Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities.The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities.However, existing work learns joint representations using multiple modalities as input and may be sensitive to noisy or missing modalities at test time.With the recent success of sequence to sequence models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time.In this paper, we propose a method to learn robust joint representations by translating between modalities.Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities.Once our translation model is trained with paired multimodal data, we only need data from the source modality at test-time for prediction.This ensures that our model remains robust from perturbations or missing target modalities.We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube.Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to perturbations of all other modalities.

Presentiamo un modello che impara rappresentazioni congiunte robuste eseguendo traduzioni cicliche gerarchiche tra modalità multiple.

Questo articolo presenta il Multimodal Cyclic Translation Network (MCTN) e lo valuta per l'analisi multimodale del sentimento.


The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning.The Hessian is often used to understand these geometric properties.We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian.Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian.We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16.To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature.This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

Comprendere gli autovalori dell'Hessiana delle reti neurali sotto la distribuzione generatrice di dati.

Questo articolo analizza lo spettro della matrice Hessiana di grandi reti neurali, con un'analisi max/min degli autovalori e la visualizzazione degli spettri utilizzando un approccio di quadratura Lanczos.

Questo articolo usa la teoria delle matrici casuali per studiare la distribuzione dello spettro dell'Hessiana empirica e dell'Hessiana vera per deep learning, e propone un metodo efficiente di visualizzazione dello spettro.


Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text.In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

Un semplice trucco per migliorare i modelli che processano le sequenze componendoli con un modello a grafo

Questo articolo presenta un modello di summarization strutturale con un encoder basato su grafi esteso da RNN.

Questo lavoro combina le Graph Neural Networks con un approccio sequenziale all'abstractive summarization, efficace su tutti i dataset rispetto alle baseline esterne.


In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability.Nevertheless, it is difficult to determine the number of components.We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM).In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components.The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner.Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity.Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.

Un classificatore sparso basato su un modello discriminativo a miscela gaussiana, che può anche essere incorporato in una rete neurale.

L'articolo presenta un modello di mistura gaussiana trained tramite gradient descent che permette di indurre la sparsità e di ridurre i parametri trainable di layer del modello.

Questo articolo propone un classificatore, chiamato SDGM, basato sulla mistura discriminativa gaussiana e la sua stima dei parametri sparsi.


We recently observed that convolutional filters initializedfarthest apart from each other using offthe-shelf pre-computed Grassmannian subspacepacking codebooks performed surprisingly wellacross many datasets.Through this short paper,weâ€™d like to disseminate some initial results in thisregard in the hope that we stimulate the curiosityof the deep-learning community towards consideringclassical Grassmannian subspace packingresults as a source of new ideas for more efficientinitialization strategies.

Inizializzare i pesi utilizzando i codebook di Grassmann, ottenere un training più veloce e una migliore accuratezza


Domain adaptation is critical for success in new, unseen environments.Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.  Our model can be applied in a variety of visual recognition and prediction settings.We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

Un approccio di domain adaptation unsupervised che si adatta sia a livello di pixel che di feature

Questo articolo propone un approccio di domain adaptation estendendo la CycleGAN con funzioni di loss specifiche del task e loss imposte sia sui pixel che sulle feature.

Questo articolo propone l'uso di CycleGAN per domain adaptation

Questo articolo fa una nuova estensione al lavoro precedente su CycleGAN accoppiandolo con approcci di adversarial adaptation, includendo una nuova feature e una loss semantica nell'obiettivo generale del CycleGAN, con chiari benefici.


Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information.The proposed approach efficiently removes affixes from an Amharic word.The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming.While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support.In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix.The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states.The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem.The performance of the generated stemmer is evaluated using manually annotated Amharic words.The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.

Amharic Light Stemmer è progettato per migliorare le prestazioni di Amharic Sentiment Classification.

Questo articolo studia lo stemming per le lingue morfologicamente ricche con uno stemmer leggero che rimuove solo gli affissi nella misura in cui l'informazione semantica originale della parola viene mantenuta.

Questo articolo propone una tecnica di stemming leggera per la lingua amarica utilizzando una cascata di trasformazioni che standardizzano la forma, rimuovono i suffissi, i prefissi e gli infissi.


Place and grid-cells are known to aid navigation in animals and humans.Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space.We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons.Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks.Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.

Abbiamo studiato se le reti deep semplici possiedono neuroni artificiali simili a celle di griglia durante il recupero della memoria nello spazio concettuale appreso.


We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective.Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies.  Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data.The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression.Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.

Pro e contro della saccade-based computer vision in una prospettiva di codifica predittiva

Presenta un framework computazionale per il problema della visione attiva e spiega come la policy di controllo può essere appresa per ridurre l'entropia della credenza a posteriori.


Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.To circumvent this problem and learn on graphs, graph feature representation is required.Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.

