Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs).Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy.Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences.In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks.This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.

Sostituiamo alcuni percorsi dei gradienti nelle RNN gerarchiche con una loss ausiliaria. Mostriamo che questo può ridurre il costo della memoria preservando le prestazioni.

L'articolo introduce un'architettura RNN gerarchica che potrebbe essere addestrata in modo più efficiente.

Il documento proposto suggerisce di disaccoppiare i diversi livelli di gerarchia in RNN utilizzando loss ausiliarie.


In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations.In particular, we use singular value decomposition to analyze the relevant variations in feature space.These variations are formalized as the effective dimension of the embedding.We consider how the effective dimension varies across layers within class.We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation.Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.

Le reti neurali che fanno un buon lavoro di classificazione proiettano i punti in forme più sferiche prima di comprimerli in meno dimensioni.


Deep learning methods have achieved high performance in sound recognition tasks.Deciding how to feed the training data is important for further performance improvement.We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.We then input the mixed sound to the model and train the model to output the mixing ratio.The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisherâ€™s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.As a result, we achieved a performance surpasses the human level.

Proponiamo un nuovo metodo di deep sound recognition chiamato apprendimento BC.

Gli autori hanno definito un nuovo task di apprendimento che richiede una DNN per prevedere il rapporto di mistura tra i suoni di due classi diverse per aumentare il potere discriminatorio della rete finale.

Propone un metodo per migliorare le prestazioni di un metodo di apprendimento generico generando sample di training "tra le classi" e presenta l'intuizione di base e la necessità della tecnica proposta.


Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable.Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results.The problem could be exacerbated in black-box prediction models, such as deep neural networks.In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels.Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures.We evaluate our proposed method on forecasting temperatures in Los Angeles.

Proponiamo un metodo che deduce il livello di qualità dei dati variabili nel tempo per la previsione spazio-temporale senza label esplicitamente assegnate.

Introduce una nuova definizione di qualità dei dati che si basa sulla nozione di variazione locale definita in (Zhou e Scholkopf) e la estende a più fonti di dati eterogenee.

Questo lavoro ha proposto un nuovo modo di valutare la qualità delle diverse fonti di dati con il modello a grafo variabile nel tempo, con il livello di qualità usato come termine di regolarizzazione nella funzione obiettivo


Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.

Proponiamo programmi di 3D shape, una rappresentazione strutturata e composita delle forme. Il nostro modello impara a dedurre ed eseguire shape program per spiegare le forme 3D.

Un approccio per dedurre shape program dati modelli 3D, con un'architettura composta da una rete ricorrente che codifica una forma 3D e fornisce istruzioni, e un secondo modulo che renderizza il programma in 3D.

Questo articolo introduce una descrizione semantica di alto livello per le forme 3D, data dallo ShapeProgram.


Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention  thanks to its encouraging performance on a variety of control tasks.Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment.In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult.We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better.Our findings are further confirmed to be robust against the choice of training hyperparameters.We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough.We hope our study provides guidance for future practices in regularizing policy optimization algorithms.

Mostriamo che i metodi di regolarizzazione convenzionali (ad esempio, $L_2$, dropout), che sono stati ampiamente ignorati nei metodi RL, possono essere molto efficaci nell'ottimizzazione delle policy.

Gli autori studiano una serie di metodi di ottimizzazione della policy diretta esistenti nel campo del reinforcement learning e forniscono un'indagine dettagliata sull'effetto dei regolamenti sulle prestazioni e sul comportamento degli agenti che seguono questi metodi.

Questo articolo fornisce uno studio sull'effetto della regolarizzazione sulle prestazioni in ambienti di training in metodi di ottimizzazione delle policy in task di controllo continuo multipli.


We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images.The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection.To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives.In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements.We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline.Preliminary results indicate that the task poses a significant machine learning challenge.We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

Presentiamo un dataset di question-answering, FigureQA, come un primo passo verso lo sviluppo di modelli che possono riconoscere intuitivamente i pattern dalle rappresentazioni visive dei dati.

Questo articolo introduce un dataset di question-answering con template sulle figure, che coinvolgono il ragionamento sugli elementi delle figure.

L'articolo introduce un nuovo dataset di visual reasoning chiamato Figure-QA che consiste in 140K immagini di figure e 1.55M di coppie QA, che può aiutare nello sviluppo di modelli che possono estrarre informazioni utili dalle rappresentazioni visive dei dati.


In this paper, I discuss some varieties of explanation that can arisein intelligent agents.I distinguish between process accounts, whichaddress the detailed decisions made during heuristic search, andpreference accounts, which clarify the ordering of alternativesindependent of how they were generated.I also hypothesize which types of users will appreciate which types of explanation.In addition, I discuss three facets of multi-step decision making-- conceptual inference, plan generation, and plan execution --in which explanations can arise.I also consider alternative waysto present questions to agents and for them provide their answers.

Questo position paper analizza diversi tipi di self explanation che possono sorgere nella pianificazione e nei sistemi correlati.

Discute diversi aspetti delle explanation, in particolare nel contesto del sequential decision making.


Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details.Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery.To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion:(i) co-registration,(ii) fusion,(iii) up-sampling, and(iv) registration-at-the-loss.Co-registration of low-res views is learned implicitly through a reference-frame channel, with no explicit registration mechanism.We learn a global fusion operator that is applied recursively on an arbitrary number of low-res pairs.We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet.We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth observation data at scale.Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.

Il primo approccio di deep learning a MFSR per risolvere la registrazione, fusione, up-sampling in modo end-to-end.

Questo articolo propone un algoritmo di super-risoluzione end-to-end multi-frame, che si basa su una co-registrazione a coppie e su fusing block (convolutional residual block), incorporati in una rete di encoder-decoder "HighRes-net" che stima l'immagine con super-risoluzione.

