The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA-layers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.

Sostituiamo i layer fully connected di una rete neurale con il multi-scale entanglement renormalization ansatz, un tipo di operazione quantistica che descrive le correlazioni a lungo raggio.

Nell'articolo gli autori suggeriscono di usare la tecnica di tensorizzazione MERA per comprimere le reti neurali.

Una nuova parametrizzazione delle mappe lineari per l'uso nelle reti neurali, utilizzando una fattorizzazione gerarchica della mappa lineare che riduce il numero di parametri pur permettendo di modellare interazioni relativamente complesse.

Studia come comprimere i layer feed forward usando decomposizioni tensoriali di basso rango ed esplorano una decomposizione ad albero


Deep learning models have outperformed traditional methods in many fields such  as natural language processing and computer vision. However, despite their  tremendous success, the methods of designing optimal Convolutional Neural Networks  (CNNs) are still based on heuristics or grid search. The resulting networks  obtained using these techniques are often overparametrized with huge computational  and memory requirements. This paper focuses on a structured, explainable  approach towards optimal model design that maximizes accuracy while keeping  computational costs tractable. We propose a single-shot analysis of a trained CNN  that uses Principal Component Analysis (PCA) to determine the number of filters  that are doing significant transformations per layer, without the need for retraining.  It can be interpreted as identifying the dimensionality of the hypothesis space  under consideration. The proposed technique also helps estimate an optimal number  of layers by looking at the expansion of dimensions as the model gets deeper.  This analysis can be used to design an optimal structure of a given network on  a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate  these techniques by optimizing VGG and AlexNet networks on CIFAR-10,  CIFAR-100 and ImageNet datasets.

Presentiamo un'analisi single shot di una rete neurale addestrata per rimuovere la ridondanza e identifichiamo la struttura ottimale della rete

Questo articolo propone una serie di euristiche per identificare una buona architettura di rete neurale, basata sulla PCA delle attivazioni delle unità sul dataset

Questo articolo presenta un framework per ottimizzare le architetture delle reti neurali attraverso l'identificazione dei filtri ridondanti tra i layer


Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels.   First, we define the threat model for these attacks:  our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victim ’s deep learning  (DL) system is running and passively monitors the accesses of the target functions in the shared framework.   Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victim’s entire network architecture.   In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having only observed one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model,  we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attacker’s observations. Our empirical security analysis represents a step toward understanding the DNNs’ vulnerability to cache side-channel attacks.

Conduciamo la prima analisi di sicurezza approfondita degli attacchi di fingerprinting alle DNN che sfruttano i side-channel della cache, il che rappresenta un passo verso la comprensione delle vulnerabilità delle DNN agli attacchi side-channel.

Questo articolo considera il problema del fingerprinting delle architetture di reti neurali utilizzando i side-channel della cache, e discute le difese security-through-obscurity.

Questo articolo esegue attacchi cache side-channel per estrarre gli attributi di un modello vittima e dedurre la sua architettura, oltre a mostrare che possono raggiungere una precisione di classificazione quasi perfetta.


Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.

Proponiamo il Complement Objective Training (COT), un nuovo paradigma di training che ottimizza sia gli obiettivi primari che quelli complementari per apprendere efficacemente i parametri delle reti neurali.

Considera l'aggiunta all'obiettivo di cross-entropy della massimizzazione dell'obiettivo "complemento", che mira a neutralizzare le probabilità previste di classi diverse dalle label corretta.

Gli autori propongono un obiettivo secondario per la minimizzazione del softmax basato sulla valutazione delle informazioni raccolte dalle classi errate, portando a un nuovo approccio di training.

Si occupa del training di reti neurali per task di classificazione o generazione di sequenze utilizzando la cross-entropy loss


We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.

Stima dell'incertezza in una singola forward pass senza parametri aggiuntivi apprendibili.

Un nuovo metodo per calcolare le stime di incertezza dell'output nelle DNN per problemi di classificazione che compete coi metodi allo stato dell'arte per la stima dell'incertezza e li supera nei task di out-of-distribution detection.

Gli autori presentano il softmax inibito, una modifica del softmax attraverso l'aggiunta di un'attivazione costante che fornisce una misura per l'incertezza.


When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.

Abbiamo realizzato un sistema ricco di funzionalità per il deep learning con input criptati, producendo output criptati, preservando la privacy.

Un framework per l'inferenza privata del modello di deep learning utilizzando schemi FHE che supportano il bootstrapping veloce e quindi possono ridurre il tempo di calcolo.

L'articolo presenta un mezzo per valutare una rete neurale in modo sicuro usando la crittografia omomorfa.


In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving.

Introduciamo un sistema chiamato GamePad per esplorare l'applicazione di metodi di apprendimento automatico alla dimostrazione di teoremi nell'assistente di prova Coq.

Questo articolo descrive un sistema per applicare l'apprendimento automatico alla dimostrazione interattiva dei teoremi, si concentra sui task di predizione della tattica e di valutazione della posizione, e mostra che un modello neurale supera un SVM su entrambi i task.

Propone che le tecniche di apprendimento automatico siano utilizzate per aiutare a costruire dimostrazioni nel theorem prover Coq.


Deep neural networks are usually huge, which significantly limits the deployment on low-end devices. In recent years, many  weight-quantized models have  been proposed. They have small storage and fast inference, but training can still be time-consuming. This can be improved with distributed learning. To reduce the high communication cost due to worker-server synchronization, recently gradient quantization has also been proposed to train deep networks with full-precision weights.   In this paper, we theoretically study how the combination of both weight and gradient quantization affects convergence.  We show  that (i) weight-quantized models converge to an error related to the weight quantization resolution and weight dimension; (ii) quantizing gradients slows convergence by a factor related to the gradient quantization resolution and dimension; and (iii) clipping the gradient before quantization renders this factor dimension-free, thus allowing the use of fewer bits for gradient quantization. Empirical experiments confirm the theoretical convergence results, and demonstrate that quantized networks can speed up training and have comparable performance as full-precision networks.

In questo articolo, abbiamo studiato il training efficiente delle reti a peso quantizzato con gradiente quantizzato in un ambiente distribuito, sia teoricamente che empiricamente.