Studiamo teoricamente la consistenza dello spettro del Laplaciano e lo usiamo come embedding di interi grafi

Questo articolo si basa sullo spettro del laplaciano di un grafo come mezzo per generare una rappresentazione da utilizzare per confrontare i grafi e classificarli.

Questo lavoro ha proposto di usare lo spettro del Graph Laplacian per imparare la rappresentazione dei grafi.


Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD).  In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice.  Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost.  Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy at epsilon=8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at epsilon=2/255 in 12 hours, in comparison to past work based on ``free'' adversarial training which took 10 and 50 hours to reach the same respective thresholds.

Il training avversario basato su FGSM, con randomizzazione, funziona altrettanto bene dell'adversarial training basato su PGD: possiamo usarlo per addestrare un classificatore robusto in 6 minuti su CIFAR10, e 12 ore su ImageNet, su una singola macchina.

Questo articolo rivisita il metodo Random+FGSM per addestrare modelli robusti contro forti attacchi di evasione PGD più velocemente dei metodi precedenti.

L'affermazione principale di questo articolo è che una semplice strategia di randomizzazione più il gradient sign method adversarial training produce reti neurali robuste.


In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training.The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values.But it cannot provide useful gradients and therefore requires complex optimization techniques.The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent.Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity.Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant.Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level.We also show that DeepHoyer can be applied to both element-wise and structural pruning.

Proponiamo regolatori quasi ovunque differenziabili e invarianti rispetto alla scala per il pruning DNN, che possono portare alla sparsità suprema attraverso il training standard SGD.

L'articolo propone un regolarizzatore invariante rispetto alla scala (DeepHoyer) ispirato alla misura Hoyer per indurre la sparsità nelle reti neurali.


Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data.However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise).In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help.We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.We call this approach limited self-supervision, as we limit ourselves to only the data at-hand.We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data.Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task.Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains.In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62).Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.

Dimostriamo che i dati extra senza label non sono necessari affinché i task ausiliari self-supervised siano utili per la classificazione delle serie temporali, e presentiamo nuovi ed efficaci task ausiliari.

Questo articolo propone un metodo self-supervised per l'apprendimento da dati di serie temporali in ambienti sanitari attraverso la progettazione di task ausiliari basati sulla struttura interna dei dati per creare training task ausiliari con più label.

Questo articolo propone un approccio per l'apprendimento self-supervised su serie temporali.


Are neural networks biased toward simple functions?Does depth always help learn more complex features?Is training the last layer of a network as good as training all layers?These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.Roughly, the CK and the NTK tell us respectively ``"what a network looks like at initialization" and "``what a network looks like during and after training."Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.

Gli autovalori di Conjugate (aka NNGP) e Neural Tangent Kernel possono essere calcolati in forma chiusa sul cubo booleano e rivelano gli effetti degli iperparametri sul bias induttivo, training e generalizzazione delle reti neurali.

Questo articolo fornisce un'analisi spettrale sul kernel coniugato delle reti neurali e sul kernel tangente neurale sul cubo booleano per risolvere il motivo per cui le reti deep sono orientate verso funzioni semplici.


To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain.Here we consider atlases used to parcellate the brain when studying brain function.We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.

Tutte le parcellizzazioni funzionali del cervello sono sbagliate, ma alcune sono utili


High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.  In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?We show that adversarial imitation can work well even in this high dimensional observation space.Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.  Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

Imitazione dai pixel, con reward scarsa o nulla, usando RL off-policy e una piccola funzione di reward adversarially-learned.

L'articolo propone di utilizzare un "avversario minimo" nel generative adversarial imitation learning in spazi visivi ad alta densità.

Questo articolo mira a risolvere il problema della stima di reward sparse in un ambiente di input ad alta densità.


In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.We provide results on MNIST, CIFAR10, music and speech data.

Mostriamo strategie per identificare facilmente i fake sample generati con il framework Generative Adversarial Network.

Mostrare che i fake sample creati con le comuni implementazioni di reti generative avversarie (GAN) sono facilmente identificabili utilizzando varie tecniche statistiche.

L'articolo propone delle statistiche per identificare i dati falsi generati usando le GAN basate su semplici statistiche marginali o specifiche formali generate automaticamente dai dati reali.


Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

Presentiamo un framework analitico per determinare i requisiti di bit-width di accumulazione in tutti e tre i GEMM di deep learning e verifichiamo la validità e la precisione del nostro metodo tramite esperimenti di benchmarking.

Gli autori propongono un metodo analitico per prevedere il numero di bit di mantissa necessari per le sommatorie parziali per i layer convoluzionali e fully connected

Gli autori conducono un'analisi approfondita della precisione numerica richiesta per le operazioni di accumulazione nella formazione delle reti neurali e mostrano l'impatto teorico della riduzione del numero di bit nell'accumulatore in virgola mobile.


Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space.This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping representations for the two domains well-separated.Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one.To handle both within and cross domain verifications, we propose a Feature Transfer Network (FTN) to separate the target feature space from the original source space while aligned with a transformed source space.Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain.In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.