Questo articolo propone un framework che include la fusione ricorsiva alla loss di co-registrazione per risolvere il problema dei risultati con super-risoluzione e delle label ad alta risoluzione che non sono allineate ai pixel.


Large mini-batch parallel SGD is commonly used for distributed training of deep networks.Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication.In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.SGP uses a gossip algorithm called PushSum for approximate distributed averaging, allowing for much more loosely coupled communications which can be beneficial in high-latency or high-variability scenarios.The tradeoff is that approximate distributed averaging injects additional noise in the gradient which can affect the train and test accuracies.We prove that SGP converges to a stationary point of smooth, non-convex objective functions.Furthermore, we validate empirically the potential of SGP.For example, using 32 nodes with 8 GPUs per node to train ResNet-50 on ImageNet, where nodes communicate over 10Gbps Ethernet, SGP completes 90 epochs in around 1.5 hours while AllReduce SGD takes over 5 hours, and the top-1 validation accuracy of SGP remains within 1.2% of that obtained using AllReduce SGD.

Per il training distribuito su reti ad alta latenza, si usa una media distribuita approssimata gossip-based invece di una media distribuita esatta come AllReduce.

Gli autori propongono di usare algoritmi di gossip come metodo generale per calcolare approssimativamente la media su un insieme di worker

L'articolo dimostra la convergenza di SGP per funzioni smooth non convesse e mostra che SGP può raggiungere uno speed-up significativo nell'ambiente a bassa latenza senza sacrificare troppo in prestazioni predittive.


In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on.The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator.We also explore two approaches to accomplish the conditional discriminator: (1) $phredGAN_a$, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) $phredGAN_d$, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance.To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends.Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation.We also explore the trade-offs from using either variant of $phredGAN$ on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).

Questo articolo sviluppa un framework di adversarial learning per modelli di conversazione neurali con persona

Questo articolo propone un'estensione di hredGAN per imparare simultaneamente un insieme di embeddings di attributi che rappresentano la persona di ogni speaker e generare risposte basate sulla persona


We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space.Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task.This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.

Le reti neurali artificiali ispirate biologicamente, composte da neuroni posizionati in uno spazio bidimensionale, sono in grado di formare gruppi indipendenti per eseguire diversi task.


The transformer has become a central model for many NLP tasks from translation to language modeling to representation learning.Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks.In theory attention also offers more insights into the modelâ€™s internal decisions; however, in practice when stacked it quickly becomes nearly as fully-connected as recurrent models.In this work, we propose an alternative transformer architecture, discrete transformer, with the goal of better separating out internal model decisions.The model uses hard attention to ensure that each step only depends on a fixed context.Additionally, the model uses a separate â€œsyntacticâ€ controller to separate out network structure from decision making.Finally we show that this approach can be further sparsified with direct regularization.Empirically, this approach is able to maintain the same level of performance on several datasets, while discretizing reasoning decisions over the data.

Trasformer discreto che usa hard attention per assicurare che ogni step dipenda solo da un contesto fisso.

Questo articolo presenta delle modifiche all'architettura standard del transformer con l'obiettivo di migliorare l'interpretabilità mantenendo le prestazioni nei task NLP.

Questo articolo propone tre transformer discreti: un modulo di attention discreto e stocastico basato su Gumbel-softmax, un transformer sintattico e semantico a due flussi e la regolarizzazione della sparsità.


Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex.We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019).In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.

Mostriamo evidenza empirica che i modelli di predictive encoding producono rappresentazioni più correlate ai dati del cervello rispetto ai modelli supervised di riconoscimento delle immagini.


The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples.Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest.Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion.We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task.Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations.The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources.Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.

Un framework generico per gestire il transfer e multi-task learning usando coppie di autoencoder con pesi specifici del task e pesi condivisi.

Propone un framework generico per il transfer learning end-to-end / domain adaptation con deep neural network.

Questo articolo propone un modello per permettere alle architetture di deep neural network di condividere i parametri tra diversi dataset, e lo applica al transfer learning.

L'articolo si concentra sull'apprendimento di feature comuni da dati di domini multipli e finisce con un'architettura generale per il multi-task learning, semi-supervised learning e transfer learning.


Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers).ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects.We demonstrate this on classification and regression tasks, achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and outperforming standard neural networks, random forests and gradient boosted trees on the SARCOS dataset.Furthermore, ANT optimisation naturally adapts the architecture to the size and complexity of the training data.

Proponiamo una framework per combinare alberi decisionali e reti neurali, e dimostriamo su task di classificazione di immagini che gode dei benefici complementari dei due approcci, mentre affronta le limitazioni del lavoro precedente.

Gli autori hanno proposto un nuovo modello, Adaptive Neural Trees, combinando l'apprendimento della rappresentazione e l'ottimizzazione del gradiente delle reti neurali con l'apprendimento dell'architettura degli alberi decisionali

Questo articolo propone l'approccio Adaptive Neural Trees per combinare i due paradigmi di apprendimento delle deep neural network e degli alberi decisionali


While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language.XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark.With improvements of up to 4.8, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu.XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.On the SQuAD question answering task, we see that XLDA provides a 1.0 performance increase on the English evaluation set.Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.

La traduzione di porzioni dell'input durante l'training può migliorare le prestazioni multilingue.

L'articolo propone un metodo di augmentation dei dati multilingue per migliorare l'inferenza linguistica e i task di question answering.

Questo articolo propone di aumentare i dati crosslinguali con scambi euristici usando traduzioni allineate, come fanno gli umani bilingui nel code-switching.


Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse. We find, and empirically show, that one of the major reasons behind posterior collapse is rooted in the way that generative models are conditioned, i.e., through concatenation of the latent variable and the condition. To mitigate this problem, we propose to explicitly make the latent variables depend on the condition by unifying the conditioning and latent variable sampling, thus coupling them so as to prevent the model from discarding the root of variations. To achieve this, we develop a conditional Variational Autoencoder architecture that learns a distribution not only of the latent variables, but also of the condition, the latter acting as prior on the former. Our experiments on the challenging tasks of conditional human motion prediction and image captioning demonstrate the effectiveness of our approach at avoiding posterior collapse. Video results of our approach are anonymously provided in http://bit.ly/iclr2020

Proponiamo un framework di autoencoder condizionale variazionale che mitiga il posterior collapse negli scenari in cui il segnale di condizionamento è abbastanza forte per un decoder espressivo per generare da esso un output plausibile.

Questo articolo considera i modelli generativi fortemente condizionati, e propone una funzione obiettivo e una parametrizzazione della distribuzione variazionale tale che le variabili latenti dipendano esplicitamente dalle condizioni di input.

Questo articolo sostiene che quando il decoder è condizionato dalla concatenazione di variabili latenti e informazioni ausiliarie, allora il collasso posteriore è più probabile che nella VAE standard.


We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.  We propose a methodology for testing for statistical differences in model performances under several replications.To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.We find that the selected implementations exhibit stability across random seed, and repeats.

Proponiamo uno studio della stabilità di diversi algoritmi di few-shot learning soggetti a variazioni negli iper-parametri e negli schemi di ottimizzazione mentre controlliamo il random seed.

Questo articolo studia la riproducibilità per few-shot learning


We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach.Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial.To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation.We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice.Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.

Traduciamo un bound sulla sub-ottimalità delle rappresentazioni in un obiettivo pratico di training nel contesto del reinforcement learning gerarchico.

Gli autori propongono un nuovo approccio nell'apprendimento di una rappresentazione per HRL e dichiarano un'intrigante connessione tra representation learning e il contenimento della sub-ottimalità che risulta in un algoritmo basato sul gradiente

Questo articolo propone un modo per gestire la sub-ottimalità nel contesto del representaton learning che si riferiscono alla sub-ottimalità della policy gerarchica rispetto alla reward del task.


Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time.In online planning, algorithms for real-time search or deadline-aware search have been considered before.However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.  Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner.Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.

Metareasoning in un Situated Temporal Planner

Questo articolo affronta il problema del Situated Temporal Planning, proponendo un'ulteriore semplificazione sulle strategie greedy precedentemente proposte da Shperberg.


Neural networks are vulnerable to small adversarial perturbations.Existing literature largely focused on understanding and mitigating the vulnerability of learned models.In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier.Empirical investigations further confirm our finding.We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.

Le prestazioni di robustezza dei modelli trained da PGD sono sensibili alle trasformazioni semantics-preserving dei dataset di immagini, il che implica la difficoltà nella pratica della valutazione degli algoritmi di learning robusto.

Il documento chiarisce la differenza tra clean e robust accuracy e mostra che cambiare la distribuzione marginale dei dati di input P(x) conservando la sua semantica P(y|x) influisce sulla robustezza del modello.

Questo articolo indaga l'origine della mancanza di robustezza dei classificatori alle perturbazioni degli adversarial input sotto perturbazioni l-inf bounded.


Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity.Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence.When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors.We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures.Using a structured attention mechanism, our model matches possible spans in the first sentence to possible spans in the second sentence, simultaneously discovering the tree structure of each sentence and performing a comparison, in a model that is fully differentiable and is trained only on the comparison objective.We evaluate this model on two sentence comparison tasks: the Stanford natural language inference dataset and the TREC-QA dataset.We find that comparing spans results in superior performance to comparing words individually, and that the learned trees are consistent with actual linguistic structures.

Corrispondenza delle frasi tramite l'apprendimento delle strutture latenti dell'albero dei costituenti con una variante dell'algoritmo inside-outside incorporato come layer della rete neurale.

Questo articolo introduce un meccanismo di attention strutturato per calcolare i punteggi di allineamento tra tutti i possibili span in due frasi date

Questo articolo propone un modello di allineamenti strutturati tra le frasi come mezzo per confrontare le frasi abbinando le loro strutture latenti.


Learning disentangled representation from any unlabelled data is a non-trivial problem.In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion.We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.

Imparare le rappresentazioni disaccopiate in modo unsupervised.

Gli autori presentano un framework in cui un encoder automatico (E, D) è regolarizzato in modo che la sua rappresentazione latente condivida informazioni reciproche con una rappresentazione generata dello spazio latente.


Effective training of neural networks requires much data.In the low-data regime,parameters are underdetermined, and learnt networks generalise poorly.DataAugmentation (Krizhevsky et al., 2012) alleviates this by using existing datamore effectively.However standard data augmentation produces only limitedplausible alternative data.Given there is potential to generate a much broader setof augmentations, we design and train a generative model to do data augmentation.The model, based on image conditional Generative Adversarial Networks, takesdata from a source domain and learns to take any data item and generalise itto generate other within-class data items.As this generative process does notdepend on the classes themselves, it can be applied to novel unseen classes of data.We show that a Data Augmentation Generative Adversarial Network (DAGAN)augments standard vanilla classifiers well.We also show a DAGAN can enhancefew-shot learning systems such as Matching Networks.We demonstrate theseapproaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, andVGG-Face data.In our experiments we can see over 13% increase in accuracy inthe low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot weobserve an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% inEMNIST (from 59.5% to 61.3%).

Le GAN condizionali addestrate per generare sample di dati arricchite dei loro input condizionali utilizzati per migliorare la classificazione standard e i sistemi di apprendimento one shot come le matching network e la distanza tra pixel

Gli autori propongono un metodo per condurre data augmentation in cui le trasformazioni tra classi sono mappate in uno spazio latente a bassa dimensione utilizzando GAN condizionali


Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model.For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables.However, randomization tests require users to specify test statistics.We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known.We show that f-divergences provide a broad class of proper test statistics.In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI.Questions of feature importance can be asked at the level of an individual sample.  We show that estimators from the same AMI test can also be used to find important features in a particular instance.We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection.We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet.Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.