Questo articolo studia le proprietà di convergenza della quantizzazione del peso consapevole della loss con diverse precisioni del gradiente nell'ambiente distribuito, e fornisce un'analisi di convergenza per la quantizzazione del peso con gradienti completi, quantizzati e quantizzati clipped.

Gli autori propongono un'analisi dell'effetto della quantizzazione simultanea dei pesi e dei gradienti nel training di un modello parametrizzato in un ambiente distribuito completamente sincronizzato.


Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that  imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations,  our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.

Una strategia di regolarizzazione per migliorare le prestazioni del sequential learning

Un nuovo approccio basato sulla regolarizzazione al problema del sequential learning utilizzando un modello a dimensione fissa che aggiunge termini extra alla loss, incoraggiando la sparsità della rappresentazione e combattendo il catastrophic forgetting.

Questo articolo affronta il problema del catastrophic forgetting nel lifelong learning proponendo strategie di apprendimento regolarizzate


A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved that the inhibitory probability function -log(1-exp(-x)) in surprisal space is the topologically conjugate function of the inhibitory complementary probability 1-x in probability space. Furthermore, we found that the derivative of the synapse over the parameter in the surprisal space is equal to the negative Bose-Einstein distribution. In addition, we constructed a fully connected synapse graph (tensor) as a synapse block of a synaptic neural network. Moreover, we proved the gradient formula of a cross-entropy loss function over parameters, so synapse learning can work with the gradient descent and backpropagation algorithms. In the proof-of-concept experiment, we performed an MNIST training and testing on the MLP model with synapse network as hidden layers.

Una rete neurale sinaptica con grafo di sinapsi e un processo di learning che ha la feature della coniugazione topologica e della distribuzione di Bose-Einstein nello spazio di sorpresa.

Gli autori propongono una rete neurale ibrida composta da un grafo di sinapsi che può essere incorporato in una rete neurale standard

Presenta un modello di rete neurale biologicamente ispirato basato sui canali ionici eccitatori e inibitori nelle membrane delle cellule reali


Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning algorithms, and propose to extend this by introducing a multi-shot unsupervised learning framework. Empirical results on several real-world data set show that the proposed model consistently and significantly outperforms existing state-of-the-art approaches on knowledge base completion and graph based multi-label classification tasks.

Modelli generalizzati di graph embedding

Un approccio generalizzato di embedding dei knowledge graph che impara gli embedding basati su tre diversi obiettivi simultanei, e performa alla pari o addirittura supera gli approcci esistenti allo stato dell'arte.

Affronta il task di apprendere embedding di grafi multi-relazionali usando una rete neurale

Propone un nuovo metodo, GEN, per calcolare embedding di grafi multirelazionali, in particolare perché le cosiddette E-Cells e R-Cells possano rispondere a query della forma (h,r,?),(?r,t), e (h,?,t)


We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are encouraged to be small and diverse early on, and then larger, harder, and allowably more homogeneous in later stages. At each stage, model weights and training sets are chosen by solving a joint continuous-discrete minimax optimization, whose objective is composed of a continuous loss (reflecting training set hardness) and a discrete submodular promoter of diversity for the chosen subset. MCL repeatedly solves a sequence of such optimizations with a schedule of increasing training set size and decreasing pressure on diversity encouragement. We reduce MCL to the minimization of a surrogate function handled by submodular maximization and continuous gradient methods. We show that MCL achieves better performance and, with a clustering trick, uses fewer labeled samples for both shallow and deep models while achieving the same performance. Our method involves repeatedly solving constrained submodular maximization of an only slowly varying function on the same ground set. Therefore, we develop a heuristic method that utilizes the previous submodular maximization solution as a warm start for the current submodular maximization process to reduce computation while still yielding a guarantee.

Il Minimax Curriculum Learning è un metodo di machine learning che comporta l'aumento della hardness desiderabile e la riduzione programmata della diversità.

Un approccio di curriculum learning che utilizza una funzione set submodulare che cattura la diversità degli esempi scelti durante il training.

L'articolo introduce il MiniMax Curriculum learning come un approccio per il training adattivo dei modelli fornendo diversi sottoinsiemi di dati.


Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform the second best result by an absolute difference of 15-45.3%.

Modelli impliciti applicati alla causalità e alla genetica

Gli autori propongono di utilizzare il modello implicito per affrontare il problema dell'associazione Genome-Wide.

Questo articolo propone soluzioni per i problemi di confondimento negli studi di associazione genome-wide dovuti alla struttura della popolazione e la potenziale presenza di interazioni non lineari tra diverse parti del genoma, e collega la genetica statistica e il ML.

Presenta un modello generativo non lineare per GWAS che modella la struttura della popolazione in cui le non linearità sono modellate utilizzando reti neurali come approssimatori di funzioni non lineari e l'inferenza viene eseguita utilizzando l'inferenza variazionale senza likelihood


Few-shot learning trains image classifiers over datasets with few examples per category.   It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories.   Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly.  For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning.  Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.

Few-shot learning sfruttando la relazione object-level per imparare la relazione image-level (somiglianza)

Questo articolo affronta il problema del few-shot learning proponendo un approccio basato sull'embedding che impara a confrontare le feature object-level tra gli example nel support e query set

Propone un metodo di few-shot learning che sfrutta la relazione object-level tra diverse immagini basate sulla ricerca dei nearest neighbor e concatena le feature map di due immagini di input in un'unica feature map


Word embeddings are widely used in machine learning based natural language processing systems. It is common to use pre-trained word embeddings which provide benefits such as reduced training time and improved overall performance. There has been a recent interest in applying natural language processing techniques to programming languages. However, none of this recent work uses pre-trained embeddings on code tokens. Using extreme summarization as the downstream task, we show that using pre-trained embeddings on code tokens provides the same benefits as it does to natural languages, achieving: over 1.9x speedup, 5\% improvement in test loss, 4\% improvement in F1 scores, and resistance to over-fitting. We also show that the choice of language used for the embeddings does not have to match that of the task to achieve these benefits and that even embeddings pre-trained on human languages provide these benefits to programming languages.

I ricercatori che esplorano le tecniche di natural language processing applicate al codice sorgente non stanno usando alcuna forma di embedding pre-addestrati, noi dimostriamo invece che dovrebbero farne uso.