Una nuova teoria di unsupervised domain adaptation per il distance metric learning e la sua applicazione al riconoscimento dei volti attraverso diverse variazioni di etnia.

Propone una nuova rete di trasferimento delle feature che ottimizza la domain adversarial loss e la domain separation loss..


In this paper, we consider the problem of training neural networks (NN).To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.

Proponiamo un algoritmo di stochastic gradient descent di tipo prossimale convergente per problemi di ottimizzazione non smooth non convessi vincolati

Questo articolo propone Prox-SGD, un framework teorico per algoritmi di ottimizzazione stocastica che convergono asintoticamente alla stazionarietà per loss smooth non convesse + vincoli/regolarizzatori convessi.

L'articolo propone un nuovo algoritmo di ottimizzazione stocastica basato sul gradiente con gradient averaging adattando la teoria degli algoritmi prossimali ad un setting non convesso.


The loss of a few neurons in a brain rarely results in any visible loss of function.However, the insight into what â€œfewâ€ means in this context is unclear.How many random neuron failures will it take to lead to a visible loss of function?In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces.We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.We give provable guarantees on the robustness of the network to these crashes.Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar.The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks.We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture.We design an algorithm achieving fault tolerance using a reasonable number of neurons.In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.

Diamo un bound per le NN sull'errore di output in caso di fallimenti dei pesi casuali usando un'espansione di Taylor nel limite continuo in cui i neuroni vicini sono simili

Questo articolo considera il problema del dropout dei neuroni da una rete neurale, mostrando che se l'obiettivo è quello di diventare robusto ai neuroni randomly dropped durante l'evaluation, allora è sufficiente fare training con dropout.

Questo contributo studia l'impatto delle cancellazioni di neuroni casuali sulla precisione di predizione dell'architettura addestrata, con l'applicazione all'analisi degli errori e al contesto specifico dell'hardware neuromorfo.


Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound.This is primarily due to lack of data that captures the interplay of action and sound.In this work, we perform the first large-scale study of the interactions between sound and robotic action.To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.Using this data, we explore the synergies between sound and action, and present three key insights.First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object.Finally, object representations derived from audio embeddings are indicative of implicit physical properties.We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.

Esploriamo e studiamo le sinergie tra suono e azione.

Questo articolo esplora le connessioni tra azione e suono costruendo un dataset sound-action-vision con un tilt-bot.

Questo articolo studia il ruolo dell'audio nella percezione degli oggetti e delle azioni, e anche il modo in cui le informazioni uditive possono aiutare l'apprendimento di modelli dinamici forward e inversi.


Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation.Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels.Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT).In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly.We conduct our method on both image classification and semantic segmentation.Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context.Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.

Proponiamo Hierarchical Complement Objective Training, un nuovo paradigma di training per sfruttare efficacemente la gerarchia delle categorie nello spazio delle label sia nella classificazione delle immagini che nella segmentazione semantica.

Un metodo che regolarizza l'entropia della distribuzione posteriore sulle classi che può essere utile per i task di classificazione e segmentazione delle immagini


There is a growing interest in automated neural architecture search (NAS).To improve the efficiency of NAS, previous approaches adopt  weight sharing method to force all models share the same set of weights.  However, it has been observed that a model performing better with shared weights does not necessarily perform  better when trained alone.In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.To alleviate this problem, we present a practical approach to guide the parameter posterior towards its true distribution.Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved.The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet.In our small search space, our model PC-NAS-S attains76.8% top-1 accuracy, 2.1% higher than MobileNetV2 (1.4x) with the same latency.When adopted to our large search space, PC-NAS-L achieves 78.1% top-1 accuracy within 11ms.The discovered architecture also transfers well to other computer vision applications such as object detection and person re-identification.

Il nostro articolo identifica il problema dell'approccio di weight sharing esistente nella ricerca dell'architettura neurale e propone un metodo pratico, ottenendo importanti risultati.

L'autore identifica un problema con il NAS chiamato posterior fading e introduce il Posterior Convergent NAS per mitigare questo effetto


Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4â€“8.2 percent points under existence of real-world noise.

Proponiamo un nuovo approccio di training in due fasi basato sull'early stopping per una training robusto su label rumorose.

Il documento propone di studiare come l'arresto precoce nell'ottimizzazione aiuta a trovare esempi sicuri

Questo articolo propone un metodo di training in due fasi per l'apprendimento con rumore di label.


Learning when to communicate and doing that effectively is essential in multi-agent tasks.Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

Introduciamo IC3Net, una singola rete che può essere usata per addestrare gli agenti in scenari cooperativi, competitivi e misti. Mostriamo anche che gli agenti possono imparare quando comunicare usando il nostro modello.

L'autore propone una nuova architettura per l'reinforcement learning multi-agente che utilizza diversi controllori LSTM con pesi legati che trasmettono un vettore continuo l'uno all'altro

Gli autori propongono un interessante schema di gating che permette agli agenti di comunicare in un ambiente RL multi-agente.


Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text.Despite the advances in abstractive summarization, custom generation of summaries (e.g. towards a user's preference) remains unexplored.In this paper, we present CATS, an abstractive neural summarization model, that summarizes content in a sequence-to-sequence fashion but also introduces a new mechanism to control the underlying latent topic distribution of the produced summaries.Our experimental results on the well-known CNN/DailyMail dataset show that our model achieves state-of-the-art performance.

Presentiamo il primo modello di riassunto neurale astrattivo in grado di personalizzare i riassunti generati.


We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameters (handled by a signal compression function), so that the framework can be easily extended to handle different combinations of neural net and compression type.In addition, it has other advantages, such as easy integration with deep learning frameworks, efficient training time, competitive practical performance in the loss-compression tradeoff, and reasonable convergence guarantees.Our toolkit is written in Python and Pytorch and we plan to make it available by the workshop time, and eventually open it for contributions from the community.

Proponiamo una framework software basata sulle idee dell'algoritmo Learning-Compression, che permette di comprimere qualsiasi rete neurale con diversi meccanismi di compressione (pruning, quantizzazione, low-rank, ecc.).

Questo articolo presenta il progetto di una libreria software che rende più facile per l'utente comprimere le loro reti nascondendo i dettagli dei metodi di compressione.


This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.First, the inference networks are trained to match the speaker identity between the two different modalities.Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.

Questo articolo propone un metodo di generazione multimodale end-to-end del volto umano dal discorso basato su un framework di apprendimento auto-supervisionato.

Questo articolo presenta un framework di apprendimento multimodale che collega la fase di inferenza e la fase di generazione per cercare la possibilità di generare il volto umano dalla sola voce.

Questo lavoro mira a costruire una framework condizionale per la generazione di immagini del viso dal segnale audio.


We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.The results of this network are then processed by two recurrent neural networks.One of the interesting aspects of our model is how propositional atoms are treated.For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

Viene presentato un approccio top-down per rappresentare ricorsivamente le formule proposizionali tramite reti neurali.

Questo articolo fornisce un nuovo modello a rete neurale di formule logiche che raccoglie informazioni su una data formula attraversando il suo albero di parse dall'alto verso il basso.

L'articolo persegue il percorso di una rete frameworkta ad albero isomorfa all'albero di parse di una formula del calcolo proposizionale, ma passando informazioni dall'alto verso il basso piuttosto che dal basso verso l'alto.


Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.We identify three key challenges that any algorithm needs to master in order to perform well on all games:  processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.  In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of 0.999 (instead of 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.When tested on a set of 42 Atari games, our algorithm exceeds the performance  of an average human on 40 games using a common set of hyper parameters.

Ape-X DQfD = DQN distribuito (molti attori + un discente + replay prioritario) con dimostrazioni che ottimizzano il ritorno non scontato di 0,999 su Atari.

L'articolo propone tre estensioni (aggiornamento di Bellman, loss di coerenza temporale e dimostrazione esperta) a DQN per migliorare le prestazioni di apprendimento sui giochi Atari, raggiungendo prestazioni superiori ai risultati dello stato dell'arte per i giochi Atari.

Questo articolo propone un operatore di Bellman trasformato che mira a risolvere la sensibilità alla ricompensa non presa, la robustezza al valore del fattore di sconto e il problema dell'esplorazione.


The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.%The resulting model encodes relations that better generalize across instances.In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.

Metodo di training per imporre vincoli rigorosi sulle embeddings apprese durante la training supervisionata. Applicato alla risposta alle domande visive.

Gli autori propongono un framework per incorporare una conoscenza preliminare semantica aggiuntiva nell'training tradizionale dei modelli di apprendimento deep per regolarizzare lo spazio di incorporazione invece dello spazio dei parametri.

L'articolo sostiene la necessità di codificare la conoscenza esterna nello layer di incorporazione linguistica di una rete neurale multimodale, come un insieme di vincoli rigidi.


Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.To combine these two concepts, we present a new kind of neural networksâ€”algorithmic neural networks (AlgoNets).These networks integrate smooth versions of classic algorithms into the topology of neural networks.Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.

Risolvere problemi inversi usando approssimazioni smooth degli algoritmi forward per addestrare i modelli inversi.


Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.Typical convolutional attentions maps are prune to high false positive regions.To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.A public reproducible PyTorch implementation is provided.

Un metodo di apprendimento deep per la localizzazione puntiforme weakly-supervised che impara usando solo le label a livello di immagine. Si basa sull'entropia condizionale per localizzare le regioni rilevanti e irrilevanti con l'obiettivo di minimizzare le regioni false positive.

Questo lavoro esplora il problema del WSL usando un nuovo design di termini di regolarizzazione e un algoritmo di cancellazione ricorsivo.

Questo articolo presenta un nuovo approccio weakly-supervised per l'apprendimento della segmentazione degli oggetti con label di classe a livello di immagine.


Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency.Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model.One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences.Search-control is critical to improve learning efficiency.In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function.Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct.We empirically show that a high frequency function is more difficult to approximate.This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples.We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach.We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.

Acquisire stati dalla regione ad alta frequenza per il search-control in Dyna.

Gli autori propongono di fare il campionamento nel dominio delle alte frequenze per aumentare la sample efficiency

Questo articolo propone un nuovo modo di selezionare gli stati da cui fare le transizioni nell'algoritmo dyna.


We propose a new architecture for distributed image compression from a group of distributed data sources.The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy.The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources.Its compression capability is much better than the method of training codecs separately.Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources.We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC).Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources.Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level.To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.

Introduciamo un framework di codifica delle fonti distribuite basato su Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).

L'articolo ha proposto un autoencoder ricorrente distribuito per la compressione delle immagini che utilizza un ConvLSTM per imparare codici binari che sono costruiti progressivamente dai residui delle informazioni precedentemente codificate

Gli autori propongono un metodo per addestrare modelli di compressione delle immagini su fonti multiple, con un encoder separato su ogni fonte e un decoder condiviso.


Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

I gate fanno tutto il lavoro pesante negli LSTM calcolando somme pesate in base agli elementi, e la rimozione della RNN semplice interna non degrada le prestazioni del modello.

Questo articolo propone una variante semplificata di LSTM rimuovendo la non linearità dell'elemento contenuto e del gate di uscita

Questo articolo presenta un'analisi delle LSTMS, mostrando che hanno una forma in cui il contenuto della cella di memoria ad ogni passo è una combinazione pesata dei valori di content update calcolati ad ogni passo temporale e offre una semplificazione delle LSTM che calcola il valore della cella di memoria ad ogni passo temporale in termini di una funzione deterministica dell'input piuttosto che una funzione dell'input e del contesto corrente.

L'articolo propone una nuova visione di LSTM in cui il focus è una somma pesata element-wise e sostiene che LSTM è ridondante mantenendo solo input gate e forget gate per calcolare i pesi


Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.  Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.

Analizzando più di 300 articoli nelle recenti conferenze sul machine learning, abbiamo scoperto che le applicazioni di Machine Learning for Health (ML4H) sono in ritardo rispetto ad altri campi di apprendimento automatico in termini di metriche di riproducibilità.

Questo documento conduce una revisione quantitativa e qualitativa dello stato della riproducibilità per le applicazioni sanitarie ML e propone raccomandazioni per rendere la ricerca più riproducibile.


We propose a solution for evaluation of mathematical expression.However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.More difficult or complex task can then be solved using a combination of these smaller network.In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc).These fundamental operations are then learned using simple feed forward neural networks.We then shows that different operations can be designed simply by reusing these smaller networks.As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.

Addestriamo molte piccole reti ognuna per una specifica operazione, queste sono poi combinate per eseguire operazioni complesse

Questo articolo propone di usare le reti neurali per valutare le espressioni matematiche progettando 8 piccoli building block per 8 operazioni fondamentali, ad esempio, addizione, sottrazione, ecc. e poi progettando la moltiplicazione e la divisione a più cifre usando questi piccoli blocchi.

L'articolo propone un metodo per progettare un motore di valutazione delle espressioni matematiche basato su NN.


In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.The generator is trained to increase the probability that fake data is real.We argue that it should also simultaneously decrease the probability that real data is real because1) this would account for a priori knowledge that half of the data in the mini-batch is fake,2) this would be observed with divergence minimization, and3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).We show that IPM-based GANs are a subset of RGANs which use the identity function. Empirically, we observe that1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.

Migliorare la qualità e la stabilità delle GAN usando un discriminatore relativistico; le GAN IPM (come WGAN-GP) sono un caso speciale.

L'articolo propone un "discriminatore relativistico", che aiuta in alcune setting, anche se un po' sensibile a iperparametri, architetture e dataset.

In questo lavoro, gli autori considerano una variante di GAN diminuendo simultaneamente la probabilità che i dati reali siano reali per il generatore.


Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

Una versione di MPO basata sulla funzione stato-valore che raggiunge buoni risultati in una vasta gamma di task nel controllo discreto e continuo.

Questo articolo presenta un algoritmo per il reinforcement learning on-policy che può gestire sia il controllo continuo/discreto, l'apprendimento singolo/multi-task e usare sia stati che pixel a bassa dimensione.

L'articolo propone una variante online di MPO, V-MPO, che impara la funzione V e aggiorna la distribuzione non parametrica verso i vantaggi.


Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.We study this problem through the lens of fundamental computer science problems: sorting and graph processing.We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.

Proponiamo motori di esecuzione neurali (NEE), che sfruttano una maschera appresa e tracce di esecuzione supervised per imitare la funzionalità delle subroutine e dimostrare una forte generalizzazione.

