Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.

Abbiamo ideato uno scaling adattativo delle loss per migliorare il training a precisione mista che supera i risultati dello stato dell'arte.

Proposta di un metodo adattivo di scaling delle loss durante la backpropagation per il training a precisione mista dove il fattore di scala viene deciso automaticamente per ridurre l'underflow.

Gli autori propongono un metodo per addestrare modelli in precisione FP16 che adotta un modo più elaborato per minimizzare l'underflow in ogni layer simultaneamente e automaticamente.


Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities.This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors.We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks.Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization.We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.

Presentiamo un nuovo approccio per imparare a prevedere insiemi con permutazione e cardinalità sconosciute usando reti neurali deep feed-forward.

Una formulazione per imparare la distribuzione su variabili di permutazione non osservabili basata su reti deep per il problema della predizione degli insiemi.


Foveation is an important part of human vision, and a number of deep networks have also used foveation.However, there have been few systematic comparisons between foveating and non-foveating deep networks, and between different variable-resolution downsampling methods.Here we define several such methods, and compare their performance on ImageNet recognition with a Densenet-121 network.The best variable-resolution method slightly outperforms uniform downsampling.Thus in our experiments, foveation does not substantially help or hinder object recognition in deep networks.

Confrontiamo le prestazioni di object recognition su immagini sottocampionate in modo uniforme e con tre diversi schemi di foveazione.


We explore the concept of co-design in the context of neural network verification.Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily.To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task.We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones.Then, improving ReLU stability leads to an additional 4-13x speedup in verification times.An important feature of our methodology is its "universality," in the sense that it can be used with a broad range of training procedures and verification approaches.

Sviluppiamo metodi per addestrare modelli neurali deep che sono robusti alle perturbazioni avversarie e la cui robustezza è significativamente più facile da verificare.

L'articolo presenta diversi modi per regolarizzare le reti ReLU semplici per ottimizzare la robustezza avversaria, la robustezza avversaria dimostrabile e la velocità di verifica.

Questo articolo propone metodi per addestrare reti neurali robuste che possono essere verificate più velocemente, utilizzando metodi di pruning per incoraggiare la sparsità dei pesi e la regolarizzazione per incoraggiare la stabilità ReLU.


Batch Normalization (BatchNorm) has shown to be effective for improving and accelerating the training of deep neural networks.However, recently it has been shown that it is also vulnerable to adversarial perturbations.In this work, we aim to investigate the cause of adversarial vulnerability of the BatchNorm.We hypothesize that the use of different normalization statistics during training and inference (mini-batch statistics for training and moving average of these values at inference) is the main cause of this adversarial vulnerability in the BatchNorm layer.We empirically proved this by experiments on various neural network architectures and datasets.Furthermore, we introduce Robust Normalization (RobustNorm) and experimentally show that it is not only resilient to adversarial perturbation but also inherit the benefits of BatchNorm.

Indagine su come BatchNorm provoca la vulnerabilità avversaria e come evitarla.

Questo articolo affronta la vulnerabilità alle perturbazioni avversarie in BatchNorm, e propone un'alternativa chiamata RobustNorm, utilizzando il rescaling min-max invece della normalizzazione.

Questo articolo indaga la ragione dietro la vulnerabilità di BatchNorm e propone Robust Normalization, un metodo di normalizzazione che raggiunge risultati significativamente migliori sotto una varietà di metodi di attacco.


Electronic Health Records (EHR) comprise of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality which become the major obstacles in drawing reliable downstream outcome.Despite greatly numbers of imputation methods are being proposed to tackle these issues, most of the existing methods ignore correlated features or temporal dynamics and entirely put aside the uncertainty.In particular, since the missing values estimates have the risk of being imprecise, it motivates us to pay attention to reliable and less certain information differently.In this work, we propose a novel variational-recurrent imputation network (V-RIN), which unified imputation and prediction network, by taking into account the correlated features, temporal dynamics, and further utilizing the uncertainty to alleviate the risk of biased missing values estimates.Specifically, we leverage the deep generative model to estimate the missing values based on the distribution among variables and a recurrent imputation network to exploit the temporal relations in conjunction with utilization of the uncertainty.We validated the effectiveness of our proposed model with publicly available real-world EHR dataset, PhysioNet Challenge 2012, and compared the results with other state-of-the-art competing methods in the literature.

La nostra rete di imputazione variazionale-recorrente (V-RIN) prende in considerazione le caratteristiche correlate, le dinamiche temporali e utilizza ulteriormente l'incertezza per alleviare il rischio di stime distorte dei valori mancanti.

Una imputation network dei dati mancanti per incorporare la correlazione, le relazioni temporali e l'incertezza dei dati per il problema della scarsità dei dati in EHRs, che produce un AUC più alto sui task di classificazione del tasso di mortalità.

L'articolo ha presentato un metodo che combina VAE e uncertainty aware GRU per l'imputazione sequenziale dei dati mancanti e la predizione dei risultati.


Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity.Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models.However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account.We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized.The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution.Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates.Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy.

Un metodo adattivo per la quantizzazione in virgola fissa delle reti neurali basato sull'analisi teorica piuttosto che sull'euristica.

Propone un metodo per quantizzare le reti neurali che permette di quantizzare i pesi con una precisione diversa a seconda della loro importanza, tenendo conto della loss.

L'articolo propone una tecnica per quantizzare i pesi di una rete neurale con profondità/precisione di bit variabile per ogni parametro.


We study the problem of learning permutation invariant representations that can capture containment relations.We propose training a model on a novel task: predicting the size of the symmetric difference between pairs of multisets, sets which may contain multiple copies of the same object.With motivation from fuzzy set theory, we formulate both multiset representations and how to predict symmetric difference sizes given these representations.We model multiset elements as vectors on the standard simplex and multisets as the summations of such vectors, and we predict symmetric difference as the l1-distance between multiset representations.We demonstrate that our representations more effectively predict the sizes of symmetric differences than DeepSets-based approaches with unconstrained object representations.Furthermore, we demonstrate that the model learns meaningful representations, mapping objects of different classes to different standard basis vectors.

Sulla base della teoria degli insiemi fuzzy, proponiamo un modello che, date solo le dimensioni delle differenze simmetriche tra coppie di multiset, impara le rappresentazioni di tali multiset e dei loro elementi.

Questo articolo propone un nuovo task di apprendimento degli insiemi, prevedendo la dimensione della differenza simmetrica tra più insiemi, e fornisce un metodo per risolvere il task basato sulla teoria degli insiemi fuzzy.


