Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\boldsymbol{\gamma}$ for expectation-based objectives $\mathbb{E}_{q_{\boldsymbol{\gamma}} (\boldsymbol{y})} [f (\boldsymbol{y}) ]$. Most existing methods either ($i$) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or ($ii$) they only apply to reparameterizable continuous random variables and employ a reparameterization trick. To address these limitations, we propose a General and One-sample (GO) gradient that ($i$) applies to many distributions associated with non-reparameterizable continuous {\em or} discrete random variables, and ($ii$) has the same low-variance as the reparameterization trick. We find that the GO gradient often works well in practice based on only one Monte Carlo sample (although one can of course use more samples if desired). Alongside the GO gradient, we develop a means of propagating the chain rule through distributions, yielding statistical back-propagation, coupling neural networks to common random variables.

Un gradiente Rep-like per distribuzioni continue/discrete non riparametrizzabili; ulteriormente generalizzato a deep probabilistic model, ottenendo una back-propagation statistica

Presenta uno stimatore del gradiente per obiettivi basati sull'aspettazione che è unbiased, ha una bassa varianza e si applica a variabili casuali continue e discrete.

Un metodo migliorato per calcolare le derivate dell'aspettazione e un nuovo stimatore di gradiente a bassa varianza che permette il training di modelli generativi in cui le osservazioni o le variabili latenti sono discrete.

Formula un gradiente a bassa varianza per distribuzioni associate a variabili casuali continue o discrete.


Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.

Mostriamo che i parametri della NN e i landscape dei costi degli iperparametri possono essere generati come stati quantici usando un singolo circuito quantico e che questi possono essere usati per il training e il meta-training.

Descrive un metodo in cui un framework di deep learning può essere quantizzato considerando la forma a due stati di una sfera di Bloch/qubit e creando una rete neurale binaria quantistica.

Questo articolo propone l'amplificazione di ampiezza quantistica, un nuovo algoritmo per il training e la selezione del modello nelle reti neurali binarie.

Propone un'idea nuova di produrre uno stato quantico che rappresenta un panorama completo dei costi di tutti i parametri per una data rete neurale binaria, costruendo una rete neurale binaria quantica (QBNN).


Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the adversarial transformations are equally important, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier's performance for tasks where some adversarial transformation are more important than others. We encode the potential harm of each adversarial transformation in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models with a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.

Un metodo generale per il training di classificatori robusti certificati sensibili ai costi contro le adversarial perturbation

Calcola e inserisce i costi degli adversarial attack nell'obiettivo di ottimizzazione per ottenere un modello che sia robusto in termi di sensitività ai costi contro gli adversarial attack.

Costruisce sul lavoro seminale di Dalvi et al. ed estende l'approccio alla robustezza certificabile con una matrice di costo che specifica per ogni coppia di classi source-target se il modello debba essere robusto agli adversarial example.


Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons,  causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce  normal patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected that produces a neural response as close as possible to the desired response. This requires a technique for computing a distance between the desired response and the achievable response that is meaningful in terms of the visual signal being conveyed. Here we propose a method to learn such a metric on neural responses, directly from recorded light responses of a population of retinal ganglion cells (RGCs) in the primate retina. The learned metric produces a measure of similarity of RGC population responses that accurately reflects the similarity of the visual input. Using data from electrical stimulation experiments, we demonstrate that this metric may improve the performance of a prosthesis.

Usare le triplette per imparare una metrica per confrontare le risposte neurali e migliorare le prestazioni di una protesi.

Gli autori sviluppano nuove metriche di distanza degli spike train, comprese le reti neurali e le metriche quadratiche. Queste metriche hanno dimostrato di superare la metrica banale della distanza di Hamming, e catturano implicitamente alcune strutture nel codice neurale.

Avendo in mente l'applicazione di migliorare le protesi neurali, gli autori propongono di imparare una metrica tra le risposte neurali ottimizzando una forma quadratica o una deep neural network.


We introduce a novel workflow, QCue, for providing textual stimulation during mind-mapping. Mind-mapping is a powerful tool whose intent is to allow one to externalize ideas and their relationships surrounding a central problem. The key challenge in mind-mapping is the difficulty in balancing the exploration of different aspects of the problem (breadth) with a detailed exploration of each of those aspects (depth). Our idea behind QCue is based on two mechanisms: (1) computer-generated automatic cues to stimulate the user to explore the breadth of topics based on the temporal and topological evolution of a mind-map and (2) user-elicited queries for helping the user explore the depth for a given topic. We present a two-phase study wherein the first phase provided insights that led to the development of our work-flow for stimulating the user through cues and queries. In the second phase, we present a between-subjects evaluation comparing QCue with a digital mind-mapping work-flow without computer intervention. Finally, we present an expert rater evaluation of the mind-maps created by users in conjunction with user feedback.

Questo articolo introduce un metodo per generare domande (spunti) e domande (suggerimenti) per aiutare gli utenti a eseguire il mind-mapping.

Presenta uno strumento per assistere il mind-mapping attraverso il contesto suggerito relativo ai nodi esistenti e attraverso domande che espandono i rami meno sviluppati.

Questo articolo presenta un approccio per assistere le persone nei task di mindmapping, progettando un'interfaccia e feature algoritmiche per supportare il mindmapping, e contribuisce a uno studio valutativo.