Sviluppiamo un semplice metodo di selezione delle feature basato sulla regressione model-agnostic per interpretare i processi di generazione dei dati con il controllo FDR, e superiamo diverse baseline popolari su diversi dataset simulati, di ambito medico e di immagini.

Questo articolo propone un miglioramento pratico del test di randomizzazione condizionale e una nuova statistica di test, dimostra che la f-divergenza è una scelta possibile, e mostra che la divergenza KL annulla alcune distribuzioni condizionali..

Questo articolo affronta il problema di trovare feature utili in un input che sono dipendenti da una variabile di risposta anche quando si condizionano tutte le altre variabili di input.

Un metodo agnostico per fornire un'interpretazione dell'influenza delle feature di input sulla risposta di un modello dal livello di macchina fino al livello di istanza, e statistiche di test adeguate per la selezione delle feature agnostiche del modello.


Supervised learning depends on annotated examples, which are taken to be the ground truth.But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk.Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem).Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples.This raises two fundamental questions: (1) How can we best learn from noisy workers?(2) How should we allocate our labeling budget to maximize the performance of a classifier?We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality.Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality.We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold.Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.

Un nuovo approccio per l'apprendimento di un modello da annotazioni rumorose in crowdsourcing.

Questo articolo propone un metodo per l'apprendimento da label rumorose, concentrandosi sul caso in cui i dati non sono annotati in modo ridondante con una validazione teorica e sperimentale

Questo articolo si concentra sul learning-from-crowds problem, dove l'aggiornamento congiunto dei pesi del classificatore e delle matrici di confusione dei worker può aiutare nel problema della stima con label rare in crowdsourcing.

Propone un algoritmo di apprendimento supervised per modellare la qualità delle label e dei lavoratori e utilizza l'algoritmo per studiare quanta ridondanza è necessaria nel crowdsourcing e se una bassa ridondanza con abbondanti esempi di rumore porta a label migliori.


Neural networks make mistakes.The reason why a mistake is made often remains a mystery.As such neural networks often are considered a black box.It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.In this paper we explain our method and demonstrate it on MNIST and CelebA.This approach could aid in demystifying neural networks for a user.

Nuovo modo per spiegare perché una rete neurale ha classificato male un'immagine

Questo articolo propone un metodo per spiegare gli errori di classificazione delle reti neurali.

Mira a comprendere meglio la classificazione delle reti neurali ed esplora lo spazio latente di un autoencoder variazionale e considera le perturbazioni dello spazio latente per ottenere la classificazione corretta.


In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers.Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome.Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive.In this paper, we go beyond these limitations and propose a principled approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities.Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.

Un metodo per la costruzione automatica di reti multi-task ramificate con una forte valutazione sperimentale su diversi dataset multi-tasking.

Questo articolo propone un nuovo framework di apprendimento multi-task con condivisione soft dei parametri basato su un framework ad albero.

Questo articolo presenta un metodo per dedurre l'architettura delle reti multi-task per determinare quale parte della rete dovrebbe essere condivisa tra diversi task.


Typical recent neural network designs are primarily convolutional layers, but the tricks enabling structured efficient linear layers (SELLs) have not yet been adapted to the convolutional setting.We present a method to express the weight tensor in a convolutional layer using diagonal matrices, discrete cosine transforms (DCTs) and permutations that can be optimised using standard stochastic gradient methods.A network composed of such structured efficient convolutional layers (SECL) outperforms existing low-rank networks and demonstrates competitive computational efficiency.

È possibile sostituire la matrice dei pesi in un layer convoluzionale per addestrarlo come un layer efficiente strutturato; con le stesse prestazioni della decomposizione low-rank.

Questo lavoro applica i precedenti Structured Efficient Linear Layer ai conv layer e propone Structured Efficient Convolutional Layer come sostituzione dei conv layer originali.


Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc.Since this problem is highly ill-posed, supervised and unsupervised learning methods are well suited for this application.Using various techniques, extensive work has been done on natural-scene deblurring.However, these extracted features are not suitable for document images.We present SVDocNet, an end-to-end trainable U-Net based spatial recurrent neural network (RNN) for blind document deblurring where the weights of the RNNs are determined by different convolutional neural networks (CNNs).This network achieves state of the art performance in terms of both quantitative measures and qualitative results.

Presentiamo SVDocNet, una rete neurale ricorrente spaziale (RNN) addestrabile end-to-end basata su U-Net per blind document deblurring.


In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations.Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos.We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images.Our learning procedure leverages smooth motion in natural videos.Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner.The learned "dynamic filters" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames.Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary "capsules" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.

Estendiamo lo sparse coding bilineare e sfruttiamo le sequenze video per imparare i filtri dinamici.


Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) are known to assign lower uncertainty to the OOD data than the target distribution.In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images.Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training.Our detector is simple, efficient in test time, and outperforms baseline OOD detectors in various domains.Further results show that SVD-RND learns a better target distribution representation than the baselines.Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy in CelebA domain.

Proponiamo un nuovo rilevatore di OOD che utilizza immagini sfocate come adversarial sample. Il nostro modello raggiunge prestazioni significative di rilevamento OOD in vari domini.

Questo articolo presenta l'idea di utilizzare immagini sfocate come esempi di regolarizzazione per migliorare le prestazioni di rilevamento di fuori della distribuzione basate su Random Network Distillation.

Questo articolo affronta la out-of-data distribution sfruttando la RND applicata alla data augmentation, addestrando un modello per abbinare gli output di una rete casuale con una augmentation come input.


Training large deep neural networks on massive datasets is Â computationally very challenging.There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue.The most prominent algorithm in this line of research is LARS, which by Â employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes.However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks.In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings.Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning.In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. Â By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1).

