieve low-energy, high-performance index decoding process. Compared with the existing magnitude-based pruning methods, index data storage requirement can be further compressed by 85.2% in MNIST and 83.9% in AlexNet while achieving similar pruning rate. Even compared with the relative index compression technique, our method can still reduce the index storage requirement by 52.7% in MNIST and 35.5% in AlexNet.

Presentiamo un nuovo metodo di pruning e un formato di matrice sparsa per permettere un alto rapporto di compressione dell'indice e un processo parallelo di decodifica dello stesso.

Gli autori usano la codifica Viterbi per comprimere drasticamente l'indice della matrice sparsa di una rete pruned, riducendo uno dei principali sovraccarichi di memoria e accelerando l'inferenza nel setting parallelo.


Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.

Una nuova hierarchical policy network che può riutilizzare le skill precedentemente apprese insieme e come sottocomponenti di nuove skill, scoprendo le relazioni sottostanti tra le skill.

Questo articolo mira ad apprendere policy gerarchiche utilizzando una struttura di policy ricorsiva regolata da una grammatica temporale stocastica

Questo articolo propone un approccio all'apprendimento delle policy gerarchiche in un contesto di lifelong learning, impilando le policy e poi usando una policy esplicita di "switch".


Embeddings are a fundamental component of many modern machine learning and natural language processing models.  Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models.  State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging.  In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology.  This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces.  We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.

Proponiamo di usare la proiezione esplicita dalle formule algebriche vettoriali come un modo alternativo per visualizzare gli spazi di embedding che è specificamente adatto ai task di analisi goal-oriented e che supera t-SNE nel nostro user study.

Analisi degli spazi di embedding in modo non parametrico (basato su esempi)


Learning deep networks which can resist large variations between training andtesting data is essential to build accurate and robust image classifiers.   Towardsthis end, a typical strategy is to apply data augmentation to enlarge the trainingset.    However,  standard  data  augmentation  is  essentially  a  brute-force  strategywhich is inefficient,  as it performs all the pre-defined transformations  to everytraining sample. In this paper, we propose a principled approach to train networkswith  significantly  improved  resistance  to  large  variations  between  training  andtesting data.   This is achieved by embedding a learnable transformation moduleinto the introspective networks (Jin et al., 2017; Lazarow et al., 2017; Lee et al.,2018), which is a convolutional neural network (CNN) classifier empowered withgenerative capabilities.   Our approach alternatively synthesizes pseudo-negativesamples with learned transformations and enhances the classifier by retraining itwith synthesized samples.   Experimental results verify that our approach signif-icantly improves the ability of deep networks to resist large variations betweentraining and testing data and achieves classification accuracy improvements onseveral benchmark datasets, including MNIST, affNIST, SVHN and CIFAR-10.

Proponiamo un approccio che conferisce ai classificatori la capacità di resistere a  variazioni più grandi tra i dati di training e di test in modo intelligente ed efficiente.

Usiamo l'introspective learning per gestire le variazioni dei dati al momento del test

Questo articolo suggerisce l'uso di transformation network apprese, incorporate in introspective network per migliorare le prestazioni di classificazione con esempi sintetizzati.


It is well known that it is possible to construct "adversarial examples"  for neural networks: inputs which are misclassified by the network  yet indistinguishable from true data. We propose a simple  modification to standard neural network architectures, thermometer  encoding, which significantly increases the robustness of the network to  adversarial examples. We demonstrate this robustness with experiments  on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that  models with thermometer-encoded inputs consistently have higher accuracy  on adversarial examples, without decreasing generalization.  State-of-the-art accuracy under the strongest known white-box attack was   increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.  We explore the properties of these networks, providing evidence  that thermometer encodings help neural networks to  find more-non-linear decision boundaries.

La discretizzazione dell'input porta alla robustezza contro gli adversarial example

Gli autori presentano uno studio approfondito sulla discretizzazione/quantizzazione dell'input come difesa contro gli adversarial example


Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models.  Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates.  These bounds tend to depend on the dimension of the model $d$ in that the number of bits needed to achieve a particular error bound increases as $d$ increases.  This is undesirable because a motivating application for low-precision training is large-scale models, such as deep learning, where $d$ can be huge.  In this paper, we prove dimension-independent bounds for low-precision training algorithms that use fixed-point arithmetic, which lets us better understand what affects the convergence of these algorithms as parameters scale.  Our methods also generalize naturally to let us prove new convergence bounds on low-precision training with other quantization schemes, such as low-precision floating-point computation and logarithmic quantization.

Abbiamo dimostrato bound indipendenti dalla dimensione per algoritmi di training a bassa precisione

Questo articolo discute le condizioni in cui la convergenza dei modelli nel training con pesi di bassa precisione non dipende dalla dimensione del modello.


We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World'  and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where exploration is important.

Modifiche a MAML e RL2 che dovrebbero permettere una migliore esplorazione.

L'articolo propone un trucco per estendere le funzioni obiettivo per guidare l'esplorazione in meta-RL in cima a due recenti algoritmi meta-RL


We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs. The key idea of our approach is to learn a rich latent space which effectively propagates program annotations from known questions to novel questions. We do this by formalizing prior work on VQA, called module networks (Andreas, 2016) as discrete, structured, latent variable models on the joint distribution over questions and answers given images, and devise a procedure to train the model effectively. Our results on a dataset of compositional questions about SHAPES (Andreas, 2016) show that our model generates more interpretable programs and obtains better accuracy on VQA in the low-data regime than prior work.

Un modello simbolico neurale probabilistico con uno spazio di programma latente, per una question answering più interpretabile

Questo articolo propone un modello a variabili latenti discreto e strutturato per  visual question answering che coinvolge la generalizzazione e il ragionamento compositivo con un guadagno significativo in termini di prestazioni e capacità.


The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and each time stronger attacks have been devised, demonstrating the shortcomings of existing defenses. This highlights a key difficulty in designing an effective defense: the inability to assess a network's robustness against future attacks. We propose to address this difficulty through formal verification techniques. We construct ground truths: adversarial examples with a provably-minimal distance from a given input point. We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement. We use this technique to assess recently suggested attack and defense techniques.

Usiamo la verifica formale per valutare l'efficacia delle tecniche per trovare o difendersi dagli adversarial example.

Questo articolo propone un metodo per calcolare adversarial example con la minima distanza dagli input originali.

Gli autori propongono di impiegare esempi a distanza minima come strumento per valutare la robustezza di una rete addestrata.