It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system).In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold a relevant information.  Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice.This is primarily due to the heavy cognitive loads required for human agents to reason and report this high dimensional information.Consider the example where we are interested in building an image classifier via first collecting a certain category of  high-dimensional image data.While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \sim p(x)$ from agents.This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents.The challenge to do so is to design an incentive-compatible score function to score each reported sample to induce truthful reports, instead of an arbitrary or even adversarial one.We show that with accurate estimation of a certain $f$-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.We then present an efficient estimator with theoretical guarantee via studying the variational forms of $f$-divergence function.Our work complements the literature of information elicitation via introducing the problem of \emph{sample elicitation}.  We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.

Questo articolo propone un metodo basato su deep learning per ottenere campioni credibili da agenti auto-interessati.

Gli autori propongono un quadro di elicitazione del campione per il problema di elicitare campioni credibili dagli agenti per distribuzioni complesse, suggeriscono che i quadri neurali deep possono essere applicati in questo quadro, e collegano l'elicitazione del campione e f-GAN.

Questo articolo studia il problema dell'elicitazione del campione, proponendo un approccio di apprendimento deep che si basa sull'espressione duale della f-divergenza che scrive come massimo su un insieme di funzioni t.


The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks.However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence.To address this challenge, we introduce a general end-to-end graph-to-sequence neural encoder-decoder architecture that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs.Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.

Grapht to Sequence Learning con reti neurali basate su attention

Un'architettura graph2seq che combina un encoder di grafi che mescola componenti GGNN e GCN con un encoder di sequenze con attention e che mostra miglioramenti rispetto alle baseline.

Questo lavoro propone un encoder di grafi end-to-end a modelli di decoder di sequenze con un meccanismo di attention nel mezzo.


We address the problem of learning to discover 3D parts for objects in unseen categories.Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches.Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion.At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories.On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples.Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.

Un framework di segmentazione zero-shot per la segmentazione di parti di oggetti 3D. Modellare la segmentazione come un processo decisionale e risolvere come un contextual bandit problem

Un metodo per la segmentazione di nuvole di punti 3D di oggetti in parti componenti, focalizzato sulla generalizzazione dei raggruppamenti di parti a nuove categorie di oggetti non visti durante il training, che mostra alte prestazioni rispetto alle baseline.

Questo articolo propone un metodo per la segmentazione di parti in nuvole di punti di oggetti.


This paper presents the ballistic graph neural network.Ballistic graph neural network tackles the weight distribution from a transportation perspective and has many different properties comparing to the traditional graph neural network pipeline.The ballistic graph neural network does not require to calculate any eigenvalue.The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$).We use a perturbed coin operator to perturb and optimize the diffusion rate.Our results show that by selecting the diffusion speed, the network can reach a similar accuracy with fewer parameters.We also show the perturbed filters act as better representations comparing to pure ballistic ones.We provide a new perspective of training graph neural network, by adjusting the diffusion rate, the neural network's performance can be improved.

Una nuova prospettiva su come raccogliere la correlazione tra i nodi basata sulle proprietà di diffusione.

Una nuova operazione di diffusione per le graph neural networks che non richiede il calcolo degli autovalori e può propagarsi esponenzialmente più velocemente rispetto alle graph neural networks tradizionali.

L'articolo propone di affrontare il problema della velocità di diffusione introducendo la camminata balistica.


In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources.Empirically, we consider two sources of weak supervision signals, unsupervised ranking functions and semantic feature similarities.We train a BERT-based passage-ranking model (which achieves new state-of-the-art performances on two benchmark datasets with full supervision) in our weak supervision framework.Without using ground-truth training labels, BERT-PR models outperform BM25 baseline by a large margin on all three datasets and even beat the previous state-of-the-art results with full supervision on two of datasets.

Proponiamo una pipeline di training con weak supervision basata sul framework di programmazione dei dati per task di classificazione, in cui addestriamo un modello di classificazione basato su BERT e stabiliamo il nuovo state-of-the-art.

Gli autori propongono una combinazione di BERT e del framework di supervisione debole per affrontare il problema del passage ranking, ottenendo risultati migliori dello stato dell'arte fully supervised.


We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.We demonstrate a very universal Frequency Principle (F-Principle) --- DNNs often fit target functions from low to high frequencies --- on high-dimensional benchmark datasets, such as MNIST/CIFAR10, and deep networks, such as VGG16.This F-Principle of DNNs is opposite to the learning behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibits faster convergence for higher frequencies, for various scientific computing problems.With a naive theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions.The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function.This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.

Nei problemi reali, abbiamo trovato che le DNN spesso adattano le funzioni target dalle basse alle alte frequenze durante il processo di training.

Questo articolo analizza la loss delle reti neurali nel dominio di Fourier e trova che le DNN tendono ad imparare le componenti a bassa frequenza prima di quelle ad alta frequenza.

L'articolo studia il processo di training delle NN attraverso l'analisi di Fourier, concludendo che le NN imparano le componenti a bassa frequenza prima di quelle ad alta frequenza.


The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties.Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization.In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph.Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule.We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.

Proponiamo un encoder-decoder multi-risoluzione e hierarchically coupled per graph-graph translation.

Un modello gerarchico di graph-graph translation per generare grafi molecolari usando sottostrutture chimiche come elementi costitutivi che è completamente autoregressivo e impara rappresentazioni multirisoluzione coerenti, superando i modelli precedenti.

Gli autori presentano un metodo gerarchico di graph-to-graph translation per generare nuove molecole organiche.


Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping.Even though some combinations of transformations might never appear (e.g. an upright face with a horizontal nose), current equivariant architectures consider the set of all possible transformations in a transformation group when learning feature representations.Contrarily, the human visual system is able to attend to the set of relevant transformations occurring in the environment and utilizes this information to assist and improve object recognition.Based on this observation, we modify conventional equivariant feature mappings such that they are able to attend to the set of co-occurring transformations in data and generalize this notion to act on groups consisting of multiple symmetries.We show that our proposed co-attentive equivariant neural networks consistently outperform conventional rotation equivariant and rotation & reflection equivariant neural networks on rotated MNIST and CIFAR-10.

Utilizziamo attention per limitare le reti neurali equivarianti all'insieme o alle trasformazioni co-occorrenti nei dati.

Questo articolo combina l'attention con l'equivarianza di gruppo, in particolare per il gruppo p4m di rotazioni, traslazioni e capovolgimenti, e deriva una forma di self-attention che non distrugge la proprietà di equivarianza.

Gli autori propongono un meccanismo di auto-attention per le reti neurali roto-equivarianti che migliora le prestazioni di classificazione rispetto alle reti roto-equivarianti regolari.


The fast generation and refinement of protein backbones would constitute a major advancement to current methodology for the design and development of de novo proteins.In this study, we train Generative Adversarial Networks (GANs) to generate fixed-length full-atom protein backbones, with the goal of sampling from the distribution of realistic 3-D backbone fragments.We represent protein structures by pairwise distances between all backbone atoms, and present a method for directly recovering and refining the corresponding backbone coordinates in a differentiable manner.We show that interpolations in the latent space of the generator correspond to smooth deformations of the output backbones, and that test set structures not seen by the generator during training exist in its image.Finally, we perform sequence design, relaxation, and ab initio folding of a subset of generated structures, and show that in some cases we can recover the generated folds after forward-folding.Together, these results suggest a mechanism for fast protein structure refinement and folding using external energy functions.