The ability to detect when an input sample was not drawn from the training distribution is an important  desirable property of deep neural networks. In this paper, we show that a simple ensembling of first and second order deep feature statistics can be exploited to effectively differentiate in-distribution and out-of-distribution samples. Specifically, we observe that  the mean and standard deviation within feature maps  differs greatly between in-distribution and out-of-distribution samples. Based on this observation, we propose a simple and  efficient plug-and-play detection procedure that does not require re-training, pre-processing or changes to the model.   The proposed method outperforms the state-of-the-art by a large margin in all standard benchmarking tasks, while being much simpler to implement and execute. Notably, our method improves the true negative rate from 39.6% to 95.3% when 95% of in-distribution (CIFAR-100) are correctly detected using a DenseNet and the out-of-distribution dataset is TinyImageNet resize. The source code of our method will be made publicly available.

Rilevamento di sample fuori distribuzione utilizzando statistiche di feature di basso ordine senza richiedere alcun cambiamento nella DNN sottostante.

Presenta un algoritmo per rilevare i sample fuori distribuzione utilizzando una running estimate della media e della varianza all'interno dei layer BatchNorm per costruire rappresentazioni di feature che vengono poi passate ad un classificatore lineare.

Un approccio per il rilevamento di sample fuori distribuzione in cui gli autori propongono di utilizzare la regressione logistica sulle statistiche semplici di ogni layer di batch normalization della CNN.

L'articolo suggerisce di usare i punteggi Z per confrontare i sample ID e OOD per valutare ciò che le deep network stanno cercando di fare.


Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. However, most of the work in binary code vulnerability detection has relied on handcrafted features which are manually chosen by a select few, knowledgeable domain experts. In this paper, we attempt to alleviate this severe binary vulnerability detection bottleneck by leveraging recent advances in deep learning representations and propose the Maximal Divergence Sequential Auto-Encoder. In particular, latent codes representing vulnerable and non-vulnerable binaries are encouraged to be maximally divergent, while still being able to maintain crucial information from the original binaries. We conducted extensive experiments to compare and contrast our proposed methods with the baselines, and the results show that our proposed methods outperform the baselines in all performance measures of interest.

Proponiamo un nuovo metodo chiamato Maximal Divergence Sequential Auto-Encoder che sfrutta la rappresentazione del Variational AutoEncoder per il rilevamento delle vulnerabilità del codice binario.

Questo articolo propone un'architettura basata sull'autoencoder variazionale per l'embedding del codice per il rilevamento delle vulnerabilità del software binario, con il risultato che gli embedding appresi sono più efficaci nel distinguere tra codice binario vulnerabile e non vulnerabile rispetto alle baseline.

Questo articolo propone un modello per estrarre automaticamente le feature per il rilevamento della vulnerabilità utilizzando la tecnica del deep learning.


Modern neural architectures critically rely on attention for mapping structured inputs to sequences. In this paper we show that prevalent attention architectures do not adequately model the dependence among the attention and output tokens across a predicted sequence.  We present an alternative architecture called  Posterior Attention Models that after a principled factorization of the full joint distribution of the attention and output variables, proposes two major changes.   First, the position where attention is marginalized is changed from the input to the output. Second, the attention propagated to the next decoding stage is a posterior attention distribution conditioned on the output. Empirically on five translation and two morphological inflection tasks the proposed posterior attention models yield better BLEU score and alignment accuracy than existing attention models.

Calcolare l'attention in base alla distribuzione posteriore porta a un'attention più significativa e a prestazioni migliori

Questo articolo propone un modello sequence-to-sequence in cui l'attention è trattata come una variabile latente, e deriva nuove procedure di inferenza per questo modello, ottenendo miglioramenti nella traduzione automatica e nei task di generazione di inflessioni morfologiche.

Questo articolo presenta un nuovo modello di attention posteriore per problemi seq2seq


The growing interest to implement Deep Neural Networks (DNNs) on resource-bound hardware has motivated innovation of compression algorithms. Using these algorithms, DNN model sizes can be substantially reduced, with little to no accuracy degradation. This is achieved by either eliminating components from the model, or penalizing complexity during training. While both approaches demonstrate considerable compressions, the former often ignores the loss function during compression while the later produces unpredictable compressions. In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function. In this technique, we formulate compression as a constrained optimization problem, and then present a solution for it. We will show that using this technique, we can achieve competitive results.

Comprimere i modelli DNN addestrati minimizzando la loro complessità mentre si limita la loro loss.

Questo articolo propone un metodo per la compressione delle deep neural network sotto vincoli di precisione.

Questo articolo presenta un metodo di codifica k-means vincolato al valore della loss per la compressione della rete e sviluppa un algoritmo iterativo per l'ottimizzazione del modello.


Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned mechanisms that contribute to such effective behaviors and more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision and natural language processing. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.

Sviluppiamo una tecnica per visualizzare i meccanismi di attention in reti neurali arbitrarie.

Propone di imparare una rete di attention latente che può aiutare a visualizzare la struttura interna di una deep neural network.

Gli autori di questo articolo propongono uno schema di visualizzazione black-box guidato dai dati.