L'articolo descrive un metodo per generare adversarial example che hanno una distanza minima dal training example usato per generarli


This paper introduces a new framework for open-domain question answering in which the retriever and the reader \emph{iteratively interact} with each other. The framework is agnostic to the architecture of the machine reading model provided it has \emph{access} to the token-level hidden representations of the reader. The retriever uses fast nearest neighbor search that allows it to scale to corpora containing millions of paragraphs. A gated recurrent unit updates the query at each step conditioned on the \emph{state} of the reader and the \emph{reformulated} query is used to re-rank the paragraphs by the retriever. We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus. Finally, we show that our multi-step-reasoning framework brings consistent improvement when applied to two widely used reader architectures (\drqa and \bidaf) on various large open-domain datasets ---\tqau, \quasart, \searchqa, and \squado\footnote{Code and pretrained models are available at \url{https://github.com/rajarshd/Multi-Step-Reasoning}}.

Paragraph retriever e machine reader interagiscono l'uno con l'altro attraverso reinforcement learning per produrre grandi miglioramenti su dataset open domain

L'articolo introduce un nuovo framework di interazione bidirezionale tra il paragraph retriever e il machine reader per open-domain question answering con l'idea di "reader state" dal reader al retriever.

L'articolo propone un modello di multi-document extractive machine reading composto da 3 parti distinte e un algoritmo.


Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper net- work architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.

Presenta una nuova architettura che sfrutta il potere di globalizzazione dell'informazione delle u-net in una rete più profonda ed esegue bene tutti i task.

Un'architettura di rete per la segmentazione semantica delle immagini, basata sulla composizione di una pila di architetture U-Net di base, che riduce il numero di parametri e migliora i risultati.

Questo paper propone un'architettura U-Net impilata per la segmentazione delle immagini.


Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.

Proponiamo una rete neurale che è in grado di generare domande specifiche per argomento.

Presenta un approccio basato su reti neurali per generare domande specifiche per argomento con la motivazione che le domande relative ad un argomento sono più significative nelle applicazioni pratiche.

Propone un metodo di generazione basato su argomenti utilizzando una LSTM per estrarre gli argomenti utilizzando una tecnica di codifica a due stadi


Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option  to restore voluntary movements after paralysis. These devices are based on the  ability to extract information about movement intent from neural signals recorded  using multi-electrode arrays chronically implanted in the motor cortices of the  brain. However, the inherent loss and turnover of recorded neurons requires repeated  recalibrations of the interface, which can potentially alter the day-to-day  user experience. The resulting need for continued user adaptation interferes with  the natural, subconscious use of the BMI. Here, we introduce a new computational  approach that decodes movement intent from a low-dimensional latent representation  of the neural data. We implement various domain adaptation methods  to stabilize the interface over significantly long times. This includes Canonical  Correlation Analysis used to align the latent variables across days; this method  requires prior point-to-point correspondence of the time series across domains.  Alternatively, we match the empirical probability distributions of the latent variables  across days through the minimization of their Kullback-Leibler divergence.  These two methods provide a significant and comparable improvement in the performance  of the interface. However, implementation of an Adversarial Domain  Adaptation Network trained to match the empirical probability distribution of the  residuals of the reconstructed neural signals outperforms the two methods based  on latent variables, while requiring remarkably few data points to solve the domain  adaptation problem.

Implementiamo una rete di adversarial domain adaptation per stabilizzare un'interfaccia Brain-Machine fissa contro i cambiamenti graduali nei segnali neurali registrati.

Descrive un nuovo approccio per l'interfaccia brain machine impiantata per affrontare il problema della calibrazione e il covariate shift.

Gli autori definiscono una BMI che utilizza un autoencoder e poi affrontano il problema del data drift nella BMI.


Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.

Analizzare e capire come gli agenti delle reti neurali imparano a capire un linguaggio semplice e grounded

Gli autori collegano i metodi sperimentali psicologici alla comprensione di come la scatola nera dei metodi di deep learning risolva i problemi.

Questo articolo presenta un'analisi degli agenti che imparano il linguaggio grounded attraverso reinforcement learning in un ambiente semplice che combina istruzioni verbali con informazioni visive


Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.

Imparare le parti degli oggetti, la struttura gerarchica e le dinamiche guardando come si muovono

Propone un modello di apprendimento unsupervised che impara a separare gli oggetti in parti, prevedere la struttura gerarchica delle parti e, sulla base delle parti separate e della gerarchia, prevedere il movimento.


A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR). Naturally, SR is of value to resource constrained devices like mobile phones, electronic photograph frames and televisions to enhance image quality. However, SR demands perhaps the most extreme amounts of memory and compute operations of any mainstream vision task known today, preventing SR from being deployed to devices that require them. In this paper, we perform a early systematic study of system resource efficiency for SR, within the context of a variety of architectural and low-precision approaches originally developed for discriminative neural networks. We present a rich set of insights, representative SR architectures, and efficiency trade-offs; for example, the prioritization of ways to compress models to reach a specific memory and computation target and techniques to compact SR models so that they are suitable for DSPs and FPGAs. As a result of doing so, we manage to achieve better and comparable performance with previous models in the existing literature, highlighting the practicality of using existing efficiency techniques in SR tasks. Collectively, we believe these results provides the foundation for further research into the little explored area of resource efficiency for SR.

Cerchiamo di comprendere le tecniche efficienti in termini di risorse per la Super-Risoluzione

L'articolo propone una valutazione empirica dettagliata dei compromessi raggiunti da varie reti neurali convoluzionali sul problema della super risoluzione.

Questo articolo ha proposto di migliorare l'efficienza delle risorse del sistema per le reti di super risoluzione.


Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.

Studiamo le reti ReLU nel dominio di Fourier e dimostriamo un comportamento particolare.

Analisi di Fourier delle reti ReLU, trovando che sono biased verso l'apprendimento a bassa frequenza

Questo articolo ha contributi teorici ed empirici sul tema dei coefficienti di Fourier delle reti neurali


Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty which can arise when the input is ambiguous, e.g., due to occlusion or blurriness. This work addresses this issue and explicitly models the uncertainty by “hedging” the location of each input in the embedding space. We introduce the hedged instance embedding (HIB) in which embeddings are modeled as random variables and the model is trained under the variational information bottleneck principle (Alemi et al., 2016; Achille & Soatto, 2018). Empirical results on our new N-digit MNIST dataset show that our method leads to the desired behavior of “hedging its bets” across the embedding space upon encountering ambiguous inputs. This results in improved performance for image matching and classification tasks, more structure in the learned embedding space, and an ability to compute a per-exemplar uncertainty measure which is correlated with downstream performance.