Addestriamo una GAN per generare e recuperare backbone di proteine full-atom, e mostriamo che in casi selezionati possiamo recuperare le proteine generate dopo la progettazione della sequenza e il forward-folding ab initio.

Un modello generativo per proteine backbone che utilizza una GAN, una rete di tipo autoencoder e un processo di raffinamento, e una serie di valutazioni qualitative che suggeriscono risultati positivi.

Questo articolo presenta un approccio end-to-end per la generazione di proteine backbone utilizzando generative adversarial networks.


Few-Shot Learning (learning with limited labeled data) aims to overcome the limitations of traditional machine learning approaches which require thousands of labeled examples to train an effective model.Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning.The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks.Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available.In this paper, we propose a novel meta-learning paradigm wherein a few-shot learning model is learnt, which simultaneously overcomes domain shift between the train and test tasks via adversarial domain adaptation.We demonstrate the efficacy the proposed method through extensive experiments.

Il Meta Learning per few-shot learning presuppone che i task di training e i task di test siano tratti dalla stessa distribuzione. Cosa fare se non lo sono? Meta Learning con domain adaptation a livello di task!

Questo articolo propone un modello che combina unsupervised adversarial domain adaptation con le prototypical networks che esegue meglio delle baseline few-shot su task di apprendimento few-shot con domain-shift

Gli autori hanno proposto il meta domain adaptation per affrontare lo scenario del domain shift nella configurazione del meta learning, dimostrando miglioramenti delle prestazioni in diversi esperimenti.


Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich and complex probabilistic models.However, this expressiveness comes at the cost of substantially complicating the process of drawing inferences from the model.In particular, inference can become challenging when the support of the model varies between executions.Though general-purpose inference engines have been designed to operate in such settings, they are typically inefficient, often relying on proposing from the prior to make transitions.To address this, we introduce a new inference framework: Divide, Conquer, and Combine (DCC).DCC divides the program into separate straight-line sub-programs, each of which has a fixed support allowing more powerful inference algorithms to be run locally, before recombining their outputs in a principled fashion.We show how DCC can be implemented as an automated and general-purpose PPS inference engine, and empirically confirm that it can provide substantial performance improvements over previous approaches.

Divide, Conquer, and Combine è un nuovo schema di inferenza che può essere eseguito sui programmi probabilistici con supporto stocastico, cioè l'esistenza stessa delle variabili è stocastica.


Detecting communities or the modular structure of real-life networks (e.g. a socialnetwork or a product purchase network) is an important task because the way anetwork functions is often determined by its communities.The traditional approaches to community detection involve modularity-based approaches,which generally speaking, construct partitions based on heuristics thatseek to maximize the ratio of the edges within the partitions to those betweenthem.Node embedding approaches, which represent each node in a graph as areal-valued vector, transform the problem of community detection in a graph tothat of clustering a set of vectors.Existing node embedding approaches are primarilybased on first initiating uniform random walks from each node to constructa context of a node and then seeks to make the vector representation ofthe node close to its context.However, standard node embedding approaches donot directly take into account the community structure of a network while constructingthe context around each node.To alleviate this, we explore two differentthreads of work.First, we investigate the use of biased random walks (specifically,maximum entropy based walks) to obtain more centrality preserving embeddingof nodes, which we hypothesize may lead to more effective clusters in the embeddedspace.Second, we propose a community structure aware node embeddingapproach where we incorporate modularity-based partitioning heuristics intothe objective function of node embedding.We demonstrate that our proposed approachfor community detection outperforms a number of modularity-based baselinesas well as K-means on a standard node-embedded vector space (specifically,node2vec) on a wide range of real-life networks of different sizes and densities.

Un algoritmo di embedding dei nodi che preserva la comunità e che risulta in un rilevamento più efficace delle comunità con un clustering sullo spazio degli embedding


A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry.However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds.This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts.Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points.Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations.Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation.Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.

Un modello di apprendimento deep autoregressivo per la generazione di nuvole di punti diversi.

Un approccio per la generazione di forme 3D come nuvole di punti che considera l'ordinamento lessicografico dei punti secondo le coordinate e addestra un modello per prevedere i punti in ordine.

L'articolo introduce un modello generativo per le nuvole di punti utilizzando un modello auto-regressivo sui pixel simile a RNN e un modello di attention per gestire le interazioni a lungo raggio.


Reinforcement learning and evolutionary algorithms can be used to create sophisticated control solutions.Unfortunately explaining how these solutions work can be difficult to due to their "black box" nature.In addition, the time-extended nature of control algorithms often prevent direct applications of explainability techniques used for standard supervised learning algorithms.This paper attempts to address explainability of blackbox control algorithms through six different techniques:1) Bayesian rule lists,2) Function analysis,3) Single time step integrated gradients,4) Grammar-based decision trees,5) Sensitivity analysis combined with temporal modeling with LSTMs, and6) Explanation templates.These techniques are tested on a simple 2d domain, where a simulated rover attempts to navigate through obstacles to reach a goal.For control, this rover uses an evolved multi-layer perception that maps an 8d field of obstacle and goal sensors to an action determining where it should go in the next time step.Results show that some simple insights in explaining the neural network are possible, but that good explanations are difficult.

Descrive una serie di tecniche di explainability applicate a un semplice controller con rete neurale utilizzato per la navigazione.

Questo articolo fornisce intuizioni e spiegazioni sul problema di fornire spiegazioni per un perceptron multilayer usato come controller inverso per il movimento del rover, e idee su come spiegare un modello black-box.


The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal.In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress.We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components.Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set).Code is available at https://github.com/chihyaoma/selfmonitoring-agent.

Proponiamo un agente di auto-monitoraggio per il task di navigazione con visione e lingua.

Un metodo per la vision+language navigation che tiene traccia dei progressi sull'istruzione usando un monitor di progresso e un modulo di co-grounding visivo-testuale, e ha buone prestazioni sui benchmark standard.

Questo articolo descrive un modello per la vision-and-language navigation con un'attention visiva panoramica e una loss ausiliaria di monitoraggio del progresso, dando risultati allo stato dell'arte.


Environments in Reinforcement Learning (RL) are usually only partially observable.To address this problem, a possible solution is to provide the agent with information about past  observations.While common methods represent this history using a Recurrent Neural Network (RNN), in this paper we propose an alternative representation which is based on the record of the past events observed in a given episode.Inspired by the human memory, these events describe only important changes in the environment and, in our approach, are automatically discovered using self-supervision. We evaluate our history representation method using two challenging RL benchmarks: some games of the Atari-57 suite and the 3D environment Obstacle Tower.Using these benchmarks we show the advantage of our solution with respect to common RNN-based approaches.

scoperta di eventi per rappresentare la storia dell'agente in RL

Gli autori studiano il problema di RL sotto impostazioni parzialmente osservate, e propongono una soluzione che utilizza una FFNN ma fornisce una rappresentazione della storia, superando PPO.