The design of small molecules with bespoke properties is of central importance to drug discovery.   However significant challenges yet remain for computational methods, despite recent advances such as deep recurrent networks and reinforcement learning strategies for sequence generation, and it can be difficult to compare results across different works.   This work proposes 19 benchmarks selected by subject experts, expands smaller datasets previously used to approximately 1.1 million training molecules, and explores how to apply new reinforcement learning techniques effectively for molecular design.   The benchmarks here, built as OpenAI Gym environments, will be open-sourced to encourage innovation in molecular design algorithms and to enable usage by those without a background in chemistry.   Finally, this work explores recent development in reinforcement-learning methods with excellent sample complexity (the A2C and PPO algorithms) and investigates their behavior in molecular generation, demonstrating significant performance gains compared to standard reinforcement learning techniques.

Indaghiamo una varietà di algoritmi RL per la generazione molecolare e definiamo nuovi benchmark (da rilasciare come OpenAI Gym), trovando che PPO e un algoritmo hill-climbing MLE funzionano meglio.

Considera la valutazione del modello per la generazione di molecole proponendo 19 benchmark, espandendo piccoli dataset a un grande dataset standardizzato, ed esplorando come applicare le tecniche RL per la progettazione molecolare.

Questo articolo mostra che i metodi RL più sofisticati sono meno efficaci della semplice tecnica hill-climbing (eccetto PPO) quando si modellano e sintetizzano le molecole.


Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.

La capacità più robusta per il ragionamento analogico è indotta quando le reti imparano le analogie contrastando le strutture relazionali astratte nei loro domini di input.

L'articolo studia la capacità di una rete neurale di imparare l'analogia, mostrando che una semplice rete neurale è in grado di risolvere alcuni problemi di analogia

Questo articolo descrive un approccio per addestrare reti neurali per task di ragionamento analogico, considerando in particolare l'analogia visiva e le analogie simboliche.


Building chatbots that can accomplish goals such as booking a flight ticket is an unsolved problem in natural language understanding. Much progress has been made to build conversation models using techniques such as sequence2sequence modeling. One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals. Recently, many methods have been proposed to address this issue by optimizing a reward that contains task status or outcome. However, adding the reward optimization on the fly usually provides little guidance for language construction and the conversation model soon becomes decoupled from the language model. In this paper, we propose a new setting in goal-oriented dialogue system to tighten the gap between these two aspects by enforcing model level information isolation on individual models between two agents. Language construction now becomes an important part in reward optimization since it is the only way information can be exchanged. We experimented our models using self-play and results showed that our method not only beat the baseline sequence2sequence model in rewards but can also generate human-readable meaningful conversations of comparable quality.

Un modello di conversazione neurale goal-oriented attraverso il self-play

Un modello di self-play per la generazione di dialoghi goal-oriented, che mira a rafforzare l'accoppiamento tra la reward del task e il language model.

Questo articolo descrive un metodo per migliorare un sistema di dialogo goal-oriented usando il self-play.


Search engine users nowadays heavily depend on query completion and correction to shape their queries.  Typically, the completion is done by database lookup which does not understand the context and cannot generalize to prefixes not in the database . In the paper, we propose to use unsupervised deep language models to complete and correct the queries given an arbitrary prefix .  We show how to address two main challenges that renders this method practical for large-scale deployment : 1) we propose a method for integrating error correction into the language model completion via a edit-distance potential and a variant of beam search that can exploit these potential functions; and 2) we show how to efficiently perform CPU-based computation to complete the queries, with error correction, in real time (generating top 10 completions within 16 ms). Experiments show that the method substantially increases hit rate over standard approaches, and is capable of handling tail queries.

Completamento delle query di ricerca in tempo reale utilizzando language model LSTM a livello di carattere

Questo articolo presenta metodi per il completamento delle query che includono la correzione dei prefissi, e alcuni dettagli tecnici per soddisfare particolari requisiti di latenza su una CPU.

Gli autori propongono un algoritmo per risolvere il problema del completamento della query con la correzione degli errori, e adottano una modellazione basata su RNN a livello di carattere e ottimizzano la parte di inferenza per raggiungere gli obiettivi in tempo reale.


RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical convergence properties have remained unclear. Further, recent work has seemed to suggest that these algorithms have worse generalization properties when compared to carefully tuned stochastic gradient descent or its momentum variants. In this work, we make progress towards a deeper understanding of ADAM and RMSProp in two ways. First, we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives, and we give bounds on the running time.   Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter \beta_1. We show that at very high values of the momentum parameter (\beta_1 = 0.99) ADAM outperforms a carefully tuned NAG on most of our experiments, in terms of getting lower training and test losses. On the other hand, NAG can sometimes do better when ADAM's \beta_1 is set to the most commonly used value: \beta_1 = 0.9, indicating the importance of tuning the hyperparameters of ADAM to get better generalization performance.   We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms, and it also produces iterates which exhibit an increasing trend for the minimum eigenvalue of the Hessian of the loss function at the iterates.

In questo articolo dimostriamo la convergenza alla criticità di RMSProp (stocastico e deterministico) e ADAM deterministico per obiettivi smooth non convessi e dimostriamo un'interessante sensibilità in termini di beta_1 per ADAM su autoencoder.

Questo articolo presenta un'analisi di convergenza di RMSProp e ADAM nel caso di funzioni smooth non convesse


Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2 algorithm. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.

L'elaborazione di meccanismi di difesa non supervisionati contro gli adversarial attack è fondamentale per garantire la generalizzabilità della difesa.