Questo articolo si prefigge di capire se il pretraining di word embedding per il codice nei linguaggi di programmazione utilizzando language model di NLP ha un impatto sul task di extreme code summarization.

Questo lavoro mostra come il pre-training dei word vector usando corpora di codice porta a rappresentazioni che sono più adatte delle rappresentazioni randomly initialized e addestrate per la predizione dei nomi di funzioni/metodi


Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.

Risolviamo il cubo di Rubik con il puro reinforcement learning

Soluzione per risolvere il cubo di Rubik usando reinforcement learning (RL) con la ricerca ad albero di Monte-Carlo (MCTS) attraverso l'iterazione autodidattica.

Questo lavoro risolve il cubo di Rubik usando un metodo di iterazione approssimativa delle policy chiamato iterazione autodidattica, superando il problema delle reward sparse creando il proprio sistema di reward.

Introduce un algoritmo di deep RL per risolvere il cubo di Rubik che gestisce l'enorme spazio di stato e la reward molto sparsa del cubo di Rubik


Answering compositional questions requiring multi-step reasoning is challenging for current models. We introduce an end-to-end differentiable model for interpreting questions, which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a knowledge graph, together with a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituents, culminating in a grounding for the complete sentence which is an answer to the question. For example, to interpret ‘not green’, the model will represent ‘green’ as a set of entities, ‘not’ as a trainable ungrounded vector, and then use this vector to parametrize a composition function to perform a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent. We show the model can learn to represent a variety of challenging semantic operators, such as quantifiers, negation, disjunctions and composed relations on a synthetic question answering task. The model also generalizes well to longer sentences than seen in its training data, in contrast to LSTM and RelNet baselines. We will release our code.

Descriviamo un modello differenziabile end-to-end per QA che impara a rappresentare segmenti di testo nella domanda come denotazioni nel knowledge graph, imparando sia moduli neurali per la composizione che la struttura sintattica della frase.

Questo articolo presenta un modello per la visual question answering che può imparare sia i parametri che gli structure predictor per una rete neurale modulare, senza strutture supervisionate o assistenza da un parser sintattico.

Propone di addestrare un modello di question answering a partire dalle sole risposte e da una KB imparando alberi latenti che catturano la sintassi e imparano la semantica delle parole


Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain- specific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.

Introduciamo una nuova infrastruttura per un compilatore che affronta le carenze dei framework esistenti per il deep learning.

Si propone di passare dalla generazione di codice ad-hoc nei motori di deep learning alle best practice in termini di compilatore e linguaggi di programmazione.

Questo articolo presenta un framework per compilatori che permette la definizione di linguaggi specifici per i sistemi di deep learning e definisce fasi di compilazione che possono trarre vantaggio dalle ottimizzazioni standard e dalle ottimizzazioni specializzate per le reti neurali.

Questo articolo introduce una DLVM per sfruttare gli aspetti di compilazione di un compilatore tensoriale


In this work, we focus on the problem of grounding language by training an agent  to follow a set of natural language instructions and navigate to a target object  in a 2D grid environment. The agent receives visual information through raw  pixels and a natural language instruction telling what task needs to be achieved.  Other than these two sources of information, our model does not have any prior  information of both the visual and textual modalities and is end-to-end trainable.  We develop an attention mechanism for multi-modal fusion of visual and textual  modalities that allows the agent to learn to complete the navigation tasks and also  achieve language grounding. Our experimental results show that our attention  mechanism outperforms the existing multi-modal fusion mechanisms proposed in  order to solve the above mentioned navigation task. We demonstrate through the  visualization of attention weights that our model learns to correlate attributes of  the object referred in the instruction with visual representations and also show  that the learnt textual representations are semantically meaningful as they follow  vector arithmetic and are also consistent enough to induce translation between instructions  in different natural languages. We also show that our model generalizes  effectively to unseen scenarios and exhibit zero-shot generalization capabilities.  In order to simulate the above described challenges, we introduce a new 2D environment  for an agent to jointly learn visual and textual modalities

Architettura basata sull'attention per il grounding della lingua attraverso reinforcement learning in un nuovo ambiente a griglia 2D personalizzabile

L'articolo affronta il problema della navigazione data da un'istruzione e propone un approccio per combinare informazioni testuali e visive attraverso un meccanismo di attention

Questo articolo considera il problema di seguire istruzioni in linguaggio naturale data una visione in prima persona di un ambiente sconosciuto a priori, e propone un metodo con un'architettura neurale.

Studia il problema della navigazione verso un oggetto target in un ambiente a griglia 2D seguendo una descrizione in linguaggio naturale e ricevendo informazioni visive come pixel grezzi.


Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks:  Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model.   On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.

Una semplice architettura composta da convoluzioni e attention raggiunge risultati alla pari con i migliori modelli ricorrenti documentati.

Un metodo di data augmentation basato sulla parafrasi veloce e ad alte prestazioni e un modello di reading comprehension non ricorrente che usa solo convoluzioni e attention.

Questo articolo propone di applicare i moduli CNN+self-attention al posto delle LSTM e di migliorare il training del modello RC con le parafrasi dei passaggi generate da un modello neurale di paraphrasing al fine di migliorare le prestazioni RC.

Questo articolo presenta un modello di reading comprehension utilizzando le convoluzioni e l'attention e propone di aggiungere  dati di training attraverso la parafrasi basata sulla traduzione automatica neurale off-the-shelf


Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective.   In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.

Introduciamo Spherical CNN, una rete convoluzionale per segnali sferici, e la applichiamo al riconoscimento di modelli 3D e alla regressione dell'energia molecolare.

L'articolo propone un framework per la costruzione di reti convoluzionali sferiche basato su una nuova sintesi di diversi concetti esistenti

Questo articolo si concentra su come estendere le reti neurali convoluzionali per incorporare l'invarianza sferica, e adatta gli strumenti dell'analisi armonica non abeliana per raggiungere questo obiettivo.