L'articolo propone di usare distribuzioni di probabilità invece dei punti per task di embedding di istanze come il riconoscimento e la verifica.

Il paper propone un'alternativa all'attuale embedding di punti e una tecnica per addestrarli.

L'articolo propone un modello che utilizza gli embedding di incertezze per estendere il deep learning alle applicazioni bayesiane


Convolution neural networks typically consist of many convolutional layers followed by several fully-connected layers.   While convolutional layers map between high-order activation tensors, the fully-connected layers operate on flattened activation vectors.   Despite its success, this approach has notable drawbacks. Flattening discards the multi-dimensional structure of the activations, and the fully-connected layers require a large number of parameters.   We present two new techniques to address these problems.   First, we introduce tensor contraction layers which can replace the ordinary fully-connected layers in a neural network. Second, we introduce tensor regression layers, which express the output of a neural network as a low-rank multi-linear mapping from a high-order activation tensor to the softmax layer.   Both the contraction and regression weights are learned end-to-end by backpropagation. By imposing low rank on both, we use significantly fewer parameters.   Experiments on the ImageNet dataset show that applied to the popular VGG and ResNet architectures, our methods significantly reduce the number of parameters in the fully connected layers (about 65% space savings) while negligibly impacting accuracy.

Proponiamo i layer di contrazione e di regressione dei tensori a basso rango per preservare e sfruttare la struttura multi-lineare in tutta la rete, ottenendo un enorme risparmio di spazio con poco o nessun impatto sulle prestazioni.

Questo articolo propone nuove architetture di layer di reti neurali utilizzando una rappresentazione a basso rango di tensori

Questo articolo incorpora la decomposizione e la regressione tensoriale nelle CNN utilizzando un nuovo layer di regressione tensoriale.


We explore ways of incorporating bilingual dictionaries to enable semi-supervised  neural machine translation. Conventional back-translation methods have shown  success in leveraging target side monolingual data. However, since the quality of  back-translation models is tied to the size of the available parallel corpora, this  could adversely impact the synthetically generated sentences in a low resource  setting. We propose a simple data augmentation technique to address both this  shortcoming. We incorporate widely available bilingual dictionaries that yield  word-by-word translations to generate synthetic sentences. This automatically  expands the vocabulary of the model while maintaining high quality content. Our  method shows an appreciable improvement in performance over strong baselines.

Usiamo i dizionari bilingui per data augmentation per la traduzione automatica neurale

Questo articolo studia l'uso di dizionari bilingui per creare fonti sintetiche per i dati monolinguali lato target per migliorare i modelli di NMT addestrati con piccole quantità di dati paralleli.


Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. The code is available at https://github.com/google-research/episodic-curiosity/.

Proponiamo un nuovo modello di curiosity basato sulla memoria episodica e le idee di raggiungibilità che ci permette di superare i noti problemi di "couch-potato" dei lavori precedenti.

Propone di dare bonus di esplorazione negli algoritmi RL dando bonus più grandi alle osservazioni che sono più lontane in termini di passi nell'ambiente.

Gli autori propongono un bonus di esplorazione che ha lo scopo di aiutare nei problemi RL a reward sparsa e considerano molti esperimenti su ambienti 3D complessi


We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class---PossibleWorldNets---which computes entailment as a ``convolution over possible worlds''. Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks.

Introduciamo un nuovo dataset di implicazioni logiche allo scopo di misurare la capacità dei modelli di catturare e sfruttare la struttura delle espressioni logiche in un task di entailment prediction.

L'articolo propone un nuovo modello per utilizzare deep model per rilevare l'implicazione logica come prodotto di funzioni continue su mondi possibili.

Propone un nuovo modello progettato per machine learning con logical entailment prediction.


Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification.   However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing a DCNN to allow new classes to be learned while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned classes. An updated network for learning new set of classes is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing).

L'articolo riguarda una nuova metodologia ad alta efficienza energetica per incremental learning

Propone una procedura per l'incremental learning come transfer learning.

L'articolo presenta un metodo per addestrare deep convolutional neural network in modo incrementale, in cui i dati sono disponibili in piccole batch per un periodo di tempo.

Presenta un approccio al class-incremental learning utilizzando deep network, proponendo tre diverse strategie di apprendimento nell'approccio finale/migliore.


Recurrent neural networks (RNNs) are widely used to model sequential data but  their non-linear dependencies between sequence elements prevent parallelizing  training over sequence length. We show the training of RNNs with only linear  sequential dependencies can be parallelized over the sequence length using the  parallel scan algorithm, leading to rapid training on long sequences even with  small minibatch size. We develop a parallel linear recurrence CUDA kernel and  show that it can be applied to immediately speed up training and inference of  several state of the art RNN architectures by up to 9x.  We abstract recent work  on linear RNNs into a new framework of linear surrogate RNNs and develop a  linear surrogate model for the long short-term memory unit, the GILR-LSTM, that  utilizes parallel linear recurrence.  We extend sequence learning to new  extremely long sequence regimes that were previously out of reach by  successfully training a GILR-LSTM on a synthetic sequence classification task  with a one million timestep dependency.

Utilizzare la scansione parallela per parallelizzare le reti neurali ricorrenti lineari. Si addestra il modello sulla lunghezza di 1 milione di dipendenze

Propone di accelerare le RNN applicando il metodo di Blelloch.

Gli autori propongono un algoritmo parallelo per le Linear Surrogate RNN, che produce uno speedup rispetto alle implementazioni esistenti di Quasi-RNN, SRU e LSTM.


Neural text generation models are often autoregressive language models or seq2seq models. Neural autoregressive and seq2seq models that generate text by sampling words sequentially, with each word conditioned on the previous model, are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of sample quality. Language models are typically trained via maximum likelihood and most often with teacher forcing. Teacher forcing is well-suited to optimizing perplexity but can result in poor sample quality because generating text requires conditioning on sequences of words that were never observed at training time. We propose to improve sample quality using Generative Adversarial Network (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally to designed to output differentiable values, so discrete language generation is challenging for them. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic text samples compared to a maximum likelihood trained model.

GAN per il linguaggio naturale per il riempimento dello spazio vuoto

Questo articolo propone di generare testo usando le GAN.