Questo articolo presenta un metodo per rilevare adversarial example in un ambiente di classificazione di deep learning

Questo articolo presenta un metodo unsupervised per rilevare adversarial example di reti neurali.


Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.

Ricerca di architetture neurali senza proxy per l'apprendimento diretto di architetture su task di larga scala (ImageNet) riducendo il costo allo stesso livello del normale training.

Questo articolo affronta il problema della ricerca dell'architettura, e in particolare cerca di farlo senza doversi allenare su task "proxy" dove il problema è semplificato per via di un'ottimizzazione, di una complessità dell'architettura o di una dimensione del dataset più trattabili.


With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to have very little control over its uncertainty for test examples, which potentially causes very harmful and annoying consequences in practical scenarios. In this paper, we are particularly interested in designing a higher-order uncertainty metric for deep neural networks and investigate its performance on the out-of-distribution detection task proposed by~\cite{hendrycks2016baseline}. Our method first assumes there exists a underlying higher-order distribution $\mathcal{P}(z)$ , which generated label-wise distribution $\mathcal{P}(y)$ over classes on the K-dimension simplex, and then approximate such higher-order distribution via parameterized posterior function $p_{\theta}(z|x)$ under variational inference framework, finally we use the entropy of learned posterior distribution $p_{\theta}(z|x)$ as uncertainty measure to detect out-of-distribution examples. However , we identify the overwhelming over-concentration issue in such a framework, which greatly hinders the detection performance. Therefore , we further design a log-smoothing function to alleviate such issue to greatly increase the robustness of the proposed entropy-based uncertainty measure. Through comprehensive experiments on various datasets and architectures, our proposed variational Dirichlet framework with entropy-based uncertainty measure is consistently observed to yield significant improvements over many baseline systems.

Un nuovo framework basato sull'inferenza variazionale per out-of-distribution detection

Descrive un approccio probabilistico per quantificare l'incertezza nei task di classificazione DNN che supera altri metodi SOTA nel task di out-of-distribution detection.

Un nuovo framework per il rilevamento di out-of-distribution, basato sull'inferenza variazionale e una distribuzione di Dirichlet a priori, che riporta risultati allo stato dell'arte su diversi dataset.

Out-of-distribution detection tramite un nuovo metodo per approssimare la distribuzione di fiducia della probabilità di classificazione usando l'inferenza variazionale della distribuzione di Dirichlet.


Intelligent agents can learn to represent the action spaces of other agents simply by observing them act. Such representations help agents quickly learn to predict the effects of their own actions on the environment and to plan complex action sequences. In this work, we address the problem of learning an agent’s action space purely from visual observation. We use stochastic video prediction to learn a latent variable that captures the scene's dynamics while being minimally sensitive to the scene's static content. We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions. We call the full model with composable action representations Composable Learned Action Space Predictor (CLASP). We show the applicability of our method to synthetic settings and its potential to capture action spaces in complex, realistic visual settings. When used in a semi-supervised setting, our learned representations perform comparably to existing fully supervised methods on tasks such as action-conditioned video prediction and planning in the learned action space, while requiring orders of magnitude fewer action labels. Project website: https://daniilidis-group.github.io/learned_action_spaces

Impariamo una rappresentazione dello spazio d'azione di un agente dalle pure osservazioni visive. Usiamo un approccio con variabili latenti ricorrenti con una nuova loss di componibilità.

Propone un modello compositivo a variabili latenti per imparare modelli che prevedano cosa accadrà dopo in scenari in cui le action-label non sono disponibili in abbondanza.

Un approccio basato su IB variazionale per imparare le rappresentazioni delle azioni direttamente dai video delle azioni compiute, ottenendo una migliore efficienza dei metodi di apprendimento successivi e richiedendo una minore quantità di video di action label.

Questo articolo propone un approccio alla predizione video che trova autonomamente uno spazio d'azione che codifica le differenze tra i fotogrammi successivi


When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.

Il reinforcement learning può essere usato per addestrare gli agenti a negoziare la formazione della squadra attraverso molti protocolli di negoziazione

Questo articolo studia il deep RL multi-agente in ambienti in cui tutti gli agenti devono cooperare per portare a termine un task (ad esempio, ricerca e salvataggio, videogiochi multi-player), e utilizza semplici giochi di voto ponderato cooperativo per studiare l'efficacia del deep RL e per confrontare le soluzioni trovate dal deep RL con una soluzione fair.

Un approccio di reinforcement learning per la negoziazione di coalizioni in setting di teoria dei giochi cooperativi che può essere utilizzato nei casi in cui sono disponibili simulazioni di allenamento illimitate.


Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.

Metodi non supervisionati per trovare, analizzare e controllare i neuroni importanti nella NMT

Questo articolo presenta approcci non supervisionati per scoprire i neuroni importanti nei sistemi di traduzione automatica neurale e analizza le proprietà linguistiche controllate da questi neuroni.

Metodi non supervisionati per classificare i neuroni nella traduzione automatica, dove i neuroni importanti sono così identificati e utilizzati per controllare l'output della traduzione automatica.


Recent state-of-the-art reinforcement learning algorithms are trained under the goal of excelling in one specific task. Hence, both environment and task specific knowledge are entangled into one framework. However, there are often scenarios where the environment (e.g. the physical world) is fixed while only the target task changes. Hence, borrowing the idea from hierarchical reinforcement learning, we propose a framework that disentangles task and environment specific knowledge by separating them into two units. The environment-specific unit handles how to move from one state to the target state; and the task-specific unit plans for the next target state given a specific task. The extensive results in simulators indicate that our method can efficiently separate and learn two independent units, and also adapt to a new task more efficiently than the state-of-the-art methods.