Gli autori sviluppano un nuovo schema per rappresentare i dati sferici da zero


We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural network, perform gradient decent to maximize the fitness. We demonstrate this methods effectiveness by designing an optimized heat sink and both 2D and 3D airfoils that maximize the lift drag ratio under steady state flow conditions. We highlight that our method has two distinct benefits over other automated design approaches. First, evaluating the neural networks prediction of fitness can be orders of magnitude faster then simulating the system of interest. Second, using gradient decent allows the design space to be searched much more efficiently then other gradient free methods. These two strengths work together to overcome some of the current shortcomings of automated design.

Un metodo per eseguire la progettazione automatizzata su oggetti del mondo reale come dissipatori di calore e profili alari che fa uso di reti neurali e gradient descent.

Rete neurale (parametrizzazione e predizione) e gradient descent (backpropagation) per progettare automaticamente i task di ingegneria.

Questo articolo introduce l'uso di una deep network per approssimare il comportamento di un sistema fisico complesso, e poi progettare dispositivi ottimali ottimizzando questa rete rispetto ai suoi input.


Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.

Proponiamo una versione duale della distanza logistic adversarial per l'allineamento delle feature e mostriamo che produce iterazioni a gradiente più stabili dell'obiettivo min-max.

L'articolo si occupa di migliorare le GAN a livello computazionale

Questo articolo studia una formulazione duale di una loss adversarial basata su un upper-bound della loss logistica, e trasforma il problema min max standard dell'adversarial training in un singolo problema di minimizzazione.

Propone di riformulare l'obiettivo del punto di sella delle GAN (per un discriminatore di regressione logistica) come un problema di minimizzazione dualizzando l'obiettivo di massima verosimiglianza per la regressione logistica regolarizzata


There are many applications scenarios for which the computational   performance and memory footprint of the prediction phase of Deep   Neural Networks (DNNs) need to be optimized. Binary Deep Neural   Networks (BDNNs) have been shown to be an effective way of achieving   this objective. In this paper, we show how Convolutional Neural   Networks (CNNs) can be implemented using binary   representations. Espresso is a compact, yet powerful   library written in C/CUDA that features all the functionalities   required for the forward propagation of CNNs, in a binary file less   than 400KB, without any external dependencies. Although it is mainly   designed to take advantage of massive GPU parallelism, Espresso also   provides an equivalent CPU implementation for CNNs. Espresso   provides special convolutional and dense layers for BCNNs,   leveraging bit-packing and bit-wise computations   for efficient execution. These techniques provide a speed-up of   matrix-multiplication routines, and at the same time, reduce memory   usage when storing parameters and activations. We experimentally   show that Espresso is significantly faster than existing   implementations of optimized binary neural networks (~ 2   orders of magnitude). Espresso is released under the Apache 2.0   license and is available at http://github.com/organization/project.

Implementazione delle reti neurali binarie allo stato dell'arte in termini di prestazioni computazionali

L'articolo presenta una libreria scritta in C/CUDA che presenta tutte le funzionalità necessarie per la forward propagation delle BCNN

Questo paper si basa su Binary-NET e lo espande alle architetture CNN, fornisce ottimizzazioni che migliorano la velocità del forward pass, e fornisce codice ottimizzato per Binary CNN.


Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall.

Proponiamo un algoritmo di subset selection che è addestrabile con metodi basati sul gradiente ma che raggiunge prestazioni quasi ottimali tramite l'ottimizzazione submodulare.

Propone un modello basato sulla rete neurale che integra la funzione submodulare combinando la tecnica di ottimizzazione basata sul gradiente con un framework submodulare chiamato 'Differentiable Greedy Network' (DGN).

Propone una rete neurale che mira a selezionare un sottoinsieme di elementi (ad esempio, selezionando k frasi che sono più legate ad un claim da un insieme di documenti recuperati)


The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which oftentimes involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Specifically, we place a nonparametric Bayesian prior on embeddings to handle dynamic mixture hierarchies under the variational autoencoder framework, and to adopt the generative process of a hierarchical-versioned Gaussian mixture model. Compared with a few prior works focusing on unifying representation learning and hierarchical clustering, HCRL is the first model to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. This generation process enables more meaningful separations and mergers of clusters via branches in a hierarchy. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.

Introduciamo lo hierarchically clustered representation learning (HCRL), che ottimizza simultaneamente il representation learning e il clustering gerarchico nello spazio di embedding.

L'articolo propone di usare il CRP annidato come un modello di clustering piuttosto che come un topic model

Presenta un nuovo metodo di clustering gerarchico su uno spazio di embedding in cui sia lo spazio di embedding che il clustering gerarchico sono appresi simultaneamente


We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input space are used to design soft decision labels for feature to label isometry. Classconditional distributions over features are also learned using BNP-MFA to develop plug-in maximum a posterior (MAP) classifiers to replace the traditional multinomial logistic softmax classification layers. This novel unsupervised augmented framework, which we call geometrically robust networks (GRN), is applied to CIFAR-10, CIFAR-100, and to Radio-ML (a time series dataset for radio modulation recognition). We demonstrate the robustness of GRN models to adversarial attacks from fast gradient sign method, Carlini-Wagner, and projected gradient descent.

Sviluppiamo un framework di augmentation dell'unsupervised learning statistico-geometrico per deep neural network per renderle robuste agli adversarial attack.

Trasforma le deep neural network tradizionali in classificatori robusti adversarial utilizzando le GRN

Propone una difesa basata su distribuzioni di feature class-conditional per trasformare le deep neural network in classificatori robusti


Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.

Rendere più efficiente il deep reinforcement learning in grandi spazi di stato-azione usando l'esplorazione strutturata con deep hierarchical policy.

Un metodo per coordinare il comportamento degli agenti usando policy che hanno una struttura latente condivisa, un metodo di ottimizzazione variazionale delle policy per ottimizzare le policy coordinate, e una derivazione dell'update variazionale e gerarchico degli autori.