Generazione di sample di testo utilizzando le GAN e un meccanismo per riempire le parole mancanti condizionato dal testo circostante


Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. For example, in case of inspecting decorative surfaces the de- tection of visible texture anomalies without any prior knowledge is required. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture percep- tion, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Additionally, we introduce a novel objective function to train one-class neural networks for novelty detection and compare the results to standard one-class SVM approaches. Our experiments clearly show the differences between human-vision-inspired texture representations and learnt features in detecting visual anomalies. Based on a dig- ital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features.

Confronto tra le rappresentazioni delle texture psicofisiche e quelle codificate dalla CNN in un'applicazione di novelty detection con rete neurale ad una classe.

Questo articolo si concentra su novelty detection e mostra che le rappresentazioni psicofisiche possono superare le feature del VGG-encoder in una parte di questo task

Questo articolo considera il rilevamento delle anomalie nelle texture e propone una loss originale.

Propone il training di due rilevatori di anomalie da tre modelli diversi per rilevare anomalie percettive nelle texture visive.


In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regularization approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regularized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.

Proponiamo un nuovo tipo di approccio di regolarizzazione che incoraggia la non sovrapposizione nel representation learning, al fine di migliorare l'interpretabilità e ridurre l'overfitting.

L'articolo introduce un regolarizzatore di matrice per indurre simultaneamente sia la sparsità che l'ortogonalità approssimativa.

L'articolo studia un metodo di regolarizzazione per promuovere la sparsità e ridurre la sovrapposizione tra i supporti dei vettori dei pesi nelle rappresentazioni apprese per migliorare l'interpretabilità ed evitare l'overfitting

L'articolo ha proposto un nuovo approccio di regolarizzazione che incoraggia simultaneamente i vettori dei pesi (W) ad essere sparsi e ortogonali tra loro.


Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use \emph{ad hoc} gating mechanisms.   Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. 	  We prove that learnable gates in a recurrent model formally provide \emph{quasi-invariance to general time transformations} in the input data. We recover part of the LSTM architecture from a simple axiomatic approach. 	  This result leads to a new way of initializing gate biases in LSTMs and GRUs. Experimentally, this new \emph{chrono initialization} is shown to greatly improve learning of long term dependencies, with minimal implementation effort.

Dimostra che i meccanismi di gating forniscono invarianza alle trasformazioni temporali. Introduce e testa una nuova inizializzazione per le LSTM da questa intuizione.

Il paper collega il design delle reti ricorrenti e il suo effetto su come la rete reagisce alle trasformazioni del tempo, e usa questo per sviluppare un semplice schema di inizializzazione dei bias.


Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach ``learning to teach''. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).

Proponiamo e verifichiamo l'efficacia del "learning to teach", un nuovo framework per guidare automaticamente il processo di apprendimento automatico.

Questo articolo si concentra sul "machine teaching" e propone di sfruttare reinforcement learning definendo la reward come la velocità di apprendimento dell'allievo e usando il policy gradient per aggiornare i parametri dell'insegnante

Gli autori definiscono un modello di deep learning composto da quattro componenti: un modello di studente, un modello di insegnante, una loss e un dataset.

Suggerisce un framework di "learning to teach", corrispondente alle scelte sui dati presentati al learner.


We present DL2, a system for training and querying neural networks with logical constraints. The key idea is to translate these constraints into a differentiable loss with desirable mathematical properties and to then either train with this loss in an iterative manner or to use the loss for querying the network for inputs subject to the constraints. We empirically demonstrate that DL2 is effective in both training and querying scenarios, across a range of constraints and data sets.

Una loss differenziabile per i vincoli logici per il training e il querying delle reti neurali.

Un framework per trasformare il querying sui parametri e coppie di input-ouput alle reti neurali in funzioni di loss differenziabili e un linguaggio dichiarativo associato per specificare queste query

Questo articolo affronta il problema di combinare approcci logici con reti neurali traducendo una formula logica in una loss non negativa per una rete neurale.


Genetic algorithms have been widely used in many practical optimization problems.  Inspired by natural selection, operators, including mutation, crossover  and selection, provide effective heuristics for search and black-box optimization.  However, they have not been shown useful for deep reinforcement learning, possibly  due to the catastrophic consequence of parameter crossovers of neural networks.  Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm  for sample-efficient deep policy optimization. GPO uses imitation learning  for policy crossover in the state space and applies policy gradient methods for mutation.  Our experiments on MuJoCo tasks show that GPO as a genetic algorithm  is able to provide superior performance over the state-of-the-art policy gradient  methods and achieves comparable or higher sample efficiency.

Approccio basato su algoritmi genetici per l'ottimizzazione delle policy delle deep neural network

Gli autori presentano un algoritmo per il training di ensemble di policy network che mescola regolarmente diverse policy nell'ensemble.

Questo articolo propone un metodo di ottimizzazione delle policy ispirato agli algoritmi genetici, che imita gli operatori di mutazione e crossover sulle policy network.


To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works.  Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. We further perform theoretical analyses showing that ProxQuant converges to stationary points under mild smoothness assumptions, whereas variants such as lazy prox-gradient method can fail to converge in the same setting.

Un framework per la quantizzazione del modello usando il metodo del gradiente prossimale, con valutazione empirica e analisi teorica della convergenza.

Propone il metodo ProxQuant per addestrare reti neurali con pesi quantizzati.

Propone di risolvere le reti binarie e le sue varianti usando la proximal gradient descent.


Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model or training method, strong assumptions on the labels (e.g., “near” linear separability), or an unrealistically wide hidden layer with \Omega\(N) units.    Results: We examine a MNN with one hidden layer of piecewise linear units, a single output, and a quadratic loss. We prove that, with high probability in the limit of N\rightarrow\infty datapoints, the volume of differentiable regions of the empiric loss containing sub-optimal differentiable local minima is exponentially vanishing in comparison with the same volume of global minima, given standard normal input of dimension d_0=\tilde{\Omega}(\sqrt{N}), and a more realistic number of d_1=\tilde{\Omega}(N/d_0) hidden units. We demonstrate our results numerically: for example, 0% binary classification training error on CIFAR with only N/d_0 = 16 hidden neurons.

I minimi locali "cattivi" svaniscono in una rete neurale multilayer: una prova con ipotesi più ragionevoli di prima

Nelle reti con un solo hidden layer, il volume dei minimi locali subottimali diminuisce esponenzialmente rispetto ai minimi globali.