Proponiamo un framework DRL che distingue la conoscenza specifica del task e dell'ambiente.

Gli autori propongono di decomporre reinforcement learning in una funzione PATH e una funzione GOAL

Un'architettura modulare con l'obiettivo di separare la conoscenza specifica dell'ambiente e la conoscenza specifica del task in diversi moduli, alla pari con l'A3C standard in una vasta gamma di task.


Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e.g., simulation and robotics. We propose pix2scene, a deep generative-based approach that implicitly models the geometric properties of a scene from images. Our method learns the depth and orientation of scene points visible in images. Our model can then predict the structure of a scene from various, previously unseen view points. It relies on a bi-directional adversarial learning mechanism to generate scene representations from a latent code, inferring the 3D representation of the underlying scene geometry. We showcase a novel differentiable renderer to train the 3D model in an end-to-end fashion, using only images. We demonstrate the generative ability of our model qualitatively on both a custom dataset and on ShapeNet. Finally, we evaluate the effectiveness of the learned 3D scene representation in supporting a 3D spatial reasoning.

pix2scene: un approccio generativo profondo per modellare implicitamente le proprietà geometriche di una scena 3D a partire dalle immagini

Esplora la spiegazione di scene con surfel in un modello di riconoscimento neurale, e dimostra i risultati sulla ricostruzione e la sintesi dell'immagine e mental shape rotation.

Gli autori introducono un metodo per creare un modello di scena 3D dato un'immagine 2D e una posa della telecamera utilizzando un modello self-supervised


Identifying the relations that connect words is an important step towards understanding human languages and is useful for various NLP tasks such as knowledge base completion and analogical reasoning. Simple unsupervised operators such as vector offset between two-word embeddings have shown to recover some specific relationships between those words, if any. Despite this, how to accurately learn generic relation representations from word representations remains unclear. We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations. We propose a method for learning relation representations using a feed-forward neural network that performs relation prediction. Our evaluations on two benchmark datasets reveal that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words.

Identificare le relazioni che collegano le parole è importante per vari task NLP. Modelliamo la rappresentazione delle relazioni come un problema di apprendimento supervisionato e impariamo operatori parametrici che mappano i word embedding pre-addestrati alle rappresentazioni delle relazioni.

Questo articolo presenta un nuovo metodo per rappresentare le relazioni lessicali come vettori usando solo i word embedding pre-addestrati e una nuova loss che opera su coppie di coppie di parole.

Una nuova soluzione al problema della composizione delle relazioni quando si hanno già embedding di parole/entità preaddestrati e si è interessati solo ad imparare a comporre rappresentazioni di relazioni.


Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem. In this paper we propose a simple technique called fraternal dropout that takes advantage of dropout to achieve this goal. Specifically, we propose to train two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. In this way our regularization encourages the representations of RNNs to be invariant to dropout mask, thus being robust. We show that our regularization term is upper bounded by the expectation-linear dropout objective which has been shown to address the gap due to the difference between the train and inference phases of dropout. We evaluate our model and achieve state-of-the-art results in sequence modeling tasks on two benchmark datasets - Penn Treebank and Wikitext-2. We also show that our approach leads to performance improvement by a significant margin in image captioning (Microsoft COCO) and semi-supervised (CIFAR-10) tasks.

Proponiamo di addestrare due copie identiche di una rete neurale ricorrente (che condividono i parametri) con diverse maschere di dropout, minimizzando la differenza tra le loro predizioni (pre-softmax).

Presenta Fraternal dropout come un miglioramento rispetto a Expectation-linear dropout in termini di convergenza, e dimostra l'utilità di Fraternal dropout su una serie di task e dataset.


We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.

Apprendimento di ponderazioni e deformazioni di dataset spazio-temporali per approssimazioni altamente efficienti del comportamento dei liquidi.

Un modello basato su reti neurali è usato per interpolare simulazioni per nuove condizioni di scena da superfici implicite 4D densamente registrate per una scena strutturata.

Questo articolo presenta un approccio di deep learning accoppiato per generare dati realistici di simulazione di liquidi che possono essere utili per applicazioni di supporto decisionale in tempo reale.

Questo articolo introduce un approccio di deep learning per la simulazione fisica che combina due reti per sintetizzare dati 4D che rappresentano simulazioni fisiche 3D


This is an empirical paper which constructs color invariant networks and evaluates their performances on a realistic data set. The paper studies the simplest possible case of color invariance: invariance under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color object, but its colorfulness. The data set introduced in the paper consists of images showing crashed cars from which ten classes were extracted. An additional annotation was done which labeled whether the car shown was red or non-red.   The networks were evaluated by their performance on the classification task. With the color annotation we altered the color ratios  in the training data and analyzed the generalization capabilities of the networks on the unaltered test data. We further split the test data in red and non-red cars and did a similar evaluation. It is shown in the paper that an pixel-wise ordering of the rgb-values of the images performs better or at least similarly for small deviations from the true color ratios. The limits of these networks are also discussed.