Questo articolo indaga un problema di costruzione di un motore di esecuzione di programmi con reti neurali e propone un modello basato su trasformer per imparare le subroutine di base e le applica in diversi algoritmi standard.

Questo articolo affronta il problema della progettazione di architetture di reti neurali che possono imparare e implementare programmi generali.


Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.

Il rilevamento bayesiano dei punti di cambiamento permette il meta learning direttamente dai dati delle serie temporali.

L'articolo considera il meta learning nel setting del task non segmentato e applica il rilevamento bayesiano online del punto di cambiamento con il meta learning.

Questo articolo spinge il meta learning verso setting non segmentati, dove il framework MOCA adotta uno schema di stima bayesiana dei punti di cambiamento per il rilevamento dei cambiamenti di attività.


People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.Fricative phonemes have an important part of their content concentrated in high frequency bands.It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.

Un approccio basato sul deep learning per il rilevamento di fonemi fricativi a ritardo zero

Questo articolo applica metodi di deep learning supervised per rilevare la durata esatta di un fonema fricativo al fine di migliorare l'algoritmo pratico di abbassamento della frequenza.


Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.

Un meccanismo di attention online e a tempo lineare che esegue un'attention soft su pezzi della sequenza di input localizzati in modo adattivo.

Questo articolo propone una piccola modifica all'attention monotonica in [1] aggiungendo un'attention soft al segmento previsto dall'attention monotonica.

L'articolo propone un'estensione di un precedente modello di attention monotona (Raffel et al 2017) per usare attentetion su una finestra di dimensioni fisse fino alla posizione di allineamento.


We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network.An image patch is a group of pixels residing in a continuous area contained in the sample.Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training.In addition, we conduct systematic experiments and provide evidence that CNNâ€™s generalization capabilities do not correlate with human recognizable features present in training samples.We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance.Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.

Sviluppare nuove tecniche che si basino sul riordino delle patch per consentire un'analisi dettagliata della relazione tra i dataset e le prestazioni di training e generalizzazione.


Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning.One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations.However, domain randomization is highly inefficient and may lead to policies with high variance across domains.In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant.We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.

Produciamo agenti di reinforcement learning che generalizzano bene ad una vasta gamma di ambienti utilizzando una nuova tecnica di regolarizzazione.

L'articolo introduce la sfida delle policy ad alta varianza nella randomizzazione del dominio per il reinforcement learning e si concentra principalmente sul problema della randomizzazione visiva, dove i diversi domini randomizzati differiscono solo nello spazio di stato e le reward e le dinamiche sottostanti sono le stesse.

Per migliorare la capacità di generalizzazione degli agenti di deep RL attraverso i task con diversi modelli visivi, questo articolo ha proposto una semplice tecnica di regolarizzazione per la randomizzazione del dominio.


Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest.The field of deep neural networks has mostly left inspiration from these claims out.In this paper, we propose three architectures and use each of them to explore the intersection of network neuroscience and deep learning in an attempt to bridge the gap between the two fields.Using the teachings from network neuroscience and connectomics, we show improvements over the ResNet architecture, we show a possible connection between early training and the spectral properties of the network, and we show the trainability of a DNN based on the neuronal network of C.Elegans.

Esploriamo l'intersezione tra le neuroscienze di rete e il deep learning.


Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.  In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.  Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.We demonstrate this by constructing a high precision (typically 97\%+) knowledge base for people from a single seed fact.

Questo articolo presenta un sistema per la costruzione unsupervised e ad alta precisione di knowledge base, utilizzando un programma probabilistico per definire un processo di conversione dei fatti della knowledge base in testo non strutturato.

Panoramica sulla knowledge base esistente che è costruita con un modello probabilistico, con l'approccio di costruzione della knowledge base valutato contro altri approcci di knowledge base YAGO2, NELL, Knowledge Vault, e DeepDive.

Questo articolo usa un programma probabilistico che descrive il processo attraverso il quale i fatti che descrivono le entità possono essere realizzati nel testo e in un gran numero di pagine web, per imparare ad eseguire l'estrazione di fatti sulle persone usando un singolo fatto come seed.


Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations.Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms.Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method.We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.

Nuovo metodo di estrazione del segnale nel dominio di Fourier


We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.The networkâ€™s ability to capture interaction dynamics in swarms is demonstrated through transfer learning.We finally discuss the inherent availability and challenges in the scalability of GNN, and proposed a method to improve it with layer-wise tuning and mixing of data enabled by padding.

Migliorare la scalabilità delle graph neural networks sull'imitation learning e la previsione del movimento dello swarm

L'articolo propone un nuovo modello di serie temporali per l'apprendimento di una sequenza di grafi.

Questo lavoro considera i problemi di predizione delle sequenze in un sistema multi-agente.


Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints.In this work, we propose a generic and end-to-end learnable compression framework termed differentiable product quantization (DPQ).We present two instantiations of DPQ that leverage different approximation techniques to enable differentiability in end-to-end learning.Our method can readily serve as a drop-in alternative for any existing embedding layer.Empirically, DPQ offers significant compression ratios (14-238x) at negligible or no performance cost on 10 datasets across three different language tasks.