Questo articolo propone un nuovo modo di rappresentare la storia passata come input per un agente RL, mostrando di avere prestazioni migliori di PPO e di una variante RNN di PPO.


The unconditional generation of high fidelity images is a longstanding benchmarkfor testing the performance of image decoders.Autoregressive image modelshave been able to generate small images unconditionally, but the extension ofthese methods to large images where fidelity can be more readily assessed hasremained an open problem.Among the major challenges are the capacity to encodethe vast previous context and the sheer difficulty of learning a distribution thatpreserves both global semantic coherence and exactness of detail.To address theformer challenge, we propose the Subscale Pixel Network (SPN), a conditionaldecoder architecture that generates an image as a sequence of image slices of equalsize.The SPN compactly captures image-wide spatial dependencies and requires afraction of the memory and the computation.To address the latter challenge, wepropose to use multidimensional upscaling to grow an image in both size and depthvia intermediate stages corresponding to distinct SPNs.We evaluate SPNs on theunconditional generation of CelebAHQ of size 256 and of ImageNet from size 32to 128.We achieve state-of-the-art likelihood results in multiple settings, set upnew benchmark results in previously unexplored settings and are able to generatevery high fidelity large scale samples on the basis of both datasets.

Mostriamo che i modelli autoregressivi possono generare immagini ad alta fedeltà.

Un'architettura che utilizza componenti decoder, size-upscaling decoder e depth-upscaling decoder per affrontare il problema dell'apprendimento delle dipendenze a lungo raggio nelle immagini al fine di ottenere immagini ad alta fedeltà.

Questo articolo affronta il problema della generazione di immagini ad alta fedeltà, mostrando con successo campioni convincenti di Imagenet con risoluzione 128x128 per un likelihood density model.


Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents.This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects.By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics.We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning.The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.

Un modello di spazio di stato gerarchico deep in cui le transizioni di stato degli oggetti correlati sono coordinate da graph neural networks.

Un modello gerarchico a variabili latenti di processi dinamici sequenziali di oggetti multipli quando ogni oggetto presenta una stocasticità significativa.

L'articolo presenta un modello di spazio di stato relazionale che simula le transizioni di stato congiunte di oggetti correlati che sono gerarchicamente coordinati in una struttura a grafo.


Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses).When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed.While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents.This paper proposes to add such inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated.Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

Introduciamo un nuovo bias induttivo che integra le strutture ad albero nelle reti neurali ricorrenti.

Questo articolo propone ON-LSTM, una nuova unità RNN che integra la struttura ad albero latente nei modelli ricorrenti e che ha buoni risultati nella modellazione del linguaggio, nel parsing non supervisionato, nella valutazione sintattica mirata e nell'inferenza logica.


Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures.A completely satisfactory explanation for their success remains elusive.Here, we present a novel explanation for the benefits of skip connections in training very deep networks.The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model.Several such singularities have been identified in previous works:(i) overlap singularities caused by the permutation symmetry of nodes in a given layer,(ii) elimination singularities corresponding to the elimination, i.e. consistent deactivation, of nodes,(iii) singularities generated by the linear dependence of the nodes.These singularities cause degenerate manifolds in the loss landscape that slow down learning.We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent.Moreover, for typical initializations, skip connections move the network away from the "ghosts" of these singularities and sculpt the landscape around them to alleviate the learning slow-down.These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.

Varietà degeneri derivanti dalla non identificabilità del modello rallentano l'apprendimento nelle reti deep; le skip connections aiutano rompendo le degenerazioni.

Gli autori mostrano che le singolarità di eliminazione e le singolarità di sovrapposizione impediscono l'apprendimento nelle reti neurali deep, e dimostrano che le skip connections possono ridurre la prevalenza di queste singolarità, accelerando l'apprendimento.

Il documento esamina l'uso delle skip connections nelle reti deep come un modo per alleviare le singolarità nella matrice Hessiana durante l'allenamento.


Representation learning is a central challenge across a range of machine learning areas.In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.Most prior work on representation learning has focused on generative approaches, learning representations that capture all the underlying factors of variation in the observation space in a more disentangled or well-ordered manner.In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable".These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, eliminating the need for explicit reconstruction.We show how these learned representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks.We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.

Apprendimento di rappresentazioni di stato che catturano i fattori necessari per il controllo

Un approccio al representation learning nel contesto del reinforcement learning che distingue due fasi in termini di azioni necessarie per raggiungerle.

L'articolo presenta un metodo per imparare rappresentazioni in cui la vicinanza in distanza euclidea rappresenta stati che sono raggiunti da policy simili.


We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks.  This setting corresponds to that which human learners face as they acquire domain expertise, for example, as an individual reads a textbook chapter-by-chapter.Through simulations involving sequences of 10 related tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming domain experts.We observed two key phenomena.First, forward facilitation---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, backward interference---the forgetting of the n previous tasks when learning task n+1---diminishes with n.  Forward facilitation is the goal of research on metalearning, and reduced backward interference is the goal of research on ameliorating catastrophic forgetting.We find that both of these goals are attained simply through broader exposure to a domain.

Studiamo il comportamento di una CNN mentre impara nuovi task conservando la padronanza per i task precedentemente appresi


We demonstrate a low effort method that unsupervisedly constructs task-optimized embeddings from existing word embeddings to gain performance on a supervised end-task.This avoids additional labeling or building more complex model architectures by instead providing specialized embeddings better fit for the end-task(s).Furthermore, the method can be used to roughly estimate whether a specific kind of end-task(s) can be learned form, or is represented in, a given unlabeled dataset, e.g. using publicly available probing tasks.We evaluate our method for diverse word embedding probing tasks and by size of embedding training corpus -- i.e. to explore its use in reduced (pretraining-resource) settings.

Morty modifica pretrained word embeddings per: (a) migliorare le prestazioni complessive degli embedding (per setting multi-task) o migliorare le prestazioni single-task, richiedendo solo uno sforzo minimo.


Data augmentation is commonly used to encode invariances in learning methods.However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set.The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply.In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset.We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.

Aumentando selettivamente i punti difficili da classificare si ottiene un training efficiente.

Gli autori studiano il problema dell'identificazione delle strategie di sottocampionamento per l'aumento dei dati e propongono strategie basate sull'influenza e la loss del modello, così come il benchmarking empirico dei metodi proposti.

Gli autori propongono di usare metodi basati sull'influenza o sulla loss per selezionare un sottoinsieme di punti da usare per aumentare i set di dati per il training di modelli in cui la loss è additiva sui data point.


Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules.We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules.Modeling the entire process of constructing a molecule during generation offers a number of advantages.First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions.Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes.Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product.

Un modello generativo deep per le molecole organiche che genera prima i blocchi di costruzione dei reagenti prima di combinarli usando un predittore di reazione.

Un modello generativo molecolare che genera molecole attraverso un processo a due fasi che fornisce percorsi di sintesi delle molecole generate, permettendo agli utenti di esaminare l'accessibilità sintetica dei composti generati.


Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks.  However, they have no inherent capability to recognize when their predictions might go wrong.There have been several efforts in the recent past to detect natural errors i.e.  misclassified inputs but these mechanisms pose additional energy requirements.  To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way.  We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs).   The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs.The combined confidence of RACs is utilized to determine if classification should terminate at an early stage.We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet.Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.

Migliorare la robustezza e l'efficienza energetica di una deep neural network usando le rappresentazioni nascoste.

Questo articolo mira a ridurre gli errori di classificazione delle reti neurali deep in un modo efficiente dal punto di vista energetico aggiungendo celle ausiliarie basate su caratteristiche rilevanti dopo uno o più hidden layer per decidere se terminare la classificazione in anticipo.


Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred.To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping.Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained.We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities.For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.

Comprendere la struttura della rappresentazione dei knowledge graph usando l'idea dei word embedding.

Questo articolo cerca di capire la struttura latente che sta alla base dei metodi di incorporazione dei knwoledge graph, e dimostra che la capacità di un modello di rappresentare un tipo di relazione dipende dai limiti dell'architettura del modello rispetto alle condizioni di relazione.

Questo articolo propone uno studio dettagliato sulla explainability dei modelli di predizione dei link (LP) utilizzando una recente interpretazione dei word embeddings per fornire una migliore comprensione delle prestazioni dei modelli LP.


Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements.For example, air quality monitoring system records PM2.5, CO, etc.The resulting time-series data often has missing values due to device outages or communication errors.In order to impute the missing values, state-of-the-art methods are built on Recurrent Neural Networks (RNN), which process each time stamp sequentially, prohibiting the direct modeling of the relationship between distant time stamps.Recently, the self-attention mechanism has been proposed for sequence modeling tasks such as machine translation, significantly outperforming RNN because the relationship between each two time stamps can be modeled explicitly.In this paper, we are the first to adapt the self-attention mechanism for multivariate, geo-tagged time series data.In order to jointly capture the self-attention across different dimensions (i.e. time, location and sensor measurements) while keep the size of attention maps reasonable, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.On three real-world datasets, including one our newly collected NYC-traffic dataset, extensive experiments demonstrate the superiority of our approach compared to state-of-the-art methods for both imputation and forecasting tasks.

Un nuovo meccanismo di self-attention per l'imputazione di serie temporali multivariate e geo-taggate.

Questo articolo propone il problema di applicare i transformer ai dati spazio-temporali in un modo computazionalmente efficiente, e studia i modi di implementare l'attention 3D.

Questo articolo studia empiricamente l'efficacia dei transformer per l'imputazione dei dati delle serie temporali attraverso le dimensioni dell'input.


The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software.This work focuses on improving the quality of scanned documents in order to improve the OCR output.We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones.Deep neural network based denoising auto-encoders are trained to improve the OCR quality.We train a blind model that works on different noise levels of scanned text documents.Results are shown for blurring and watermark noise removal from noisy scanned documents.

Abbiamo progettato e testato un REDNET (ResNet Encoder-Decoder) con 8 skip connections per rimuovere il rumore dai documenti, compresa la sfocatura e le filigrane, ottenendo una rete deep ad alte prestazioni per la pulizia delle immagini dei documenti.


The existence of adversarial examples, or intentional mis-predictions constructed from small changes to correctly predicted examples, is one of the most significant challenges in neural network research today.Ironically, many new defenses are based on a simple observation - the adversarial inputs themselves are not robust and small perturbations to the attacking input often recover the desired prediction.While the intuition is somewhat clear, a detailed understanding of this phenomenon is missing from the research literature.This paper presents a comprehensive experimental analysis of when and why perturbation defenses work and potential mechanisms that could explain their effectiveness (or ineffectiveness) in different settings.

Identifichiamo una famiglia di tecniche di difesa e mostriamo che sia la compressione lossy deterministica che le perturbazioni randomizzate all'input portano a guadagni simili nella robustezza.

Questo articolo discute i modi di destabilizzare un dato attacco avversario, cosa rende le immagini avversarie non robuste, e se è possibile per gli attaccanti usare un modello universale di perturbazioni per rendere i loro esempi avversari robusti contro tali perturbazioni.

L'articolo studia la robustezza degli attacchi avversari alle trasformazioni del loro input.


There is no consensus yet on the question whether adaptive gradient methods like Adam are easier to use than non-adaptive optimization methods like SGD.In this work, we fill in the important, yet ambiguous concept of â€˜ease-of-useâ€™ by defining an optimizerâ€™s tunability:  How easy is it to find good hyperparameter configurations using automatic random hyperparameter search?We propose a practical and universal quantitative measure for optimizer tunability that can form the basis for a fair optimizer benchmark.  Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, we find  that Adam is the most tunable for the majority of problems, especially with a low budget for hyperparameter tuning.

Forniamo un metodo per il benchmark degli ottimizzatori che è consapevole del processo di tuning degli iperparametri.

Introduzione di una nuova metrica per catturare l'adattabilità di un ottimizzatore, e un confronto empirico completo degli ottimizzatori di deep learning sotto diverse quantità di tuning degli iperparametri.

Questo articolo introduce una semplice misura di adattabilità che permette di confrontare gli ottimizzatori sotto vincoli di risorse, scoprendo che il tuning del learning rate dell'ottimizatore Adam è la più facile per trovare configurazioni di iperparametri ben performanti.


The phase problem in diffraction physics is one of the oldest inverse problems in all of science.The central difficulty that any approach to solving this inverse problem must overcome is that half of the information, namely the phase of the diffracted beam, is always missing.In the context of electron microscopy, the phase problem is generally non-linear and solutions provided by phase-retrieval techniques are known to be poor approximations to the physics of electrons interacting with matter.Here, we show that a semi-supervised learning approach can effectively solve the phase problem in electron microscopy/scattering.In particular, we introduce a new Deep Neural Network (DNN), Y-net, which simultaneously learns a reconstruction algorithm via supervised training in addition to learning a physics-based regularization via unsupervised training.We demonstrate that this constrained, semi-supervised approach is an order of magnitude more data-efficient and accurate than the same model trained in a purely supervised fashion.In addition, the architecture of the Y-net model provides for a straightforward evaluation of the consistency of the model's prediction during inference and is generally applicable to the phase problem in other settings.