Costruiamo e valutiamo reti neurali invarianti al colore su un nuovo dataset realistico

Propone un metodo per rendere invarianti al colore le reti neurali per il riconoscimento delle immagini e lo valuta sul dataset cifar 10.

Gli autori studiano un layer di input modificato che risulta in reti invarianti al colore, e mostrano che certi layer di input invarianti al colore possono migliorare l'accuratezza per le immagini di test provenienti da una distribuzione di colore diversa dalle immagini di training.

Gli autori testano una CNN su immagini con canali di colore modificati per essere invarianti alle permutazioni, con prestazioni non troppo degradate.


Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger. For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size. In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of "overlaps" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field).  To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well. Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks. Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.

Analizziamo come il grado di sovrapposizione tra i campi recettivi di una rete convoluzionale influenza la sua potenza espressiva.

L'articolo studia la potenza espressiva fornita dalla "sovrapposizione" nei layer di convoluzione delle DNN considerando attivazioni lineari con il product pooling.

Questo articolo analizza l'espressività dei circuiti aritmetici convoluzionali e mostra che un numero esponenzialmente grande di ConvAC non sovrapposti è richiesto per approssimare il grid tensor di un ConvAC sovrapposto.


We provide a theoretical algorithm for checking local optimality and escaping saddles at nondifferentiable points of empirical risks of two-layer ReLU networks. Our algorithm receives any parameter value and returns: local minimum, second-order stationary point, or a strict descent direction. The presence of M data points on the nondifferentiability of the ReLU divides the parameter space into at most 2^M regions, which makes analysis difficult. By exploiting polyhedral geometry, we reduce the total computation down to one convex quadratic program (QP) for each hidden node, O(M) (in)equality tests, and one (or a few) nonconvex QP. For the last QP, we show that our specific problem can be solved efficiently, in spite of nonconvexity. In the benign case, we solve one equality constrained QP, and we prove that projected gradient descent solves it exponentially fast. In the bad case, we have to solve a few more inequality constrained QPs, but we prove that the time complexity is exponential only in the number of inequality constraints. Our experiments show that either benign case or bad case with very few inequality constraints occurs, implying that our algorithm is efficient in most cases.

Un algoritmo teorico per testare l'ottimalità locale ed estrarre le direzioni di discesa in punti non differenziabili di rischi empirici di reti ReLU a un hidden layer.

Propone un algoritmo per verificare se un dato punto è un punto stazionario generalizzato del secondo ordine.

Un algoritmo teorico, che coinvolge la risoluzione di programmi quadratici convessi e non convessi, per verificare l'ottimalità locale e sfuggire ai punti di sella quando si addestrano reti ReLU a due layer.

L'autore propone un metodo per controllare se un punto è un punto stazionario o no e poi classificare i punti stazionari come min locali o stazionari di secondo ordine


We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.   Inspired by the use of hard negatives in structured prediction, and ranking loss functions used in retrieval, we introduce a simple change to common loss functions used to learn multi-modal embeddings.   That, combined with fine-tuning and the use of augmented data, yields significant gains in retrieval performance.   We showcase our approach, dubbed VSE++, on the MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods.   On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval, and 11.3% in image retrieval (based on R@1).

Una nuova loss basata su negativi relativamente hard che raggiunge prestazioni allo stato dell'arte nel recupero delle image-caption.

Imparare l'embedding congiunto di frasi e immagini usando la triplet loss che viene applicata ai negativi più difficili invece di fare la media su tutte le triplet


We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimization problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising when compared to those trained using traditional backpropagation techniques, both in terms of training speed, as well as feature extraction and reconstruction performance.

Utilizziamo il principio di minimizzazione alternata per fornire una nuova tecnica efficace per addestrare i deep autoencoder.

Struttura di minimizzazione alternata per il training di reti autoencoder e encoder-decoder

Gli autori esplorano un approccio di ottimizzazione alternata per il training degli Auto Encoder, trattando ogni layer come un modello lineare generalizzato, e suggeriscono di usare la GD stocastica normalizzata come algoritmo di minimizzazione in ogni fase.


We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.

Transfer learning per stimare gli effetti causali usando le reti neurali.

Sviluppa algoritmi per stimare l'effetto di trattamento medio condizionato per dataset ausiliari in diversi ambienti, sia con che senza learner di base.

Gli autori propongono metodi per affrontare un nuovo task di transfer learning per stimare la funzione CATE, e li valutano utilizzando un setting sintetico e un dataset sperimentale del mondo reale.

Utilizzando la regressione tramite rete neurale e confrontando i framework di transfer learning si stima un effetto di trattamento medio condizionato sotto l'ipotesi di ignorabilità delle stringhe


Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.

Presentiamo LeMoNADe, un metodo di rilevamento di motif appresi end-to-end che opera direttamente sui video di imaging del calcio.

Questo articolo propone un modello in stile VAE per identificare i motif dai video di imaging del calcio, basandosi sulle variabili di Bernouli e usando il trucco del Gumbel-softmax per l'inferenza.