Proponiamo un framework di quantizzazione del prodotto differenziabile che può ridurre la dimensione del layer di embedding in un training end-to-end senza costi di performance.

Questo articolo lavora sui metodi per comprimere i layer di embedding per l'inferenza a basso costo di memoria, dove gli embedding compressi sono appresi insieme ai modelli specifici del task in un modo differenziabile end-to-end.


For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.We empirically demonstrate on several synthetic problems that our method(i) can learn multi-valued functions and produce the conditional modes,(ii) scales well to high-dimensional inputs and(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.

Introduciamo un semplice e nuovo algoritmo di regressione modale che è facile da scalare a problemi di grossa taglia.

L'articolo propone un approccio di funzione implicita per imparare le modalità di regressione multimodale.

Il presente lavoro propone un approccio parametrico per stimare il modo condizionale usando il Teorema della Funzione Implicita per distribuzioni multimodali.


Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.Current methods rely heavily on on-policy experience, limiting their sample efficiency.They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.This probabilistic interpretation enables posterior sampling for structured and efficient exploration.We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.

Meta-RL sample-efficient combinando l'inferenza variazionale delle variabili probabilistiche del task con RL off-policy

Questo articolo propone di usare RL off-policy durante il meta-training per migliorare notevolmente la sample-efficiency dei metodi Meta-RL.


Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications.However, existing knowledge bases contain very little data compared to the wealth of information on the Web.This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from.Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall.As a result, these large-scale extractions remain unexploited.In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes.MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base.We make three major contributions.First, we introduce a novel concept, web source slices, to describe the contents of a web source.Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base.Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices.We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.

Questo articolo si concentra sull'identificazione di fonti web di alta qualità per la pipeline di arricchimento della knowledge base industriale.


We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.Challenges arise in practice.As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.Worse yet, we have no prior knowledge on the underlying model for a given scenario.These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances.To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common.This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand.Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms.It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets.Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.

Indaghiamo i meriti dell'impiego di reti neurali nel problema della predizione delle corrispondenze, dove si cerca di stimare la probabilità che un gruppo di M oggetti sia preferito ad un altro, sulla base di dati parziali di confronto di gruppo.

Questo articolo propone una soluzione di deep neural network al problema della classificazione degli insiemi e progetta un'architettura per questo task ispirata da precedenti algoritmi progettati manualmente.

Questo articolo fornisce una tecnica per risolvere il problema della predizione delle partite utilizzando un'architettura di deep learning.


Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.  Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs).  We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices.This is done by parametrizing with a skew-symmetric matrix using the Cayley transform.Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones.  The proposed training scheme involves a straightforward gradient calculation and update step.In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.

Un nuovo approccio per mantenere matrici di peso ricorrenti ortogonali in una RNN.

Introduce uno schema per l'apprendimento della matrice dei parametri ricorrenti in una rete neurale che usa la trasformata di Cayley e una matrice di peso di scaling.

Questo articolo suggerisce una riparametrizzazione dei pesi ricorrenti di una RNN con una matrice asimmetrica utilizzando la trasformata di Cayley per mantenere la matrice dei pesi ricorrenti ortogonale.

Una nuova parametrizzazione delle RNN permette di rappresentare le matrici di peso ortogonali in modo relativamente semplice.


A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language.These seemingly very different tasks are usually solved by specially designed architectures.In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks.We perform extensive experiments to test this insight on 10 disparate tasks as broad as dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving comparable performance as state-of-the-art specialized models.We further demonstrate benefits in multi-task learning.We convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.

Usiamo un unico modello per risolvere una grande varietà di task di analisi del linguaggio naturale formulandoli in un formato unificato di span-relation.

Questo articolo generalizza una vasta gamma di task di natural language processing in un unico framework basato sullo span e propone un'architettura generale per risolvere tutti questi problemi.

Questo lavoro presenta una formulazione unificata di vari task NLP a livello di frase e di token.


Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models.With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results.We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.Our bound instead directly parameterises a free matrix, which is an additional variational parameter.At the local maxima of the bound, this matrix is equal to the matrix inverse.We prove that our bound gives the same guarantees as earlier variational approximations.We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.

Presentiamo un limite inferiore variazionale per i modelli GP che può essere ottimizzato senza calcolare operazioni matriciali costose come le inverse, fornendo al tempo stesso le stesse garanzie delle approssimazioni variazionali esistenti.


It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations.Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature.While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models.We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned.This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.

Gli autoencoder variazionali con spazi latenti modellati come prodotti di manifold Riemanni a curvatura costante migliorano la ricostruzione delle immagini rispetto alle varianti a singolo manifold.

Questo articolo introduce una formulazione generale della nozione di VAE con uno spazio latente composto da un manifold curvo.