Introduciamo una deep neural network semi-supervised per approssimare la soluzione del problema di fase nella microscopia elettronica


Word embeddings extract semantic features of words from large datasets of text.Most embedding methods rely on a log-bilinear model to predict the occurrenceof a word in a context of other words.Here we propose word2net, a method thatreplaces their linear parametrization with neural networks.For each term in thevocabulary, word2net posits a neural network that takes the context as input andoutputs a probability of occurrence.Further, word2net can use the hierarchicalorganization of its word networks to incorporate additional meta-data, such assyntactic features, into the embedding model.For example, we show how to shareparameters across word networks to develop an embedding model that includespart-of-speech information.We study word2net with two datasets, a collectionof Wikipedia articles and a corpus of U.S. Senate speeches.Quantitatively, wefound that word2net outperforms popular embedding methods on predicting held-out words and that sharing parameters based on part of speech further boostsperformance.Qualitatively, word2net learns interpretable semantic representationsand, compared to vector-based methods, better incorporates syntactic information.

Word2net è un nuovo metodo per l'apprendimento di rappresentazioni di parole tramite reti neurali che possono usare le informazioni sintattiche per imparare migliori caratteristiche semantiche.

Questo articolo estende SGNS con un cambiamento dell'architettura da un modello bag-of-words a un modello feedforward, e contribuisce a una nuova forma di regolarizzazione vincolando un sottoinsieme di layer tra diverse reti associate.

Un metodo per utilizzare la combinazione non lineare di vettori di contesto per l'apprendimento della rappresentazione vettoriale delle parole, dove l'idea principale è quella di sostituire ogni word embedding con una rete neurale.


A key goal in neuroscience is to understand brain mechanisms of cognitive functions.An emerging approach is to study â€œbrain statesâ€ dynamics using functional magnetic resonance imaging (fMRI).So far in the literature, brain states have typically been studied using 30 seconds of fMRI data or more, and it is unclear to which extent brain states can be reliably identified from very short time series.In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used.Starting with a populational brain graph with nodes defined by a parcellation of cerebral cortex and the adjacent matrix extracted from functional connectome, GCN takes a short series of fMRI volumes as input, generates high-level domain-specific graph representations, and then predicts the corresponding cognitive state.We investigated the performance of this GCN "cognitive state annotation" in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution task fMRI data.Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 89% (chance level 4.8%).As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable as a base model for other transfer learning applications, for instance, adapting to new task domains.

Utilizzando una finestra di 10s di segnali fMRI, il nostro modello GCN ha identificato 21 diverse condizioni di task dal dataset HCP con una precisione sul test set del 89%.


Modern deep neural networks (DNNs) require high memory consumption and large computational loads.  In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including the line of works on factorization methods.Factorization methods approximate the weight matrix of a DNN layer with multiplication of two or multiple low-rank matrices.However, it is hard to measure the ranks of DNN layers during the training process.Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step.The former approach usually induces a high accuracy loss while the latter prevents DNN factorization from efficiently reaching a high compression rate.In this work, we propose SVD training, which first applies SVD to decompose DNN's layers and then performs training on the full-rank decomposed weights.To improve the training quality and convergence, we add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding.Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer.Singular value pruning is applied at the end to reach a low-rank model.We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.

Induzione efficiente di reti neurali deep a basso rango tramite training via SVD con valori singolari sparsi e vettori singolari ortogonali.

Questo articolo introduce un approccio alla compressione della rete incoraggiando la matrice dei pesi in ogni layer ad avere un rango basso e fattorizzando esplicitamente le matrici dei pesi in una fattorizzazione simile a SVD per il trattamento come nuovi parametri.

Proposta di parametrizzare ogni layer di una deep neural network, prima del training, con una decomposizione di matrice a basso rango, di conseguenza sostituire le convoluzioni con due convoluzioni consecutive, e poi addestrare il metodo decomposto.


The recent rise in popularity of few-shot learning algorithms has enabled models to quickly adapt to new tasks based on only a few training samples.Previous few-shot learning works have mainly focused on classification and reinforcement learning. In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of appropriate basis functions.This enables a few labelled samples to approximate the function.We design a Feature Extractor network to encode basis functions for a task distribution, and a  Weights Generator to generate the weight vector for a novel task.We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.

Proponiamo un modello di few-shot learning che è fatto su misura per task di regressione

Questo articolo propone un nuovo metodo di few-shot learning per problemi di regressione su piccoli campioni.

Un metodo che impara un modello di regressione con pochi campioni e supera gli altri metodi.


Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes.However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input.These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive.We propose to address this issue by discriminative detection of OOD pixels in input data.Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task.Instead, we train a dedicated OOD modelwhich discriminates the primary training set from a much larger "background" dataset which approximates the variety of the visual world.We perform our experiments on high resolution natural images in a dense prediction setup.We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset.We evaluate our approach on WildDash test, which is currently the only public test dataset with out-of-distribution images.The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.

Presentiamo un nuovo approccio per rilevare i pixel out-of-distribution nella segmentazione semantica.

Questo articolo affronta il rilevamento di out-of-distribution per aiutare il processo di segmentazione, e propone un approccio di training di un classificatore binario che distingue le patch dell'immagine da un insieme di classi conosciute da quelle di una sconosciuta.

Questo documento mira a rilevare i pixel out-of-distribution per la segmentazione semantica, e questo lavoro utilizza i dati di altri domini per rilevare le classi indeterminate per modellare meglio l'incertezza.


Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices.Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead.To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs.However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large.The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results.It is difficult for even deep reinforcement learning (DRL) DDPG-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy.In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer.Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06%, and decrease the inference energy consumption by 50.69%, while achieving the same inference accuracy.

Quantizzazione accurata, veloce e automatizzata della rete neurale Kernel-Wise con precisione mista usando Hierarchical Deep Reinforcement Learning

Un metodo per quantizzare i pesi e le attivazioni delle reti neurali che utilizza reinforcement learning per selezionare la larghezza di bit per i singoli kernel in un layer e che raggiunge prestazioni migliori, o latenza, rispetto agli approcci precedenti.

Questo articolo propone di cercare automaticamente schemi di quantizzazione per ogni kernel nella rete neurale, usando RL gerarchico per guidare la ricerca.


Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems.However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models.Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from.This poses complexity to navigate this model space to find the right model for the data and the task.In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space.Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks.Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task.The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.

Gaggle, un sistema analitico visivo interattivo per aiutare gli utenti a navigare interattivamente nello spazio dei modelli per task di classificazione e ranking.

Un nuovo sistema analitico visivo che mira a permettere agli utenti non esperti di navigare in modo interattivo in uno spazio modello utilizzando un approccio basato sulla dimostrazione.

Un sistema di visual analytics che aiuta gli analisti alle prime armi a navigare nello spazio dei modelli nell'eseguire task di classificazione e ranking.