Questo articolo mira a rispondere al perché gli algoritmi standard basati su SGD su rete neurale convergono a soluzioni "buone".


Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness, and several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we identify the attention shift phenomenon, which may hinder the transferability of adversarial examples to the defense models. It indicates that the defenses rely on different discriminative regions to make predictions compared with normally trained models. Therefore, we propose an attention-invariant attack method to generate more transferable adversarial examples. Extensive experiments on the ImageNet dataset validate the effectiveness of the proposed method. Our best attack fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the defense techniques.

Proponiamo un metodo di attacco invariante all'attention per generare adversarial example più trasferibili per gli attacchi black-box, che possono ingannare le difese allo stato dell'arte con un alto tasso di successo.

L'articolo propone un nuovo modo di superare lo stato dell'arte delle difese contro gli adversarial attack alle CNN.

Questo articolo suggerisce che l'attention shift è una proprietà chiave dietro il fallimento degli adversarial attack da trasferire e propone un metodo di attacco invariante all'attention


We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\log{K})$ memory while only requiring $O(K\log{K} + d\log{K})$ operation for inference. MACH is the first generic $K$-classification algorithm, with provably theoretical guarantees, which requires $O(\log{K})$ memory without any assumption on the relationship between classes. MACH uses universal hashing to reduce classification with a large number of classes to few independent classification task with very small (constant) number of classes. We provide theoretical quantification of accuracy-memory tradeoff by showing the first connection between extreme classification and heavy hitters. With MACH we can train ODP dataset with 100,000 classes and 400,000 features on a single Titan X GPU (12GB), with the classification accuracy of 19.28\%, which is the best-reported accuracy on this dataset. Before this work, the best performing baseline is a one-vs-all classifier that requires 40 billion parameters (320 GB model size) and achieves 9\% accuracy.   In contrast, MACH can achieve 9\% accuracy with 480x reduction in the model size (of mere 0.6GB). With MACH, we also demonstrate complete training of fine-grained imagenet dataset (compressed size 104GB), with 21,000 classes, on a single GPU.

Come allenare 100.000 classi su una singola GPU

Propone un efficiente metodo di hashing MACH per l'approssimazione di softmax nel contesto di un grande spazio di output, che risparmia sia memoria che computazione.

Un metodo per lo schema di classificazione per problemi che coinvolgono un gran numero di classi in un ambiente multiclasse dimostrato sui dataset ODP e Imagenet-21K

L'articolo presenta uno schema basato sull'hashing per ridurre la memoria e il tempo di calcolo per la classificazione K-way quando K è grande


Gradient-based optimization is the foundation of deep learning and reinforcement learning.  Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables, based on gradients of a learned function.  These estimators can be jointly trained with model parameters or policies, and are applicable in both discrete and continuous settings. We give unbiased, adaptive analogs of state-of-the-art reinforcement learning methods such as advantage actor-critic. We also demonstrate this framework for training discrete latent-variable models.

Presentiamo un metodo generale per la stima unbiased dei gradienti di funzioni black-box di variabili casuali. Applichiamo questo metodo all'inferenza variazionale discreta e al reinforcement learning.

Suggerisce un nuovo approccio per eseguire la gradient descent per l'ottimizzazione blackbox o il training di modelli discreti a variabili latenti.


Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al. (2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al. (2017) raised doubts whether the same holds when discriminator has bounded size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support. In other words, the training objective is unable to prevent mode collapse. The current paper makes two contributions. (1) It proposes a novel test for estimating support size using the birthday paradox of discrete probability. Using this  evidence is presented that well-known GANs approaches do learn distributions of fairly low support.   (2) It theoretically studies encoder-decoder GANs architectures (e.g., BiGAN/ALI), which were proposed to learn more meaningful features via GANs, and consequently to also solve the mode-collapse issue. Our result shows that such encoder-decoder training objectives also cannot guarantee learning of the full distribution because they cannot prevent serious mode collapse. More seriously, they cannot prevent learning meaningless codes for data, contrary to usual intuition.

Proponiamo uno stimatore della dimensione del supporto della distribuzione appresa dalle GAN per dimostrare che soffrono davvero di mode collapse, e dimostriamo che anche le GAN encoder-decoder non evitano il problema.

L'articolo tenta di stimare sperimentalmente la dimensione del supporto delle soluzioni prodotte dalle GAN standard.

Questo articolo propone un nuovo test intelligente basato sul birthday paradox  per misurare la diversità nel sample generato, con i risultati sperimentali che indicano che il mode collapse è forte in un certo numero di modelli generativi allo stato dell'arte.

L'articolo usa il birthday paradox per mostrare che alcune architetture GAN generano distribuzioni con un supporto abbastanza basso.


Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

Proponiamo un framework per generare avversari naturali contro i classificatori black-box sia per domini visivi che testuali, facendo la ricerca di avversari nello spazio semantico latente.

Suggerisce un metodo per la creazione di adversarial example semantici.

Propone un framework per generare adversarial example naturali cercando avversari in uno spazio latente di rappresentazione di dati densi e continui


Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017).   It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks.

Estendiamo il metodo K-FAC alle RNN sviluppando una nuova famiglia di approssimazioni di Fisher.

Gli autori estendono il metodo K-FAC alle RNN e presentano 3 modi di approssimare F, mostrando risultati di ottimizzazione su 3 dataset, che superano ADAM sia nel numero di aggiornamenti che nel tempo di calcolo.

Propone di estendere il metodo di ottimizzazione di Kronecker-factor Appropriate Curvature al setting delle reti neurali ricorrenti.

Gli autori presentano un metodo del secondo ordine che è specificamente progettato per le RNN


Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural networks.

Il modello generativo per i kernel delle reti neurali convoluzionali, che agisce come una distribuzione prior durante il training su nuovi dataset.

Un metodo per modellare le reti neurali convoluzionali usando un metodo di Bayes.

Propone il "deep weight prior": l'idea è quella di ottenere un prior su un dataset ausiliario e poi usare quel prior sui filtri CNN per avviare l'inferenza per un dataset di interesse.

Questo articolo esplora l'apprendimento di prior informativi per modelli di reti neurali convoluzionali con domini simili utilizzando autoencoder per ottenere un prior espressivo sui pesi filtrati delle reti addestrate.


The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements.   Motivated by the potential for an in-the-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures.   We use deep learning techniques to both successfully classify cells and generate a mask segmenting the cells/condition from the background. Further, we use the classification accuracy to guide a data-driven, iterative feature selection method, allowing the design neural networks requiring 90% fewer input features with little accuracy degradation.