Questo articolo suggerisce un'innovazione algoritmica costituita da variabili latenti gerarchiche per l'esplorazione coordinata in ambienti multi-agente


Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can improve generalization on individual tasks. However we lack analytic theories that can quantitatively predict how the degree of knowledge transfer depends on the relationship between the tasks. We develop an analytic theory of the nonlinear dynamics of generalization in deep linear networks, both within and across tasks. In particular, our theory provides analytic solutions to the training and testing error of deep networks as a function of training time, number of examples, network size and initialization, and the task structure and SNR. Our theory reveals that deep networks progressively learn the most important task structure first, so that generalization error at the early stopping time primarily depends on task structure and is independent of network size. This suggests any tight bound on generalization error must take into account task structure, and explains observations about real data being learned faster than random data. Intriguingly our theory also reveals the existence of a learning algorithm that proveably out-performs neural network training through gradient descent. Finally, for transfer learning, our theory reveals that knowledge transfer depends sensitively, but computably, on the SNRs and input feature alignments of pairs of tasks.

Forniamo molte intuizioni sulla generalizzazione delle reti neurali dal caso lineare trattabile dalla teoria.

Gli autori studiano un semplice modello di reti lineari per comprendere la generalizzazione e il transfer learning


We conduct a mathematical analysis on the Batch normalization (BN) effect on gradient backpropagation in residual network training in this work, which is believed to play a critical role in addressing the gradient vanishing/explosion problem. Specifically, by analyzing the mean and variance behavior of the input and the gradient in the forward and backward passes through the BN and residual branches, respectively, we show that they work together to confine the gradient variance to a certain range across residual blocks in backpropagation. As a result, the gradient vanishing/explosion problem is avoided. Furthermore, we use the same analysis to discuss the tradeoff between depth and width of a residual network and demonstrate that shallower yet wider resnets have stronger learning performance than deeper yet thinner resnets.

La batch normalization mantiene la varianza del gradiente durante l'allenamento, stabilizzando così l'ottimizzazione.

Questo articolo ha analizzato l'effetto della batch normalization sulla backpropagation del gradiente nelle reti residuali


To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task.

I giudizi comportamentali umani sono utilizzati per ottenere rappresentazioni sparse e interpretabili degli oggetti che si generalizzano ad altri task

Questo articolo descrive un esperimento su larga scala sulle rappresentazioni umane semantiche o di oggetti e un modello di tali rappresentazioni.

Questo articolo sviluppa un nuovo sistema di rappresentazione per le rappresentazioni di oggetti dal training sui dati raccolti da giudizi umani di immagini di tipo odd-one-out.

Un nuovo approccio per apprendere uno spazio semantico sparso, positivo e interpretabile che massimizza i giudizi umani di somiglianza attraverso il training per massimizzare specificamente la predizione dei giudizi umani di somiglianza.


We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering.    We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.    The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks.  We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.

Proponiamo un agente che si pone tra l'utente e un sistema di question answering a scatola nera e che impara a riformulare le domande per ottenere le migliori risposte possibili

Questo articolo propone un active question answering attraverso un approccio di reinforcement learning che impara a riformulare le domande in modo da fornire le migliori risposte possibili.

Descrive chiaramente come i ricercatori hanno progettato e addestrato attivamente due modelli per la riformulazione delle domande e la selezione delle risposte durante gli episodi di question answering


Most deep latent factor models choose simple priors for simplicity, tractability  or not knowing what prior to use. Recent studies show that the choice of  the prior may have a profound effect on the expressiveness of the model,  especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders  (AAEs). We introduce the notion of code generators to transform manually selected  simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than  AAEs in both supervised and unsupervised settings. Lastly, we present its  ability to do cross-domain translation in a  text-to-image synthesis task.

Apprendimento di prior per adversarial autoencoder

Propone una semplice estensione degli adversarial autoencoder per la generazione condizionale di immagini.

Si concentra sugli adversarial autoencoder e introduce una rete di generatori di codice per trasformare un semplice prior in uno che insieme al generatore può adattarsi meglio alla distribuzione dei dati


In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

Generazione di testo usando gli embedding di frasi dai vettori skip-thought con l'aiuto delle generative adversarial network.

Descrive l'applicazione delle generative adversarial network per la modellazione di dati testuali con l'aiuto di vettori skip-thought  e gli esperimenti con diversi tipi di GAN per due diversi dataset.


The novel \emph{Unbiased Online Recurrent Optimization} (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in a streaming fashion and avoids backtracking through past activations and inputs. UORO is computationally as costly as \emph{Truncated Backpropagation Through Time} (truncated BPTT), a widespread algorithm for online learning of recurrent networks \cite{jaeger2002tutorial}.  UORO is a modification of \emph{NoBackTrack} \cite{DBLP:journals/corr/OllivierC15} that bypasses the need for model sparsity and makes implementation easy in current deep learning frameworks, even for complex models.   Like NoBackTrack, UORO provides unbiased gradient estimates; unbiasedness is the core hypothesis in stochastic gradient descent theory, without which convergence to a local optimum is not guaranteed. On the contrary, truncated BPTT does not provide this property, leading to possible divergence.   On synthetic tasks where truncated BPTT is shown to diverge, UORO converges. For instance, when a parameter has a positive short-term but negative long-term influence, truncated BPTT diverges unless the truncation span is very significantly longer than the intrinsic temporal range of the interactions, while UORO performs well thanks to the unbiasedness of its gradients.

Introduce una stima del gradiente online, unbiased e facilmente implementabile per modelli ricorrenti.

Gli autori introducono un nuovo approccio all'apprendimento online dei parametri delle reti neurali ricorrenti da lunghe sequenze che supera la limitazione della backpropagation troncata nel tempo

Questo articolo affronta il training online di RNN, e propone una modifica a RTRL e di usare l'approccio forward per il calcolo del gradiente.


We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.  We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.

Super-risoluzione di label coarse in label a livello di pixel, applicate a immagini aeree e scansioni mediche.

Un metodo per risolvere le label di segmentazione coarse a bassa risoluzione se la distribuzione congiunta delle label a bassa e alta risoluzione è nota.


We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.

Proponiamo un metodo per allineare le feature latenti apprese da diversi dataset utilizzando le correlazioni armoniche.

Propone l'uso di corrispondenze di feature per preformare l'allineamento manifold tra batch di dati dagli stessi sample per evitare la raccolta di misure rumorose.


Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co-evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body.   We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components.   We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a 3D environment with simulated physics compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone.    A video is available at: www.youtube.com/watch?v=eei6Rgom3YY

L'evoluzione della forma del corpo negli agenti controllati da RL migliora le loro prestazioni (e aiuta l'apprendimento)

Algoritmo PEOM che incorpora il valore Shapley per accelerare l'evoluzione identificando il contributo di ogni parte del corpo


Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.

Modellare la reward con la motivazione intrinseca per evitare stati catastrofici e mitigare il catastrophic forgetting.

Un algoritmo di RL che combina l'algoritmo DQN con un fear model addestrato in parallelo per prevedere gli stati catastrofici.

L'articolo studia il catastrophic forgetting nel RL, enfatizzando i task in cui una DQN è in grado di imparare ad evitare eventi catastrofici a patto che eviti il forgetting.


Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere S^2 or a unit ball B^3---entails unique challenges. In this work, we propose a novel `"volumetric convolution" operation that can effectively convolve arbitrary functions in B^3. We develop a theoretical framework for "volumetric convolution" based on Zernike polynomials and efficiently implement it as a differentiable and an easily pluggable layer for deep networks. Furthermore, our formulation leads to derivation of a  novel formula to measure the symmetry of a function in B^3 around an arbitrary axis, that is useful in 3D shape analysis tasks. We demonstrate the efficacy of proposed volumetric convolution operation on a possible use-case i.e., 3D object recognition task.

Un nuovo operatore di convoluzione per l'apprendimento automatico della rappresentazione all'interno della sfera unitaria

Questo lavoro è legato ai recenti articoli sulle reti sferiche CNN e SE(n) equivarianti ed estende le idee precedenti ai dati volumetrici nella sfera unitaria.

Propone l'uso di convoluzioni volumetriche su reti di convoluzioni per imparare la sfera unitaria e discute la metodologia e i risultati del processo.


Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions onWorld of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.

Addestriamo policy di reinforcement learning utilizzando l'aumento della reward, il curriculum learning e il meta-learning per navigare con successo le pagine web.

Sviluppa un metodo di curriculum learning per addestrare un agente di RL a navigare nel web, basato sull'idea di decomporre un'istruzione in più sotto-istruzioni.


Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language $L_s$ to the target language $L_t$. In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task.   We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.

Classificazione del testo cross language mediante codifica universale

Questo articolo propone un approccio alla classificazione cross-linguale dei testi attraverso l'uso di corpora comparabili.

Apprendimento di cross-lingual embedding e training di un classificatore utilizzando dati annotati nella lingua di origine per affrontare l'apprendimento di un cross-language text categorizer senza informazioni annotate nella lingua di destinazione


Syntax is a powerful abstraction for language understanding. Many downstream tasks require segmenting input text into meaningful constituent chunks (e.g., noun phrases or entities); more generally, models for learning semantic representations of text benefit from integrating syntax in the form of parse trees (e.g., tree-LSTMs). Supervised parsers have traditionally been used to obtain these trees, but lately interest has increased in unsupervised methods that induce syntactic representations directly from unlabeled text. To this end, we propose the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Unlike many prior approaches, DIORA does not rely on supervision from auxiliary downstream tasks and is thus not constrained to particular domains. Furthermore, competing approaches do not learn explicit phrase representations along with tree structures, which limits their applicability to phrase-based tasks. Extensive experiments on unsupervised parsing, segmentation, and phrase clustering demonstrate the efficacy of our method. DIORA achieves the state of the art in unsupervised parsing (46.9 F1) on the benchmark WSJ dataset.

In questo lavoro proponiamo deep inside-outside recursive auto-encoder (DIORA) un metodo completamente unsupervised per scoprire la sintassi e contemporaneamente imparare rappresentazioni per i costituenti scoperti.

Un modello neurale ad albero latente addestrato con un obiettivo di autoencoding che raggiunge lo stato dell'arte nell'unsupervised constituency parsing e cattura la struttura sintattica meglio di altri modelli ad albero latente.

L'articolo propone un modello per unsupervised dependency parsing (induzione dell'albero latente) che è basato su una combinazione dell'algoritmo inside-outside con la modellazione neurale (autoencoder ricorsivi).


Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is unrolled. But because the training procedure must be unrolled thousands of times, the meta-objective must be defined with an orders-of-magnitude shorter time horizon than is typical for neural net training. We show that such short-horizon meta-objectives cause a serious bias towards small step sizes, an effect we term short-horizon bias. We introduce a toy problem, a noisy quadratic cost function, on which we analyze short-horizon bias by deriving and comparing the optimal schedules for short and long time horizons. We then run meta-optimization experiments (both offline and online) on standard benchmark datasets, showing that meta-optimization chooses too small a learning rate by multiple orders of magnitude, even when run with a moderately long time horizon (100 steps) typical of work in the area. We believe short-horizon bias is a fundamental problem that needs to be addressed if meta-optimization is to scale to practical neural net training regimes.

Indaghiamo il bias nell'obiettivo di meta-ottimizzazione short-horizon.

Questo articolo propone un modello e un problema semplificato per dimostrare lo short-horizon bias della meta-ottimizzazione del learning rate.

Questo articolo studia la questione della truncated backpropagation per la meta-ottimizzazione attraverso una serie di esperimenti su un problema giocattolo


Mainstream captioning models often follow a sequential structure to generate cap-  tions, leading to issues such as introduction of irrelevant semantics, lack of diversity  in the generated captions, and inadequate generalization performance. In this paper,  we present an alternative paradigm for image captioning, which factorizes the  captioning procedure into two stages: (1) extracting an explicit semantic represen-  tation from the given image; and (2) constructing the caption based on a recursive  compositional procedure in a bottom-up manner. Compared to conventional ones,  our paradigm better preserves the semantic content through an explicit factorization  of semantics and syntax. By using the compositional generation procedure, caption  construction follows a recursive structure, which naturally fits the properties of  human language. Moreover, the proposed compositional procedure requires less  data to train, generalizes better, and yields more diverse captions.

Un modo gerarchico e compositivo di generare didascalie

Questo articolo presenta un metodo più interpretabile per l'image captioning.


While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for neural network architectures based on topological data analysis on weighted stratified graphs. To demonstrate the usefulness of our approach, we show that neural persistence reflects best practices developed in the deep learning community such as dropout and batch normalization. Moreover, we derive a neural persistence-based stopping criterion that shortens the training process while achieving comparable accuracies as early stopping based on validation loss.