Chinese text classification has received more and more attention today.However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media.To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN).Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder.It improves the capability of Chinese text representation.Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures.Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues.The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.

Proponiamo una nuova rete di attention con un encoder hybrid per risolvere il problema della rappresentazione del testo per text classification in lingua cinese, specialmente i fenomeni linguistici sulle pronunce come il polifono e l'omofono.

Questo articolo propone un modello basato sull'attention composto da un encoder di parole e dall'encoder Pinyin per text classification in lingua cinese, ed estende l'architettura per l'encoder di caratteri Pinyin.

Proposta di una rete di attention in cui sia la parola che il pinyin sono considerati per la rappresentazione del cinese, con risultati migliori mostrati in diversi dataset per text classification.


Recent advances in learning from demonstrations (LfD) with deep neural networks have enabled learning complex robot skills that involve high dimensional perception such as raw image inputs. LfD algorithms generally assume learning from single task demonstrations.In practice, however, it is more efficient for a teacher to demonstrate a multitude of tasks without careful task set up, labeling, and engineering.Unfortunately in such cases, traditional imitation learning techniques fail to represent the multi-modal nature of the data, and often result in sub-optimal behavior.In this paper we present an LfD approach for learning multiple modes of behavior from visual data.Our approach is based on a stochastic deep neural network (SNN), which represents the underlying intention in the demonstration as a stochastic activation in the network.We present an efficient algorithm for training SNNs, and for learning with vision inputs, we also propose an architecture that associates the intention with a stochastic attention module.We demonstrate our method on real robot visual object reaching tasks, and show thatit can reliably learn the multiple behavior modes in the demonstration data.Video results are available at https://vimeo.com/240212286/fd401241b9.

imitation learning multimodale da dimostrazioni non strutturate utilizzando l'intenzione di modellazione di rete neurale stocastica.

Un nuovo approccio basato sul campionamento per l'inferenza nei modelli a variabile latente che si applica all'imitation learning multimodale e funziona meglio delle reti neurali deterministiche e delle reti neurali stocastiche per un task reale di robotica visiva.

Questo articolo mostra come apprendere diverse modalità utilizzando l'apprendimento per imitazione da dati visivi utilizzando imitation learning, e un metodo per imparare da dimostrazioni in cui sono date diverse modalità dello stesso task.


The interpretability of neural networks has become crucial for their applications in real world with respect to the reliability and trustworthiness.Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation.In natural language processing, this type of explanations is challenging for human user to understand the meaning of an explanation and draw the connection between explanation and model prediction, especially for long texts.In this work, we focus on detecting the interactions between features, and propose a novel approach to build a hierarchy of explanations based on feature interactions.The proposed method is evaluated with three neural classifiers, LSTM, CNN, and BERT, on two benchmark text classification datasets.The generated explanations are assessed by both automatic evaluation measurements and human evaluators.Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models, and understandable to humans.

Un nuovo approccio per costruire spiegazioni gerarchiche per text classification rilevando le interazioni delle feature.

Un nuovo metodo per fornire spiegazioni per le predizioni fatte dai classificatori di testo che supera le baseline sui punteggi di importanza a livello di parola, e una nuova metrica, la loss di coesione, per valutare l'importanza a livello di span.

Un metodo di interpretazione basato sulle interazioni delle feature e sul punteggio di importanza delle feature rispetto ai contributi indipendenti delle feature.


Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time.FBS introduces small auxiliary connections to existing convolutional layers.In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels.FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs.We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification.Experiments show that FBS can respectively provide 5Ã— and 2Ã— savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.

Facciamo funzionare più velocemente i layer convoluzionali potenziando e sopprimendo dinamicamente i canali nel calcolo delle feature.

Un metodo di potenziamento e soppressione delle caratteristiche per la potatura dinamica dei canali che predice l'importanza di ogni canale e poi usa una funzione affine per amplificare/sopprimere l'importanza del canale.

Proposta di un metodo di pruning dei canali per selezionare dinamicamente i canali durante il test.


We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training.Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than 0.5% classification layer connection density, or less than 5% overall network connection density.We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers.Based on our sparsifying technique, we introduce the `scatter' metric to characterize the quality of a particular connection pattern.As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.

Le reti neurali possono essere pre-definite per avere una connettività sparsa senza degradazione delle prestazioni.

Questo articolo esamina i modelli di connessione sparsi negli layer superiori delle reti di classificazione delle immagini convoluzionali, e introduce un'euristica per distribuire le connessioni tra finestre/gruppi e una misura chiamata scatter per costruire maschere di connettività.

Proposta di ridurre il numero di parametri appresi da una rete deep impostando pesi di connessione sparsi nei layer di classificazione, e introduzione di un concetto di "scatter".


Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important problems in the development of deep learning.While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms.In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks.After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods.Based on the evaluation results, we draw several important findings and provide insights for future research.

Forniamo un benchmark completo, rigoroso e coerente per valutare la robustezza avversaria dei modelli di deep learning.

Questo articolo presenta una valutazione di diversi tipi di modelli di classificazione sotto vari metodi di attacco avversario.

Uno studio empirico su larga scala che confronta diverse tecniche di attacco e difesa avversaria, e l'uso della precisione rispetto al budget di perturbazione e della precisione rispetto alle curve di forza dell'attacco per valutare attacchi e difese.


We propose a modification to traditional Artificial Neural Networks (ANNs), which provides the ANNs with new aptitudes motivated by biological neurons.  Biological neurons work far beyond linearly summing up synaptic inputs and then transforming the integrated information.  A biological neuron change firing modes accordingly to peripheral factors (e.g., neuromodulators) as well as intrinsic ones.  Our modification connects a new type of ANN nodes, which mimic the function of biological neuromodulators and are termed modulators, to enable other traditional ANN nodes to adjust their activation sensitivities in run-time based on their input patterns.  In this manner, we enable the slope of the activation function to be context dependent.  This modification produces statistically significant improvements in comparison with traditional ANN nodes in the context of Convolutional Neural Networks and Long Short-Term Memory networks.

Proponiamo una modifica alle reti neurali artificiali tradizionali motivata dalla biologia dei neuroni per permettere alla forma della funzione di attivazione di essere dipendente dal contesto.

Un metodo per scalare le attivazioni di un layer di neuroni in una ANN a seconda degli ingressi a quel layer che riporta miglioramenti al di sopra delle baseline.

Introduzione di un cambiamento nell'architettura dei neuroni di base in una rete neurale, e l'idea di moltiplicare l'uscita della combinazione lineare dei neuroni con un modulatore prima di passarla funzione di attivazione.


In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator.We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task.We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training.We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer.Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named "mix-review''.We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated.Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

Identifichiamo il problema del forgetting nel fine-tuning dei modelli pre-trained per NLG, e proponiamo la strategia mix-review per affrontarlo.