Abbiamo applicato tecniche di deep learning alla segmentazione iperspettrale delle immagini e al sampling iterativo delle feature.

Propone uno schema greedy per selezionare un sottoinsieme di feature spettrali altamente correlate in un task di classificazione.

L'articolo esplora l'uso delle reti neurali per la classificazione e la segmentazione dell'imaging iperspettrale delle cellule.

Classificazione delle cellule e implementazione della segmentazione cellulare basata su tecniche di deep learning con riduzione delle feature di input


Data Interpretation is an important part of Quantitative Aptitude exams and requires an individual to answer questions grounded in plots such as bar charts, line graphs, scatter plots, \textit{etc}. Recently, there has been an increasing interest in building models which can perform this task by learning from datasets containing triplets of the form \{plot, question, answer\}. Two such datasets have been proposed in the recent past which contain plots generated from synthetic data with limited (i) $x-y$ axes variables (ii) question templates and (iii) answer vocabulary and hence do not adequately capture the challenges posed by this task. To overcome these limitations of existing datasets, we introduce a new dataset containing $9.7$ million question-answer pairs grounded over $270,000$ plots with three main differentiators. First, the plots in our dataset contain a wide variety of realistic $x$-$y$ variables such as CO2 emission, fertility rate, \textit{etc. } extracted from  real word data sources such as World Bank, government sites, \textit{etc}. Second, the questions in our dataset are more complex as they are based on templates extracted from interesting questions asked by a crowd of workers using a fraction of these plots. Lastly, the answers in our dataset are not restricted to a small vocabulary and a large fraction of the answers seen at test time are not present in the training vocabulary. As a result, existing models for Visual Question Answering which largely use end-to-end models in a multi-class classification framework cannot be used for this task. We establish initial results on this dataset and emphasize the complexity of the task using a multi-staged modular pipeline with various sub-components to (i) extract relevant data from the plot and convert it to a semi-structured table (ii) combine the question with this table and use compositional semantic parsing to arrive at a logical form from which the answer can be derived. We believe that such a modular framework is the best way to go forward as it would enable the research community to independently make progress on all the sub-tasks involved in plot question answering.

Abbiamo creato un nuovo dataset per l'interpretazione dei dati su grafico e proponiamo anche una baseline per lo stesso.

Gli autori propongono una pipeline per risolvere il problema DIP che comporta l'apprendimento da dataset contenenti terzine della forma {grafico, domanda, risposta}

Propone un algoritmo che può interpretare i dati mostrati nei grafici scientifici.


Learning to predict complex time-series data is a fundamental challenge in a range of disciplines including Machine Learning, Robotics, and Natural Language Processing. Predictive State Recurrent Neural Networks (PSRNNs) (Downey et al.) are a state-of-the-art approach for modeling time-series data which combine the benefits of probabilistic filters and Recurrent Neural Networks into a single model. PSRNNs leverage the concept of Hilbert Space Embeddings of distributions (Smola et al.) to embed predictive states into a Reproducing Kernel Hilbert Space, then estimate, predict, and update these embedded states using Kernel Bayes Rule. Practical implementations of PSRNNs are made possible by the machinery of Random Features, where input features are mapped into a new space where dot products approximate the kernel well. Unfortunately PSRNNs often require a large number of RFs to obtain good results, resulting in large models which are slow to execute and slow to train. Orthogonal Random Features (ORFs) (Choromanski et al.) is an improvement on RFs which has been shown to decrease the number of RFs required for pointwise kernel approximation. Unfortunately, it is not clear that ORFs can be applied to PSRNNs, as PSRNNs rely on Kernel Ridge Regression as a core component of their learning algorithm, and the theoretical guarantees of ORF do not apply in this setting. In this paper, we extend the theory of ORFs to Kernel Ridge Regression and show that ORFs can be used to obtain Orthogonal PSRNNs (OPSRNNs), which are smaller and faster than PSRNNs. In particular, we show that OPSRNN models clearly outperform LSTMs and furthermore, can achieve accuracy similar to PSRNNs with an order of magnitude smaller number of features needed.

Migliorare le reti neurali ricorrenti predictive state tramite feature casuali ortogonali

Propone di migliorare le prestazioni delle reti neurali ricorrenti predictive state considerando le feature casuali ortogonali.

L'articolo affronta il problema del training di reti neurali ricorrenti predictive state e apporta due contributi.


Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality- versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose “fidelity-weighted learning” (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.

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

Questo articolo suggerisce un approccio per l'apprendimento con supervisione debole utilizzando un dataset pulito e uno rumoroso e assumendo un teacher e una rete di student

L'articolo tenta di addestrare modelli di deep neural network con pochi sample di training annotati.

Gli autori propongono un approccio per il training di modelli di deep learning per situazioni in cui non ci sono abbastanza dati annotati affidabili.


Online learning has attracted great attention due to the increasing demand for systems that have the ability of learning and evolving. When the data to be processed is also high dimensional and dimension reduction is necessary for visualization or prediction enhancement, online dimension reduction will play an essential role. The purpose of this paper is to propose new online learning approaches for supervised dimension reduction. Our first algorithm is motivated by adapting the sliced inverse regression (SIR), a pioneer and effective algorithm for supervised dimension reduction, and making it implementable in an incremental manner. The new algorithm, called incremental sliced inverse regression (ISIR), is able to update the subspace of significant factors with intrinsic lower dimensionality fast and efficiently when new observations come in. We also refine the algorithm by using an overlapping technique  and develop an incremental overlapping sliced inverse regression (IOSIR) algorithm. We verify the effectiveness and efficiency of both algorithms by simulations and real data applications.

Abbiamo proposto due nuovi approcci, la regressione inversa incrementale sliced e la regressione inversa incrementale sliced e overlapping, per implementare la dimension reduction supervisionata in un setting di online learning.

Studia il problema della dimension reduction sufficiente e propone un algoritmo di regressione inversa incrementale sliced.

Questo articolo propone un algoritmo di online learning per dimension reduction supervisionata, chiamato regressione inversa incrementale sliced


This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip, achieving better classification accuracy. Such efficient use of on-chip storage reduces off-chip storage accesses, improving energy-efficiency and speed of training. We verified the proposed training model with deep and convolutional neural network classifiers on the MNIST and voice activity detection benchmarks. For the deep neural network, our model achieves data storage requirement of as low as 2 bits/weight, whereas the conventional binary neural network learning models require data storage of 8 to 32 bits/weight. With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model. To achieve the similar classification error, the conventional binary neural network model requires 4× more data storage for weights than our proposed model. For the convolution neural network classifier, the proposed model achieves 2.4% less test error for the same on-chip storage or 6× storage savings to achieve the similar accuracy.