Sviluppiamo una nuova misura di complessità topologica per le deep neural network e dimostriamo che cattura le loro proprietà salienti.

Questo articolo propone la nozione di persistenza neurale, una misura topologica per assegnare punteggi ai layer completamente connessi in una rete neurale.

L'articolo propone di analizzare la complessità di una rete neurale usando la sua omologia persistente di ordine zero.


Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, which generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.

Guardare i confini decisionali intorno a un input ti dà più informazioni di un piccolo neighborhood fisso

Gli autori presentano un nuovo attacco per la generazione di adversarial example in cui attaccano i classificatori creati classificando casualmente piccole perturbazioni L2

Un nuovo approccio per generare adversarial attack a una rete neurale e un metodo per difendere una rete neurale da questi attacchi.


Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters.   We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters.   Our method trains a neural network to output approximately optimal weights as a function of hyperparameters.   We show that our method converges to locally optimal weights and hyperparameters for sufficiently large hypernets.   We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.

Addestriamo una rete neurale per produrre pesi approssimativamente ottimali in funzione degli iperparametri.

Iper-reti per l'ottimizzazione degli iper-parametri nelle reti neurali.


Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors. Finally, we discuss some financial applications of the GP-LVM model.

Stima della matrice di covarianza delle attività finanziarie con modelli a variabili latenti con processo gaussiano

Illustra come il Gaussian Process Latent Variable Model (GP-LVM) può sostituire i classici modelli a fattori lineari per la stima delle matrici di covarianza nei problemi di ottimizzazione del portafoglio.

Questo articolo utilizza GPLVM standard per modellare la struttura di covarianza e una rappresentazione dello spazio latente delle serie temporali finanziarie S&P500, per ottimizzare i portafogli e prevedere i valori mancanti.

Questo articolo propone di utilizzare una GPLVM per modellare i rendimenti finanziari


We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients.

Introduciamo l'apprendimento meta-adversarial, una nuova tecnica per regolarizzare le GAN, e proponiamo un metodo di training controllando esplicitamente la distribuzione dell'output del discriminatore.

L'articolo propone l'adversarial learning che regolarizza la varianza per il training delle GAN per assicurare che il gradiente del generatore non svanisca


We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights.   NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and Dueling agents (entropy reward and epsilon-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.

Un agente di deep reinforcement learning con rumore parametrico aggiunto ai suoi pesi può essere utilizzato per aiutare l'esplorazione efficiente.

Questo articolo introduce le NoisyNet, reti neurali i cui parametri sono perturbati da una funzione di rumore parametrico, che ottengono un sostanziale miglioramento delle prestazioni rispetto agli algoritmi base di deep reinforcement learning.

Nuovo metodo di esplorazione per deep RL  iniettando rumore nei pesi delle deep network, con il rumore che assume varie forme


Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to minimize the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.

"Active Neural Localizer", una rete neurale completamente differenziabile che impara a localizzare in modo efficiente utilizzando deep reinforcement learning.

Questo articolo formula il problema della localizzazione su una mappa nota usando una belief network come un problema di RL in cui l'obiettivo dell'agente è di minimizzare il numero di step per localizzarsi.

Questo è un paper chiaro e interessante che costruisce una rete parametrizzata per selezionare azioni per un robot in un ambiente simulato


Machine translation is an important real-world application, and neural network-based AutoRegressive Translation (ART) models have achieved very promising accuracy. Due to the unparallelizable nature of the autoregressive factorization, ART models have to generate tokens one by one during decoding and thus suffer from high inference latency. Recently, Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time. However, they could only achieve inferior accuracy compared with ART models. To improve the accuracy of NART models, in this paper, we propose to leverage the hints from a well-trained ART model to train the NART model. We define two hints for the machine translation task: hints from hidden states and hints from word alignments, and use such hints to regularize the optimization of NART models. Experimental results show that the NART model trained with hints could achieve significantly better translation performance than previous NART models on several tasks. In particular, for the WMT14 En-De and De-En task, we obtain BLEU scores of 25.20 and 29.52 respectively, which largely outperforms the previous non-autoregressive baselines. It is even comparable to a strong LSTM-based ART model (24.60 on WMT14 En-De), but one order of magnitude faster in inference.

Sviluppiamo un algoritmo di training per modelli di traduzione automatica non autoregressivi, ottenendo un'accuratezza paragonabile alle baseline autoregressive forti, essendo però un ordine di grandezza più veloce nell'inferenza.

Distilla la conoscenza dagli hidden state intermedi e dai pesi di attention per migliorare la traduzione automatica neurale non autoregressiva.

Propone di sfruttare il modello autoregressivo ben addestrato per informare gli hidden state e l'allineamento delle parole dei modelli di traduzione automatica non autoregressivi.


Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning  the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary  morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as Morph-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network. We have carried out our experiments on  MNIST, Fashion-MNIST, CIFAR10 and CIFAR100.

Usando le operazioni morfologiche (dilatazione ed erosione) abbiamo definito una classe di reti che può approssimare qualsiasi funzione continua.

Questo articolo propone di sostituire le unità RELU/tanh standard con una combinazione di operazioni di dilatazione ed erosione, osservando che il nuovo operatore crea più iperpiani e ha più potenza espressiva.

Gli autori introducono Morph-Net, una rete neurale a singolo layer in cui la mappatura viene eseguita utilizzando la dilatazione e l'erosione morfologica.


With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique which integrates the activation pruning with the weight pruning. Through the learning on the different importance of neuron responses and connections, the generated network, namely IPnet, balances the sparsity between activations and weights and therefore further improves execution efficiency. The feasibility and effectiveness of IPnet are thoroughly evaluated through various network models with different activation functions and on different datasets. With <0.5% disturbance on the testing accuracy, IPnet saves 71.1% ~ 96.35% of computation cost, compared to the original dense models with up to 5.8x and 10x reductions in activation and weight numbers, respectively.

Questo lavoro fa avanzare la compressione DNN oltre i pesi applicandola alle attivazioni integrando il pruning delle attivazioni con il pruning dei pesi.