Questo articolo riguarda lo sviluppo di VAE in spazi non euclidei.


Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery.We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties.Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step.We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm.We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.This simple, powerful technique operates strictly on the inputs and outputs of any translation model.We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models.Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "``for" loop.Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.

Introduciamo un algoritmo black box per l'ottimizzazione ripetuta dei composti utilizzando un framework di traduzione.

Gli autori inquadrano l'ottimizzazione delle molecole come un problema sequence-to-sequence, ed estendono i metodi esistenti per migliorare le molecole, mostrando che è vantaggioso per ottimizzare il logP ma non il QED.

L'articolo si basa su modelli di traduzione esistenti sviluppati per l'ottimizzazione molecolare, facendo un uso iterativo di modelli di traduzione da sequenza a sequenza o da grafo a grafo.


Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance.The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application.A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement.We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario.BlackMarks takes the pre-trained unmarked model and the ownerâ€™s binary signature as inputs.The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark.To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit â€˜0â€™ and bit â€˜1â€™.Given the ownerâ€™s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks.The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss.To extract the WM, BlackMarks queries the model with the WM key images and decodes the ownerâ€™s signature from the corresponding predictions using the designed encoding scheme.We perform a comprehensive evaluation of BlackMarksâ€™ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness.BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.

Proponiamo il primo framework di watermarking per l'embedding e l'estrazione della firma multi-bit usando le uscite della DNN.

Propone un metodo per il watermarking multi-bit delle reti neurali in un ambiente black-box e dimostra che le predizioni dei modelli esistenti possono trasportare una stringa multi-bit che può essere utilizzata in seguito per verificare l'appartenenza.

L'articolo propone un approccio per il watermarking del modello in cui la filigrana è una stringa di bit incorporata nel modello come parte di una procedura di fine-tuning


Adversarial training provides a principled approach for training robust neural networks.From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem.The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples.Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure.This work proposes a new adversarial training method based on a general learning-to-learn framework.Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network.At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer.From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.

Non sai come ottimizzare? Allora impara a ottimizzare!

Questo articolo propone un modo per addestrare i modelli di classificazione delle immagini per essere resistenti agli attacchi di perturbazione L-infinity.

Questo articolo propone di utilizzare un framework learning-to-learn per imparare un attacker.


In this work we introduce a new framework for performing temporal predictionsin the presence of uncertainty.It is based on a simple idea of disentangling com-ponents of the future state which are predictable from those which are inherentlyunpredictable, and encoding the unpredictable components into a low-dimensionallatent variable which is fed into the forward model.Our method uses a simple su-pervised training objective which is fast and easy to train.We evaluate it in thecontext of video prediction on multiple datasets and show that it is able to consi-tently generate diverse predictions without the need for alternating minimizationover a latent space or adversarial training.

Un metodo semplice e facile da addestrare per la predizione multimodale nelle serie temporali.

Questo articolo introduce un modello di previsione di serie temporali che impara una mappatura deterministica e allena un'altra rete per prevedere i fotogrammi futuri dato l'input e l'errore residuo della prima rete.

L'articolo propone un modello per la predizione sotto incertezza in cui si separano la predizione deterministica dei componenti e la predizione incerta dei componenti.


Conducting reinforcement-learning experiments can be a complex and timely process.A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof.In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments.This paper gives an overview  of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.

Questo articolo introduce e motiva simple_rl, una nuova libreria open source per realizzare esperimenti di reinforcement learning in Python 2 e 3 con un focus sulla semplicità.


Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint.In this study, we prove the local stability of optimizing the simple gradient penalty $\mu$-WGAN(SGP $\mu$-WGAN) under suitable assumptions regarding the equilibrium and penalty measure $\mu$.The measure valued differentiation concept is employed to deal with the derivative of the penalty terms, which is helpful for handling abstract singular measures with lower dimensional support.Based on this analysis, we claim that penalizing the data manifold or sample manifold is the key to regularizing the original WGAN with a gradient penalty.Experimental results obtained with unintuitive penalty measures that satisfy our assumptions are also provided to support our theoretical results.

Questo articolo si occupa della stabilità di una semplice ottimizzazione con gradient penalty $\mu$-WGAN introducendo un concetto di differenziazione valutato da una misura.

Si studia la WGAN con squared zero centered gradient penalty per una misura generale.

Caratterizza la convergenza di Wasserstein GAN penalizzata dal gradiente.


We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values.Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation.  We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks.We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.

Metodo di training allo stato dell'arte per reti a pesi binari e ternari basato sull'ottimizzazione alternata di partizioni di peso con rilassamento continuo random.

L'articolo propone un nuovo schema di training per ottimizzare una rete neurale ternaria.

Gli autori propongono RPR, un modo per partizionare e quantizzare i pesi in modo casuale e addestrare i parametri rimanenti seguiti dal rilassamento a cicli alternati per addestrare i modelli quantizzati.