Proponiamo un modello di apprendimento che permette alla DNN di imparare con solo 2 bit/peso, che è particolarmente utile per l'apprendimento on-device

Propone un metodo per discretizzare una NN in modo incrementale per migliorare la memoria e le prestazioni.


Within-class variation in a high-dimensional dataset can be modeled as being on a low-dimensional manifold due to the constraints of the physical processes producing that variation (e.g., translation, illumination, etc.). We desire a method for learning a representation of the manifolds induced by identity-preserving transformations that can be used to increase robustness, reduce the training burden, and encourage interpretability in machine learning tasks. In particular, what is needed is a representation of the transformation manifold that can robustly capture the shape of the manifold from the input data, generate new points on the manifold, and extend transformations outside of the training domain without significantly increasing the error. Previous work has proposed algorithms to efficiently learn analytic operators (called transport operators) that define the process of transporting one data point on a manifold to another.   The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data. The first method uses this representation in a novel randomized approach to transfer learning that employs the learned generative model to map out unseen regions of the data space. These results are shown through demonstrations of transfer learning in a data augmentation task for few-shot image classification. The second method use of transport operators for injecting specific transformations into new data examples which allows for realistic image animation and informed data augmentation.   These results are shown on stylized constructions using the classic swiss roll data structure and in demonstrations of transfer learning in a data augmentation task for few-shot image classification. We also propose the use of transport operators for injecting transformations into new data examples which allows for realistic image animation.

L'apprendimento di operatori di trasporto su manifold forma una rappresentazione preziosa per fare task come il transfer learning.

Utilizza una framework di dictionary learning per imparare operatori di trasporto su manifold su cifre USPS aumentate.

L'articolo considera il framework di apprendimento dell'operatore di trasporto su manifold di Culpepper e Olshausen (2009), e lo interpreta come se ottenesse una stima MAP sotto un modello generativo probabilistico.


The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts.

Presentiamo un modello neurale variazionale per l'apprendimento di concetti visivi compositivi guidati dal linguaggio.

Propone una nuova architettura di rete neurale che impara i concetti degli oggetti combinando un beta-VAE e SCAN.

Questo articolo introduce un modello basato su VAE per tradurre tra immagini e testo, con la loro rappresentazione latente ben adatta all'applicazione di operazioni simboliche, dando loro un linguaggio più espressivo per il sampling di immagini dal testo.

Questo articolo propone un nuovo modello chiamato SCAN (Symbol-Concept Association Network) per l'apprendimento gerarchico dei concetti e permette la generalizzazione a nuovi concetti composti da concetti esistenti usando operatori logici.


Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations. The neural topic component encodes information from the source sentence to build a global “topic” distribution over words, which is then consulted by the seq2seq model to improve generation at each time step. The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations. In a subjective human evaluation, the judges also confirm that LTCM is the preferred option comparing to competitive baseline models.

Latent Topic Conversational Model, un ibrido seq2seq e neural topic model per generare risposte più diverse e interessanti.

Questo articolo ha proposto la combinazione del topic model e del modello di conversazione seq2seq

Propone un modello di conversazione con informazioni sull'argomento combinando il modello seq2seq con neural topic model e mostra che il modello proposto supera alcuni modelli baseline seq2seq e altre varianti seq2seq basate su modelli a variabili latenti.

L'articolo affronta il problema di gestire gli argomenti nei modelli di conversazione e propone un modello che è una combinazione di un neural topic model e un sistema di dialogo basato su seq2seq.


Most of the existing Graph Neural Networks (GNNs) are the mere extension of the Convolutional Neural Networks (CNNs) to graphs. Generally, they consist of several steps of message passing between the nodes followed by a global indiscriminate feature pooling function. In many data-sets, however, the nodes are unlabeled or their labels provide no information about the similarity between the nodes and the locations of the nodes in the graph. Accordingly, message passing may not propagate helpful information throughout the graph. We show that this conventional approach can fail to learn to perform even simple graph classification tasks. We alleviate this serious shortcoming of the GNNs by making them a two step method. In the first of the proposed approach, a graph embedding algorithm is utilized to obtain a continuous feature vector for each node of the graph. The embedding algorithm represents the graph as a point-cloud in the embedding space. In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method. The GNN learns to perform the given task by inferring the topological structure of the graph encoded in the spatial distribution of the embedded vectors. In addition, we extend the proposed approach to the graph clustering problem and a new architecture for graph clustering is proposed. Moreover, the spatial representation of the graph is utilized to design a graph pooling algorithm. We turn the problem of graph down-sampling into a column sampling problem, i.e., the sampling algorithm selects a subset of the nodes whose feature vectors preserve the spatial distribution of all the feature vectors. We apply the proposed approach to several popular benchmark data-sets and it is shown that the proposed geometrical approach strongly improves the state-of-the-art result for several data-sets. For instance, for the PTC data-set, we improve the state-of-the-art result for more than 22 %.

Il problema dell'analisi dei grafi viene trasformato in un problema di analisi delle nuvole di punti.

Propone una deep GNN network per i problemi di classificazione dei grafi utilizzando il loro layer di pooling graph adaptive.

Gli autori propongono un metodo per l'apprendimento di rappresentazioni per i grafi


Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results.  Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts.   In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances.   For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses.    We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly.  Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack  has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

Proponiamo di generare un adversarial example basato su adversarial generative network in un setting semi-whitebox e black-box.

Descrive AdvGAN, una GAN condizionale con loss adversarial, e valuta AdvGAN su setting semi-white box e black box, riportando risultati allo stato dell'arte.

Questo articolo propone un modo di generare adversarial example che ingannano i sistemi di classificazione e vince la sfida mnist di MadryLab.


This paper proposes a new model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demonstrate that: a) deep autoencoder models generalize much better than the shallow ones, b) non-linear activation functions with negative parts are crucial for training deep models, and c) heavy use of regularization techniques such as dropout is necessary to prevent over-fitting. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering. The new algorithm significantly speeds up training and improves model performance. Our code is publicly available.

Questo articolo dimostra come addestrare deep autoencoder end-to-end per ottenere risultati SoA sul dataset Netflix time-split.