Un metodo di compressione integrale del modello che gestisce sia il pruning dei pesi che delle attivazioni, portando ad una computazione delle reti più efficiente e a una riduzione efficace del numero di moltiplicazioni e accumulazioni.

Questo articolo presenta un nuovo approccio per ridurre il costo di calcolo delle deep neural network integrando il pruning delle attivazioni a quello dei pesi e mostra che le tecniche comuni di pruning applicate solo ai pesi aumentano il numero di attivazioni non nulle dopo ReLU.


The Variational Auto Encoder (VAE) is a popular generative  latent variable model that is often  applied for representation learning.  Standard VAEs assume continuous valued  latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a  differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if  we want to train VAEs with discrete valued latent variables,  since reparametrized sampling is not possible. Till now, there exist no simple solutions to circumvent this problem.  In this paper, we propose an easy method to train VAEs  with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and  Categorically distributed latent representations on two different benchmark datasets.

Proponiamo un metodo semplice per addestrare Variational Auto Encoder (VAE) con rappresentazioni latenti discrete, utilizzando l'importance sampling

Introdurre una distribuzione di importance sampling e usare sample dalla distribuzione per calcolare la stima pesata d'importanza del gradiente

Questo articolo propone di usare l'importance sampling per ottimizzare VAE con variabili latenti discrete.


Distributed computing can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted: scaling the training process is difficult, and existing SGD methods require substantial tuning of hyperparameters and learning schedules to achieve sufficient accuracy when increasing the number of workers. In practice, such tuning can be prohibitively expensive given the huge number of potential hyperparameter configurations and the effort required to test each one.       We propose DANA, a novel approach that scales out-of-the-box to large clusters using the same hyperparameters and learning schedule optimized for training on a single worker, while maintaining similar final accuracy without additional overhead. DANA estimates the future value of model parameters by adapting Nesterov Accelerated Gradient to a distributed setting, and so mitigates the effect of gradient staleness, one of the main difficulties in scaling SGD to more workers.   Evaluation on three state-of-the-art network architectures and three datasets shows that DANA scales as well as or better than existing work without having to tune any hyperparameters or tweak the learning schedule. For example, DANA achieves 75.73% accuracy on ImageNet when training ResNet-50 with 16 workers, similar to the non-distributed baseline.

Un nuovo algoritmo distribuito asincrono SGD che raggiunge un'accuratezza allo stato dell'arte sulle architetture esistenti senza alcun tuning aggiuntivo o overhead.

Propone un miglioramento degli approcci ASGD esistenti a scaling di medie dimensioni utilizzando il momentum con SGD per il training asincrono attraverso un pool di lavoratori distribuiti.

Questo articolo affronta il problema della staleness del gradiente contro le prestazioni parallele nel training distribuito del deep learning, e propone un approccio per stimare i futuri parametri del modello negli slave per ridurre gli effetti della latenza di comunicazione.


This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.

Proponiamo un nuovo algoritmo di apprendimento delle deep neural network, che sblocca la dipendenza layer-wise della backpropagation.

Un paradigma di training alternativo per le DNI in cui il modulo ausiliario è addestrato per approssimare direttamente l'output finale del modello originale, offrendo benefici collaterali.

Descrive un metodo di training delle reti neurali senza blocco degli aggiornamenti.


\emph{Truncated Backpropagation Through Time} (truncated BPTT, \cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of \emph{Backpropagation Through Time} (BPTT \cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step.   However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce \emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling \cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.

Fornisce una versione unbiased della backpropagation troncata campionando le lunghezze di troncamento e ripesando di conseguenza.

Propone metodi di determinazione stocastica per i punti di troncamento nella backpropagation attraverso il tempo.

Una nuova approssimazione alla backpropagation attraverso il tempo per superare i carichi computazionali e di memoria che sorgono quando si deve imparare da lunghe sequenze.


Graph convolutional networks (GCNs) have been widely used for classifying graph nodes in the semi-supervised setting.  Previous works have shown that GCNs are vulnerable to the perturbation on adjacency and feature matrices of existing nodes. However, it is unrealistic to change the connections of  existing nodes in many applications, such as existing users in social networks. In this paper, we investigate methods attacking GCNs by adding fake nodes. A greedy algorithm is proposed to generate adjacency and feature matrices of fake nodes, aiming to minimize the classification accuracy on the existing ones. In additional, we introduce a discriminator to classify fake nodes from real nodes, and propose a Greedy-GAN algorithm to simultaneously update the discriminator and the attacker, to make fake nodes indistinguishable to the real ones.   Our non-targeted attack decreases the accuracy of GCN down to 0.10, and our targeted attack reaches a success rate of 0.99 for attacking the whole datasets, and 0.94 on average for attacking a single node.

Attacco non mirato e mirato su GCN aggiungendo nodi falsi

Gli autori propongono una nuova tecnica adversarial per aggiungere nodi "falsi" per ingannare un classificatore basato su GCN


Transfer learning aims to solve the data sparsity for a specific domain by applying information of another domain. Given a sequence (e.g. a natural language sentence), the transfer learning, usually enabled by recurrent neural network (RNN), represent the sequential information transfer. RNN uses a chain of repeating cells to model the sequence data. However, previous studies of neural network based transfer learning simply transfer the information across the whole layers, which are unfeasible for seq2seq and sequence labeling. Meanwhile, such layer-wise transfer learning mechanisms also lose the fine-grained cell-level information from the source domain.   In this paper, we proposed the aligned recurrent transfer, ART, to achieve cell-level information transfer. ART is in a recurrent manner that different cells share the same parameters. Besides transferring the corresponding information at the same position, ART transfers information from all collocated words in the source domain. This strategy enables ART to capture the word collocation across domains in a more flexible way. We conducted extensive experiments on both sequence labeling tasks (POS tagging, NER) and sentence classification (sentiment analysis). ART outperforms the state-of-the-arts over all experiments.

Transfer learning per sequenze tramite l'apprendimento per allineare le informazioni a livello di cell attraverso i domini.

L'articolo ha proposto di usare RNN/LSTM con allineamento di collocazione come un metodo di representation learning per transfer learning/domain adaptation in NLP.