Un ottimizzatore veloce per applicazioni generali e per il training con grandi batch size.

In questo articolo, gli autori hanno fatto uno studio sul training con grandi batch size per BERT, e hanno addestrato con successo un modello BERT in 76 minuti.

Questo articolo sviluppa una strategia di layerwise adaptation che permette di addestrare i modelli BERT con grandi mini-batch da 32k contro i 512 di base.


Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.However, MAML is notorious for being hard to train because of the existence of two learning rates.Therefore, in this paper, we derive the conditions that inner learning rate $\alpha$ and meta-learning rate $\beta$ must satisfy for MAML to converge to minima with some simplifications.We find that the upper bound of $\beta$ depends on $ \alpha$, in contrast to the case of using the normal gradient descent method.Moreover, we show that the threshold of $\beta$ increases as $\alpha$ approaches its own upper bound.This result is verified by experiments on various few-shot tasks and architectures; specifically, we perform sinusoid regression and classification of Omniglot and MiniImagenet datasets with a multilayer perceptron and a convolutional neural network.Based on this outcome, we present a guideline for determining the learning rates: first, search for the largest possible $\alpha$; next, tune $\beta$ based on the chosen value of $\alpha$.

Abbiamo analizzato il ruolo di due learning rate nel meta learning model-agnostic nella convergenza.

Gli autori hanno affrontato il problema dell'instabilità dell'ottimizzazione in MAML studiando i due learning rate.

Questo articolo studia un metodo per aiutare a sintonizzare i due learning rate utilizzati nell'algoritmo di training MAML.


We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity.The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.

Modello neurale indipendente dal task per l'apprendimento di associazioni tra gruppi di parole correlate.

L'articolo ha proposto un metodo per addestrare word vector specifici per una funzione, in cui ogni parola è rappresentata con tre vettori ciascuno in una categoria diversa (Soggetto-Verbo-Oggetto).

Questo articolo propone una rete neurale per imparare work representation specifiche per una funzione e dimostra il vantaggio rispetto alle alternative.


The fabrication of semiconductor involves etching process to remove selected areas from wafers.However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines.Traditional image processing usually demands on large number of annotated data and the performance is still poor.We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer.Then, we use simple image processing to carry out automatic measurement on the objects.We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset.We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments.Our deep learning based method demonstrates superiority over image processing approach with  mean accuracy reaching over 96% for the measurements, compared with the ground truth.To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.

Utilizzo del metodo di deep learning per effettuare la misurazione automatica delle immagini SEM nell'industria dei semiconduttori


Generating and scheduling activities is particularly challengingwhen considering both consumptive resources andcomplex resource interactions such as time-dependent resourceusage.We present three methods of determining validtemporal placement intervals for an activity in a temporallygrounded plan in the presence of such constraints.We introducethe Max Duration and Probe algorithms which aresound, but incomplete, and the Linear algorithm which issound and complete for linear rate resource consumption.We apply these techniques to the problem of schedulingawakes for a planetary rover where the awake durationsare affected by existing activities.We demonstrate how theProbe algorithm performs competitively with the Linear algorithmgiven an advantageous problem space and well-definedheuristics.We show that the Probe and Linear algorithmsoutperform the Max Duration algorithm empirically.We then empirically present the runtime differences betweenthe three algorithms.The Probe algorithm is currently base-linedfor use in the onboard scheduler for NASAâ€™s next planetaryrover, the Mars 2020 rover.

Questo articolo descrive e analizza tre metodi per programmare attività a durata non fissa in presenza di risorse consumabili.

L'articolo presenta tre approcci per la programmazione di attività a bordo di un rover planetario sotto vincoli di risorse di riserva.


A disentangled representation of a data set should be capable of recovering the underlying factors that generated it.One question that arises is whether using Euclidean space for latent variable models can produce a disentangled representation when the underlying generating factors have a certain geometrical structure.Take for example the images of a car seen from different angles.The angle has a periodic structure but a 1-dimensional representation would fail to capture this topology.How can we address this problem?The submissions presented for the first stage of the  NeurIPS2019 Disentanglement Challenge consist of a Diffusion Variational Autoencoder ($\Delta$VAE) with a hyperspherical latent space which can for example recover periodic true factors.The training of the $\Delta$VAE is enhanced by incorporating a modified version of the Evidence Lower Bound (ELBO) for tailoring the encoding capacity of the posterior approximate.

Descrizione della submission a NeurIPS2019 Disentanglement Challenge basata su autoencoder ipersferici variazionali


Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.Recently, classification-based methods were shown to achieve superior results on this task.In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions.Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations.Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types.The strong performance of our method is extensively validated on multiple datasets from different domains.

Un rilevamento delle anomalie che utilizza la classificazione a trasformazione casuale per generalizzare ai dati che non sono immagini.

Questo articolo propone un metodo deep per il rilevamento delle anomalie che unifica la recente classificazione deep one class e gli approcci di classificazione basati su trasformazioni.

Questo articolo propone un approccio al rilevamento delle anomalie basato sulla classificazione per dati generali utilizzando la trasformazione affine y = Wx+b.


Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications.Many of these advances leverage the availability of large corpora.While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora.This paper aims to quantify and reduce biases exhibited by language models.Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation.We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature.Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data.We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.

Riduciamo i sentiment bias basandoci sulla valutazione controfattuale della text generation utilizzando language model.

Questo articolo misura i sentiment bias nei language model come riflesso del testo generato dai modelli, e aggiunge altri termini al solito obiettivo di language modelling per ridurre i bias.

Questo articolo propone di valutare i bias nei language model pre-trained utilizzando un sistema di fixed sentiment e prova diversi modelli di prefisso.

Un metodo basato sulla somiglianza semantica e un metodo basato sulla somiglianza del sentimento per il debiasing dei language model addestrati su grandi dataset.