Questo articolo analizza il problema del forgetting nel framework del pretraining-finetuning dal punto di vista della sensibilità al contesto e del trasferimento della conoscenza, e propone una strategia di fine-tuning che supera il metodo del weight decay.

Studio del problema del forgetting nel framework pretrain-finetune, in particolare nei task di dialogue response generation, e proposta di una strategia di revisione mista per alleviare il problema del forgetting.


Combining domain knowledge models with neural models has been challenging.  End-to-end trained neural models often perform better (lower Mean Square Error) than domain knowledge models or domain/neural combinations, and the combination is inefficient to train.  In this paper, we demonstrate that by composing domain models with machine learning models, by using extrapolative testing sets, and invoking decorrelation objective functions, we create models which can predict more complex systems.The models are interpretable, extrapolative, data-efficient, and capture predictable but complex non-stochastic behavior such as unmodeled degrees of freedom and systemic measurement noise.  We apply this improved modeling paradigm to several simulated systems and an actual physical system in the context of system identification.   Several ways of composing domain models with neural models are examined for time series, boosting, bagging, and auto-encoding on various systems of varying complexity and non-linearity.  Although this work is preliminary, we show that the ability to combine models is a very promising direction for neural modeling.

Una migliore modellazione di sistemi complessi utilizza una composizione ibrida di modelli neurali/di dominio, nuove funzioni di loss di decorrelazione e test set estrapolativi

Questo articolo conduce esperimenti per confrontare le previsioni estrapolative di vari modelli ibridi che compongono modelli fisici, reti neurali e modelli stocastici, e affronta la sfida della dinamica non modellata che è un collo di bottiglia.

Questo articolo presenta approcci per la combinazione di reti neurali con modelli non-NN per prevedere il comportamento di sistemi fisici complessi.


Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning.In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning.Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data.Instantiating our framework results in a generalizable policy to unseen tasks.Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.

Impariamo i punteggi densi e il modello dinamico come priori dai dati di esplorazione e li usiamo per indurre una buona policy nei nuovi task in condizione zero-shot.

Questo articolo discute la generalizzazione dello zero shot in nuovi ambienti, e propone un approccio con risultati su Grid-World, Super Mario Bros, e 3D Robotics.

Un metodo che mira ad apprendere priori task-agnostic per la generalizzazione zero-shot, con l'idea di impiegare un approccio di modellazione in cima al framework di RL basato sul modello.


Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles.As a noticeable example, Stein variational gradient descent (SVGD) provides a deterministic and computationally efficient update, but it is known to underestimate the variance in high dimensions, the mechanism of which is poorly understood.In this work we explore a connection between SVGD and MMD-based inference algorithm via Stein's lemma.By comparing the two update rules, we identify the source of bias in SVGD as a combination of high variance and deterministic bias, and empirically demonstrate that the removal of either factors leads to accurate estimation of the variance.In addition, for learning high-dimensional Gaussian target, we analytically derive the converged variance for both algorithms, and confirm that only SVGD suffers from the "curse of dimensionality".

Analizzare i meccanismi sottostanti al collasso della varianza di SVGD in dimensioni elevate.


We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.  The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.  Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).  Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

Ripensare la generalizzazione richiede la rivisitazione di vecchie idee: approcci di meccanica statistica e comportamento di apprendimento complesso

Gli autori suggeriscono che le idee di meccanica statistica aiuteranno a capire le proprietà di generalizzazione delle reti neurali deep, e danno un approccio che fornisce forti descrizioni qualitative dei risultati empirici riguardanti le reti neurali deep e gli algoritmi di apprendimento.

Un insieme di idee legate alla comprensione teorica delle proprietà di generalizzazione delle reti neurali multilayer, e un'analogia qualitativa tra i comportamenti nel deep learning e i risultati dell'analisi fisica statistica quantitativa delle reti neurali a uno o due layer.


Computations for the softmax function in neural network models are expensive when the number of output classes is large.This can become a significant issue in both training and inference for such models.In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.During inference, our method quickly locates the most probable expert to compute small-scale softmax.Our method is learning-based and requires no knowledge of the output class partition space a priori.We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of performance.

Presentiamo una softmax doppiamente sparsa, la miscela sparsa di esperti sparsi, per migliorare l'efficienza dell'inferenza softmax attraverso lo sfruttamento della gerarchia a due livelli di sovrapposizione.

Questo articolo propone un'approssimazione veloce al calcolo di softmax quando il numero di classi è molto grande.

Questo articolo propone una miscela di esperti sparsi che impara una gerarchia di classi a due livelli per un'inferenza softmax efficiente.


Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.In this context, we assess the utility of observational supervision, where we take advantage of passively-collected signals such as eye tracking or â€œgazeâ€ data, to reduce the amount of hand-labeled data needed for model training.Specifically, we leverage gaze information to directly supervise a visual attention layer by penalizing disagreement between the spatial regions the human labeler looked at the longest and those that most heavily influence model output.We present evidence that constraining the model in this way can reduce the number of labeled examples required to achieve a given performance level by as much as 50%, and that gaze information is most helpful on more difficult tasks.

Esploriamo l'uso di dati di eye-tracking raccolti passivamente per ridurre la quantità di dati etichettati necessari durante la formazione.

Un metodo per utilizzare le informazioni sullo sguardo per ridurre la complessità del campione di un modello e lo sforzo di annotazione necessario per ottenere una performance target, con risultati migliori nei campioni di medie dimensioni e nei task più difficili.

Un metodo per incorporare i segnali di sguardo nelle CNN standard per la classificazione delle immagini, aggiungendo un termine di funzione di loss basato sulla differenza tra la Class Activation Map del modello e la mappa costruita dalle informazioni di eye tracking.


We study the robustness to symmetric label noise of GNNs training procedures.By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task.Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.

Applichiamo la correzione delle loss alle graph neural networks per addestrare un modello più robusto al rumore.

Questo articolo introduce la correzione delle loss per le reti neurali dei grafi per affrontare il rumore simmetrico delle label dei grafi, concentrandosi su un task di classificazione dei grafi.

Questo articolo propone l'uso di una loss di correzione del rumore nel contesto delle graph neural networks per affrontare le label rumorose.


Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs.Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them.We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark.Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.

Usiamo la co-attention dei grafi in un sistema di training dei grafi accoppiati per la classificazione e la regressione dei grafi.

Questo articolo inietta un meccanismo di co-attention a più teste in GCN che permette a un farmaco di osservare un altro farmaco durante la predizione degli effetti collaterali.

Un metodo per estendere l'apprendimento basato sui grafi con uno layer co-attenzionale, che supera altri precedenti su pairwise graph classification task.


In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.

Image captioning come un training di una GAN condizionale con nuove architetture, anche studiare due metodi discreti di training di GAN .

Un modello GAN migliorato per image captioning che propone un captioner LSTM context-aware, introduce un discriminatore co-attentive più forte con prestazioni migliori, e usa SCST per il training della GAN.