Questo articolo presenta un modello di deep autoencoder per la predizione del rating che supera altri approcci allo stato dell'arte sul dataset Netflix prize.

Propone di utilizzare un deep AE per svolgere task di predizione del rating nei recommender system.

Gli autori presentano un modello per raccomandazioni Netflix più accurate dimostrando che un deep autoencoder può superare i modelli più complessi basati su RNN che hanno informazioni temporali.


Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.

Introduciamo il transformer universale, un modello per sequenze ricorrente che è parallelo nel tempo e self-attentive e che supera i transformer e le LSTM su una vasta gamma di task sequence-to-sequence, compresa la traduzione automatica.

Propone un nuovo modello UT, basato sul modello Transformer, con aggiunta di ricorrenza e arresto dinamico della ricorrenza.

Questo documento estende Transformer applicando ricorsivamente un blocco di multi-head self-attention, piuttosto che impilare più blocchi come nel Transformer standard.


We present a framework for interpretable continual learning (ICL). We show that explanations of previously performed tasks can be used to improve performance on future tasks. ICL generates a good explanation of a finished task, then uses this to focus attention on what is important when facing a new task. The ICL idea is general and may be applied to many continual learning approaches. Here we focus on the variational continual learning framework to take advantage of its flexibility and efficacy in overcoming catastrophic forgetting. We use saliency maps to provide explanations of performed tasks and propose a new metric to assess their quality. Experiments show that ICL achieves state-of-the-art results in terms of overall continual learning performance as measured by average classification accuracy, and also in terms of its explanations, which are assessed qualitatively and quantitatively using the proposed metric.

L'articolo sviluppa un framework di continual learning interpretabile in cui le spiegazioni dei task finiti sono utilizzate per migliorare l'attention del learner durante i task futuri, e viene proposta anche una metrica di spiegazione.

Gli autori propongono un framework per continual learning basato su spiegazioni per classificazioni eseguite di task precedentemente appresi

Questo articolo propone un'estensione del framework di continual learning usando il continual learning variazionale esistente come metodo di base con evidence weight.


The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present results for non-SOTA networks (for instance only AlexNet for ImageNet-1K), or relatively small datasets (like CIFAR-10). In this work, we train state-of-the-art visual understanding neural networks on the ImageNet-1K dataset, with Integer operations on General Purpose (GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate (FMA) operations which take two pairs of INT16 operands and accumulate results into an INT32 output.We propose a shared exponent representation of tensors and develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network operations. The nuances of developing an efficient integer convolution kernel is examined, including methods to handle overflow of the INT32 accumulator. We implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and these networks achieve or exceed SOTA accuracy within the same number of iterations as their FP32 counterparts without any change in hyper-parameters and with a 1.8X improvement in end-to-end training throughput. To the best of our knowledge these results represent the first INT16 training results on GP hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported accuracy using half precision

Pipeline di training a precisione mista usando numeri interi a 16 bit su HW generico; accuratezza SOTA per CNN di classe ImageNet; migliore accuratezza riportata per il task di classificazione ImageNet-1K con qualsiasi training a precisione ridotta;

Questo articolo mostra che un'attenta implementazione del calcolo dinamico in virgola fissa a precisione mista può raggiungere una precisione allo stato dell'arte utilizzando un modello di deep learning a precisione ridotta con una rappresentazione degli interi a 16 bit

Propone uno schema a "virgola fissa dinamica" che condivide la parte dell'esponente per un tensore e sviluppa procedure per fare calcoli NN con questo formato e lo dimostra per un training a precisione limitata.


In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the acoustic model -- no phonetic word lexicon is needed. Key ingredients for the acoustic model are Gated Linear Units and high dropout. We show near state-of-the-art results in word error rate on the LibriSpeech corpus with MFSC features, both on the clean and other configurations.

Un modello acustico ConvNet basato sulle lettere porta a una pipeline di riconoscimento vocale semplice e competitiva.

Questo articolo applica le reti neurali convoluzionali gated al riconoscimento vocale, utilizzando il criterio di training ASG.


Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse mapping from data to this latent space. We propose Invariant Encoding Generative Adversarial Networks (IVE-GANs), a novel GAN framework that introduces such a mapping for individual samples from the data by utilizing features in the data which are invariant to certain transformations. Since the model maps individual samples to the latent space, it naturally encourages the generator to cover all modes. We demonstrate the effectiveness of our approach in terms of generative performance and learning rich representations on several datasets including common benchmark image generation tasks.

Un nuovo framework GAN che utilizza feature invarianti alla trasformazione per imparare rappresentazioni ricche e generatori forti.

Propone un obiettivo GAN modificato composto da un termine GAN classico e un termine di codifica invariante.

Questo articolo presenta l'IVE-GAN, un modello che introduce l'encoder nel framework delle Generative Adversarial Network.


We propose a method for learning the dependency structure between latent variables in deep latent variable models.   Our general modeling and inference framework combines the complementary strengths of deep generative models and probabilistic graphical models. In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure.   The network parameters, variational parameters as well as the latent topology are optimized simultaneously with a single objective.   Inference is formulated via a sampling procedure that produces expectations over latent variable structures and incorporates top-down and bottom-up reasoning over latent variable values.   We validate our framework in extensive experiments on MNIST, Omniglot, and CIFAR-10. Comparisons to state-of-the-art structured variational autoencoder baselines show improvements in terms of the expressiveness of the learned model.

Proponiamo un metodo per l'apprendimento della struttura di dipendenza latente negli autoencoder variazionali.

Utilizza una matrice di variabili casuali binarie per catturare le dipendenze tra le variabili latenti in un hierarchical deep generative model.

Questo articolo presenta un approccio VAE in cui una struttura di dipendenza sulla variabile latente viene appresa durante il training.

Gli autori propongono di aumentare lo spazio latente di un VAE con una struttura auto-regressiva, per migliorare l'espressività sia della rete di inferenza che del prior latente


Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system's structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.

Introduciamo un'architettura di rete neurale invariante alla scala per il rilevamento dei punti di cambiamento nelle serie temporali multivariate.

Il documento sfrutta il concetto di trasformata wavelet all'interno di un'architettura di deep learning per risolvere il rilevamento dei punti di cambiamento.

Questo articolo propone una rete neurale basata su una piramide e la applica a segnali 1D con processi sottostanti che si verificano a diverse scale temporali dove il task è la rilevazione del punto di cambiamento