Topic modeling of text documents is one of the most important tasks in representation learning.In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders.On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically.On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically.Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks.Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.

Un modello nonparametrico bayesiano di topic con autoencoder variazionali che raggiunge lo stato dell'arte su benchmark pubblici in termini di perplexity, coerenza di argomenti e task di retrieval.

Questo articolo costruisce un modello infinito di topic con autoencoder variazionali combinando l'autoencoder variazionale di Nalisnick & Smith con l'allocazione latente di Dirichlet e diverse tecniche di inferenza usate in Miao.


Knowledge Distillation (KD) is a widely used technique in recent deep learning research to obtain small and simple models whose performance is on a par with their large and complex counterparts.Standard Knowledge Distillation tends to be time-consuming because of the training time spent to obtain a teacher model that would then provide guidance for the student model.It might be possible to cut short the time by training a teacher model on the fly, but it is not trivial to have such a high-capacity teacher that gives quality guidance to student models this way.To improve this, we present a novel framework of Knowledge Distillation exploiting dark knowledge from the whole training set.In this framework, we propose a simple and effective implementation named Distillation by Utilizing Peer Samples (DUPS) in one generation.We verify our algorithm on numerous experiments.Compared with standard training on modern architectures, DUPS achieves an average improvement of 1%-2% on various tasks with nearly zero extra cost.Considering some typical Knowledge Distillation methods which are much more time-consuming, we also get comparable or even better performance using DUPS.

Presentiamo un nuovo framework di Knowledge Distillation che utilizza peer sample come teacher

Propone un metodo per migliorare l'efficacia della knowledge distillation rendendo soft le label utilizzate e utilizzando un dataset invece di un singolo sample.

Questo articolo propone di affrontare il costo computazionale extra del training con la knowledge distillation, basandosi sulla tecnica Snapshot Distillation recentemente proposta.


We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitivesâ€”policies that are executed for large numbers of timesteps.Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.

imparare le sotto-policy gerarchiche attraverso il training end-to-end su una distribuzione di task

Gli autori considerano il problema dell'apprendimento di un utile insieme di "policy secondarie" che possono essere condivise tra i task in modo da avviare l'apprendimento su nuovi task tratti dalla distribuzione dei task.

Questo articolo propone un nuovo metodo per indurre un framework gerarchico temporale in un ambiente specializzato multi-task.


This paper proposes a new model for document embedding.Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize.We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model.This allows us to train deeper architectures that are fully parallelizable.Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document.Empirically we demonstrate superior results on two publicly available benchmarks.Full code will be released with the final version of this paper.

Modello di rete neurale convoluzionale per document embedding unsupervised.

Introduce un nuovo modello per il task generale di indurre rappresentazioni di documenti (embeddings) che utilizza un'architettura CNN per migliorare l'efficienza computazionale.

Questo articolo propone di usare CNN con un obiettivo simile a skip-gram come un modo veloce per produrre embedding di documenti


We prove bounds on the generalization error of convolutional networks.The bounds are in terms of the training loss, the number ofparameters, the Lipschitz constant of the loss and the distance fromthe weights to the initial weights. They are independent of thenumber of pixels in the input, and the height and width of hiddenfeature maps. We present experiments with CIFAR-10, along with varyinghyperparameters of a deep convolutional network, comparing our boundswith practical generalization gaps.

Dimostriamo i limiti di generalizzazione per le reti neurali convoluzionali che tengono conto del weight-tying

Studia il potere di generalizzazione delle CNN e migliora i limiti superiori degli errori di generalizzazione, mostrando la correlazione tra l'errore di generalizzazione delle CNN apprese e il termine dominante del limite superiore.

Questo articolo presenta un limite di generalizzazione per le reti neurali convoluzionali basato sul numero di parametri, la costante Lipschitz e la distanza dei pesi finali dall'inizializzazione.


MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years.New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks.Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 âˆ’ 6 bits albeit with a modest to significant drop in accuracy.While quantization to sub-byte values (i.e. precision â‰¤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs.Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets.The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets.Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.

Un risparmio di un fattore 2 nella dimensione del modello, riduzione del 28% dell'energia per MobileNet su ImageNet senza perdita di precisione utilizzando layer ibridi composti da filtri convenzionali a piena precisione e filtri ternari

Si concentra sulla quantizzazione dell'architettura MobileNet a valori ternari, abbassando lo spazio richiesto e il calcolo per rendere le reti neurali più efficienti dal punto di vista energetico.

L'articolo propone un banco di filtri ibrido a layer che quantizza a valori ternari solo una frazione dei filtri convoluzionali verso l'architettura MobileNet.


Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.Due to the lack of suitable datasets, previous research have only examined deep learning on controlled synthetic noise, and real-world noise has never been systematically studied in a controlled setting.To this end, this paper establishes a benchmark of real-world noisy labels at 10 controlled noise levels.As real-world noise possesses unique properties, to understand the difference, we conduct a large-scale study across a variety of noise levels and types, architectures, methods, and training settings.Our study shows that: (1) Deep Neural Networks (DNNs) generalize much better on real-world noise.(2) DNNs may not learn patterns first on real-world noisy data.(3) When networks are fine-tuned, ImageNet architectures generalize well on noisy data.(4) Real-world noise appears to be less harmful, yet it is more difficult for robust DNN methods to improve.(5) Robust learning methods that work well on synthetic noise may not work as well on real-world noise, and vice versa.We hope our benchmark, as well as our findings, will facilitate deep learning research on noisy data.

Stabiliamo un benchmark di rumore reale controllato e riveliamo diversi risultati interessanti sui dati rumorosi reali.

Questo articolo confronta 6 metodi esistenti per l'apprendimento di label rumorose in due setting di training: da zero e finetuning.

Gli autori stabiliscono un grande dataset e un benchmark di rumore reale controllato per eseguire esperimenti controllati su dati rumorosi nel deep learning.


Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation.Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints.Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA.LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure.By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks.Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process.Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance.In an ablation study, we analyze the importance of our method's different components.

Impariamo a risolvere il problema RNA Design con il reinforcement learning usando approcci di meta learning e autoML.

Ha utilizzato policy gradient optimization per generare sequenze di RNA che si ripiegano in un framework target secondario, ottenendo chiari miglioramenti in termini di precisione e tempo di esecuzione.


Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed.However, the value of pruning has largely evaded scrutiny.In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations.First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network.Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable.Such architectures are powerful when trained from scratch.Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.

Addestrare piccole reti batte il pruning, ma il pruning trova buone piccole reti da addestrare che sono facili da copiare.


Supervised learning problems---particularly those involving social data---are often subjective.That is, human readers, looking at the same data, might come to legitimate but completely different conclusions based on their personal experiences.Yet in machine learning settings feedback from multiple human annotators is often reduced to a single ``ground truth'' label, thus hiding the true, potentially rich and diverse interpretations of the data found across the social spectrum.We explore the rewards and challenges of discovering and learning representative distributions of the labeling opinions of a large human population.A major, critical cost to this approach is the number of humans needed to provide enough labels not only to obtain representative samples but also to train a machine to predict representative distributions on unlabeled data.We propose aggregating label distributions over, not just individuals, but also data items, in order to maximize the costs of humans in the loop.We test different aggregation approaches on state-of-the-art deep learning models.Our results suggest that careful label aggregation methods can greatly reduce the number of samples needed to obtain representative distributions.

Studiamo il problema dell'apprendimento per prevedere la diversità sottostante dei belief presenti nei domini di apprendimento supervised.


Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images.While much more effective than conventional approaches, deep learning models require large datasets and great computational resources for training, and inpainting quality varies considerably when training data vary in size and diversity.To address these problems, we present in this paper a inpainting strategy of \textit{Comparative Sample Augmentation}, which enhances the quality of training set by filtering out irrelevant images and constructing additional images using information about the surrounding regions of the images to be inpainted.Experiments on multiple datasets demonstrate that our method extends the applicability of deep inpainting models to training sets with varying sizes, while maintaining inpainting quality as measured by qualitative and quantitative metrics for a large class of deep models, with little need for model-specific consideration.

Abbiamo introdotto una strategia che permette l'inpainting dei modelli su dataset di varie dimensioni

Migliora l'inpainting dell'immagine usando GAN utilizzando un comparative augmenting filter e aggiungendo rumore casuale ad ogni pixel.


Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion.Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost.GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other playersâ€™ parameters.One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium.Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence.We show that this view is overly restrictive.During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful.We provide empirical counterexamples to the view of GAN training as divergence minimization.Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail.We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful.This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.

Troviamo prove che la minimizzazione della divergenza potrebbe non essere una caratterizzazione accurata del training delle GAN.

La submission mira a presentare l'evidenza empirica che la teoria della minimizzazione della divergenza è più uno strumento per comprendere il risultato del training delle GAN che una condizione necessaria da far rispettare durante il training stesso

Questo articolo studia le GAN non saturanti e l'effetto di due approcci con penalty del gradiente, considerando diversi thought experiment per dimostrare le osservazioni e validarle su esperimenti di dati reali.


Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.Recent works have developed accurate MI estimators through provably low-bias approximations and tight variational lower bounds assuming abundant supply of samples, but require an unrealistic number of samples to guarantee statistical significance of the estimation.In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE) that can provide a tight lower confident interval of MI under limited data, through adding cross-validation to the MINE lower bound (Belghazi et al., 2018).Hyperparameter search is employed and a novel meta-learning approach with task augmentation is developed to increase robustness to hyperparamters, reduce overfitting and improve accuracy.With improved data-efficiency, our DEMINE estimator enables statistical testing of dependency at practical dataset sizes.We demonstrate the effectiveness of DEMINE on synthetic benchmarks and a real world fMRI dataset, with application of inter-subject correlation analysis.

Un nuovo e pratico test statistico di dipendenza usando le reti neurali, con un benchmark su dataset sintetici e reali di fMRI.

Propone una stima dell'informazione reciproca basata su reti neurali che può funzionare in modo affidabile con piccoli dataset, riducendo la sample complexity disaccoppiando il problema dell'apprendimento della rete e il problema della stima.


Language and vision are processed as two different modal in current work for image captioning.However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem.In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.The experimental results on Flickr30k data shows the proposed method gives high quality image captions.An interactive demo is ready to show at the workshop.

Image captioning utilizzando word embedding bidimensionali.


Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.However, choosing a non-trivial scheduling method may drastically improve convergence.In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).Our method parameterizes mini-batches dynamically based on the \textit{easiness} and \textit{true diverseness} of the sample within a salient feature representation space.In LEAP, we train an \textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.The \textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \textit{easiness} from cross-entropy losses and \textit{true diverseness} of examples from the representation space sculpted by the \textit{embedding} CNN.We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.

LEAP combina la forza dell'adaptive sampling con quella dell'apprendimento online in mini-batch e dell'apprendimento adattivo della rappresentazione per formulare una strategia rappresentativa self-paced in un protocollo di training DNN end-to-end.

Introduce un metodo per creare mini batch per una rete di student utilizzando un secondo spazio di rappresentazione appreso per selezionare dinamicamente gli esempi in base alla loro "facilità e reale diversità".

Sperimenta l'accuratezza di classificazione su MNIST, FashionMNIST, e CIFAR-10 dataset per imparare una rappresentazione con la selezione di minibatch tramite curriculum learning in un framework end-to-end.


Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments.To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space.The problem lies in how to find an appropriate macro action to augment the primitive action space.  The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure.In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions.However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy.On the other hand, repeating actions may limit the diversity of behaviors of the agent.Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.  Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not.   We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods.Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments.We further provide a comprehensive set of ablation analysis to validate our methodology.

Proponiamo di costruire le macro azioni con un algoritmo genetico, che elimina la dipendenza della procedura di derivazione delle macro azioni dalle policy passate dell'agente.

Questo articolo propone un algoritmo generico per la costruzione di macro azioni per il deep reinforcement learning aggiungendo una macro azione allo spazio delle azioni primitive.


A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients.A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process.Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system.We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.

Proponiamo un'estensione di LFADS in grado di dedurre gli spike train per ricostruire le tracce di fluorescenza del calcio usando i VAE gerarchici.


In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal.In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora.Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation.Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation.The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively.Our implementation is released as an open source project.

Introduciamo il primo metodo di successo per neural machine translation in modo unsupervised, usando solo corpora monolingue

Gli autori presentano un modello di NMT unsupervised che non richiede corpora paralleli tra le due lingue di interesse.

Questo è un articolo sulla machine translation unsupervised che addestra un'architettura standard utilizzando word embedding in uno spazio di embedding condiviso solo con word paper bilingui e un encoder-decoder addestrato utilizzando dati monolingua.


We describe a new training methodology for generative adversarial networks.The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2.We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10.Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator.Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation.As an additional contribution, we construct a higher-quality version of the CelebA dataset.

Addestriamo le generative adversarial network in modo progressivo, permettendoci di generare immagini ad alta risoluzione con alta qualità.

Introduce la crescita progressiva e una semplice funzione statistica di riepilogo dei minibatch senza parametri da usare nel training delle GAN per consentire la sintesi di immagini ad alta risoluzione.


Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance.DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata.This contribution is twofold.First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors.Second, we evaluate DeepSphere on relevant problems.Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation.Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.

Una CNN sferica basata su grafi che trova un interessante equilibrio di compromessi per un'ampia varietà di applicazioni.

Combina le strutture CNN esistenti basate sulla discretizzazione di una sfera come un grafo per mostrare un risultato di convergenza che è legato all'equivalenza rispetto alla rotazione su una sfera.

Gli autori usano la formulazione esistente di CNN a grafo e una strategia di pooling che sfrutta le pixelature gerarchiche della sfera per imparare dalla sfera discretizzata.


The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics.In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity.Here, we derive stationary fluctuation-dissipation relations that link measurable quantities and hyperparameters in the stochastic gradient descent algorithm.These relations hold exactly for any stationary state and can in particular be used to adaptively set training schedule.We can further use the relations to efficiently extract information pertaining to a loss-function landscape such as the magnitudes of its Hessian and anharmonicity.Our claims are empirically verified.

Dimostriamo le relazioni di fluttuazione-dissipazione per SGD, che possono essere utilizzate per (i) impostare in modo adattivo i learning rate e (ii) sondare le superfici della loss.

I concetti del paper rientrano nel formalismo a tempo discreto, usano l'equazione master ed eliminano la dipendenza da un'approssimazione localmente quadratica della funzione di loss o da qualsiasi ipotesi gaussiana del rumore SGD.

Gli autori derivano le relazioni di fluttuazione-dissipazione stazionarie che collegano le quantità misurabili e gli iperparametri in SGD e usano le relazioni per impostare la schedule di training in modo adattivo e analizzare il landscape della funzione di loss.


Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise.We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance.Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence.The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states.This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step.On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss.We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.

Proponiamo un meccanismo di denoising dello stato interno di una RNN per migliorare le prestazioni di generalizzazione.


We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking.Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns.Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training.We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines.We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs.Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

Per environment dettati parzialmente da processi di input esterni, deriviamo una baseline dipendente dall'input che riduce provatamente la varianza per i metod policy gradient e migliora le prestazioni della policy in una vasta gamma di task RL.

Gli autori considerano il problema dell'apprendimento in environment guidati dall'input, mostrano come il teorema PG si applichi ancora per un critic consapevole dell'input, e mostrano che le baseline dipendenti dall'input sono le migliori da usare nella congettura con quel critic.

Questo articolo introduce la nozione di baseline dipendente dall'input nei metodi di policy gradient in RL, e propone diversi metodi per addestrare la funzione di baseline dipendente dall'input per aiutare a cancellare la varianza dalla perturbazione dei fattori esterni.


Deep networks have shown great performance in classification tasks.However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification.We introduce a network that has the capacity to do both classification and reconstruction by adding a "style memory" to the output layer of the network.We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses.The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct.We further investigate the nature of the style memory, and how it relates to composing digits and letters.

Aumentare il layer superiore di una rete classificatrice con una style memory le permette di essere generativa.

Questo articolo propone di addestrare una rete neurale classificatrice non solo per classificare, ma anche per ricostruire una rappresentazione del suo input, al fine di fattorizzare le informazioni della classe dall'aspetto.

L'articolo propone di addestrare un autoencoder in modo tale che la rappresentazione del layer intermedio consista nella label di classe dell'input e in una hidden representation.


Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works.Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions.Both architectural diversity and routing depth can increase the representational power of a routing network.In this work, we address both of these deficiencies.We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth.In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup.However, when scaling up routing depth, we find that modern routing techniques struggle with optimization.We conclude by discussing both the positive and negative results, and suggest directions for future research.

I modelli di routing che lavorano sul singolo esempio beneficiano della diversità dell'architettura, ma faticano ancora nello scalare a un gran numero di decisioni di routing.

Aggiunge diversità al tipo di unità architetturale disponibile per il router ad ogni decisione e scalando a reti più profonde, raggiungendo prestazioni allo stato dell'arte su Omniglot.

Questo lavoro estende le reti di routing per utilizzare diverse architetture attraverso i moduli instradati