A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning. However, state-of-the-art imitation learning algorithms often assume the optimality of the given demonstration set.  In this paper, we address such optimal assumption by learning only from the most suitable demonstrations in a given set. Suitability of a demonstration is estimated by whether imitating it produce desirable outcomes for achieving the goals of the tasks. For more efficient demonstration suitability assessments, the learning agent should be capable of imitating a demonstration as quick as possible, which shares similar spirit with fast adaptation in the meta-learning regime. Our framework, thus built on top of Model-Agnostic Meta-Learning, evaluates how desirable the imitated outcomes are, after adaptation to each demonstration in the set. The resulting assessments hence enable us to select suitable demonstration subsets for acquiring better imitated skills. The videos related to our experiments are available at: https://sites.google.com/view/deepdj

Proponiamo una framework per imparare una buona policy attraverso l'imitation learning da un set di dimostrazioni rumorose attraverso il meta-training di un valutatore di idoneità della dimostrazione.

Arricchisce l'imitation learning con un algoritmo basato su MAML che determina automaticamente se le dimostrazioni fornite sono "adatte".

Un metodo per fare imitation learning da un insieme di dimostrazioni che include comportamenti inutili, che seleziona le dimostrazioni utili in base ai loro guadagni di performance forniti al momento del meta-training.


We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider the application of conditional and interventional sampling of face images with binary feature labels, such as mustache, young. We preserve the dependency structure between the labels with a given causal graph. We devise a two-stage procedure for learning a CiGM over the labels and the image. First we train a CiGM over the binary labels using a  Wasserstein GAN where the generator neural network is consistent with the causal graph between the labels. Later, we combine this with a conditional GAN to generate images conditioned on the binary labels. We propose two new conditional GAN architectures: CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained CiGM for the labels is then a CiGM over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.

Introduciamo modelli generativi causali impliciti, che possono campionare da distribuzioni condizionali e interventistiche e proponiamo anche due nuove GAN condizionali che usiamo per addestrarli.

Un metodo per combinare un grafo casuale, che descrive la struttura di dipendenza delle label, con due architetture GAN condizionali che generano immagini condizionando sulla label binaria

Gli autori affrontano la questione dell'apprendimento di un modello causale tra le variabili dell'immagine e l'immagine stessa dai dati osservazionali, quando è data una struttura causale tra le label dell'immagini.


Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. This property is useful to computationally-intensive neural network classifiers, as the cost of computing the partition function grows linearly with the number of classes and may become prohibitive. In particular, since neural language models may deal with up to millions of classes, their self-normalization properties received notable attention. Several  recent studies empirically found that language models, trained using Noise Contrastive Estimation (NCE), exhibit self-normalization, but could not explain why. In this study, we provide a theoretical justification to this property by viewing  NCE as a low-rank matrix approximation. Our empirical investigation compares NCE to the alternative explicit approach for self-normalizing language models. It also uncovers a surprising negative correlation between self-normalization and  perplexity, as well as some regularity in the observed errors that may potentially be used for improving self-normalization algorithms in the future.

Dimostriamo che NCE è auto-normalizzato e lo dimostriamo su dataset

Presenta una prova dell'auto normalizzazione di NCE come risultato dell'essere un'approssimazione della matrice di basso rango della matrice delle probabilità condizionali normalizzate.

Questo articolo considera il problema dei modelli auto-normalizzanti e spiega il meccanismo di auto-normalizzazione interpretando NCE in termini di fattorizzazione della matrice.


Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external knowledge. Our work addresses the question: can affect lexica improve the word representations learnt from a corpus? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotional orientation, into the training process of Word2Vec SkipGram, Word2Vec CBOW and GloVe methods using a joint learning approach. We use affect scores from Warriner's affect lexicon to regularize the vector representations learnt from an unlabelled corpus. Our proposed method outperforms previously proposed methods on standard tasks for word similarity detection, outlier detection and sentiment detection. We also demonstrate the usefulness of our approach for a new task related to the prediction of formality, frustration and politeness in corporate communication.

Arricchire gli embedding di parole con informazioni sugli affect migliora le loro prestazioni nei task di sentiment prediction.

Propone di utilizzare i lexica degli affect per migliorare gli embedding di parole per superare lo standard Word2vec e Glove.

Questo articolo propone di integrare le informazioni di una risorsa semantica che quantifica l'affect delle parole in un algoritmo di word embedding basato sul testo per rendere i modelli linguistici più riflessivi dei fenomeni semantici e pragmatici.

Questo articolo introduce delle modifiche alle funzioni di loss word2vec e GloVe per incorporare i lexica degli affect e facilitare l'apprendimento di embedding di parole sensibili agli affect.


Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks. Recently, in order to inductively learn representations for graph structures that is unobservable during training, a general framework with sampling and aggregating (GraphSAGE) was proposed by Hamilton and Ying and had been proved more efficient than transductive methods on fileds like transfer learning or evolving dataset. However, GraphSAGE is uncapable of selective neighbor sampling and lack of memory of known nodes that've been trained. To address these problems, we present an unsupervised method that samples neighborhood information attended by co-occurring structures and optimizes a trainable global bias as a representation expectation for each node in the given graph. Experiments show that our approach outperforms the state-of-the-art inductive and unsupervised methods for representation learning on graphs.

Per gli embedding non supervisionati e induttivi di reti, proponiamo un nuovo approccio per esplorare i vicini più rilevanti e preservare la conoscenza precedentemente appresa dei nodi utilizzando l'architettura di bi-attention e introducendo un bias globale, rispettivamente

Questo paper propone un'estensione di GraphSAGE utilizzando una matrice globale di embedding di bias nelle funzioni di aggregazione locale e un metodo per campionare i nodi interessanti.


Learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications. Linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors. We argue that the generalization performance of these methods is not due to the dimensionality constraint as commonly believed, but rather the small norm of embedding vectors. Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients. We performed extensive experiments to validate our analysis and showcased the importance of proper norm regularization in practice.

Noi sosteniamo che la generalizzazione dell'embedding dei grafi lineari non è dovuta al vincolo di dimensionalità ma piuttosto alla piccola norma dei vettori di embedding.

Gli autori mostrano che l'errore di generalizzazione dei metodi di embedding dei grafi lineari è limitato dalla norma dei vettori di embedding piuttosto che dai vincoli di dimensionalità

Gli autori propongono un limite teorico sulle prestazioni di generalizzazione dell'apprendimento di embedding di grafi e sostengono che la norma delle coordinate determina il successo della rappresentazione appresa.


Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. Code is immediately available.

Mescolate SGD e momentum (o fate qualcosa di simile con Adam) per ottenere un grande profitto.

L'articolo propone semplici modifiche a SGD e Adam, chiamate varianti QH, che possono recuperare il metodo originale e una serie di altri trucchi di ottimizzazione.

Una variante del momentum classico che prende una media ponderata del momentum e dell'aggiornamento del gradiente, e una valutazione delle sue relazioni con altri schemi di ottimizzazione basati sul momentum.


Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using the next state's value function. lambda-returns define the target of the RL agent as a weighted combination of rewards estimated by using multiple many-step look-aheads. Although mathematically tractable, the use of  exponentially decaying weighting of n-step returns based targets in lambda-returns is a rather ad-hoc design choice. Our major contribution  is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. In contrast to lambda-returns wherein the RL agent is restricted to use an exponentially decaying weighting scheme, CAR allows the agent to learn to decide how much it wants to weigh the n-step returns based targets. Our experiments, in addition to showing the efficacy of CAR, also empirically demonstrate that using sophisticated weighted mixtures of multi-step returns (like CAR and lambda-returns) considerably outperforms the use of n-step returns. We perform our experiments on the  Asynchronous Advantage Actor Critic (A3C) algorithm in the Atari 2600 domain.

Un nuovo modo di generalizzare i lambda-return permettendo all'agente RL di decidere quanto vuole pesare ciascuno degli n-step return.

Estende l'algoritmo A3C con lambda return, e propone un approccio per imparare i pesi dei return.

Gli autori presentano i return autodidattici confidence-based, un metodo di deep RL per regolare i pesi di un vettore di ammissibilità nella stima del valore TD(lambda)-like per favorire stime più stabili dello stato.


Current end-to-end deep learning driving models have two problems: (1) Poor  generalization ability of unobserved driving environment when diversity of train-  ing driving dataset is limited (2) Lack of accident explanation ability when driving  models don’t work as expected. To tackle these two problems, rooted on the be-  lieve that knowledge of associated easy task is benificial for addressing difficult  task, we proposed a new driving model which is composed of perception module  for see and think and driving module for behave, and trained it with multi-task  perception-related basic knowledge and driving knowledge stepwisely.  Specifi-  cally segmentation map and depth map (pixel level understanding of images) were  considered as what & where and how far knowledge for tackling easier driving-  related perception problems before generating final control commands for difficult  driving task. The results of experiments demonstrated the effectiveness of multi-  task perception knowledge for better generalization and accident explanation abil-  ity. With our method the average sucess rate of finishing most difficult navigation  tasks in untrained city of CoRL test surpassed current benchmark method for 15  percent in trained weather and 20 percent in untrained weathers.

Abbiamo proposto un nuovo modello di guida autonoma che è composto da modulo di percezione per vedere e pensare e un modulo di guida per comportarsi al fine di acquisire una migliore generalizzazione e capacità di spiegazione degli incidenti.

Presenta un'architettura di multitask learning per la stima della mappa di profondità, segmentazione e la predizione di guida utilizzando un modulo di percezione e un modulo di decisione di guida.

Un metodo per un'architettura end-to-end modificata che ha una migliore generalizzazione e capacità di spiegazione, è più robusto per un diverso setting di test e ha un output di decodifica che può aiutare nel debugging del modello.

Gli autori presentano una rete neurale convoluzionale multi-task per la guida end-to-end e forniscono valutazioni con il simulatore open source CARLA mostrando migliori prestazioni di generalizzazione in nuove condizioni di guida rispetto alle baseline


Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.

Un framework generico per scalare le tecniche esistenti di embedding di grafi a grandi grafi.

Questo articolo propone un framework di embedding multilivello da applicare sopra i metodi di embedding di rete esistenti al fine di scalare a reti su larga scala con maggiore velocità.

Gli autori propongono un framework a tre stadi per l'embedding di grafi su larga scala con una migliore qualità di embedding.


Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries. Learners such as One-Class Support Vector Machines (OCSVMs) have been successfully in anomaly detection, yet their performance may degrade significantly in the presence of sophisticated adversaries, who target the algorithm itself by compromising the integrity of the training data. With the rise in the use of machine learning in mission critical day-to-day activities where errors may have significant consequences, it is imperative that machine learning systems are made secure. To address this, we propose a defense mechanism that is based on a contraction of the data, and we test its effectiveness using OCSVMs. The proposed approach introduces a layer of uncertainty on top of the OCSVM learner, making it infeasible for the adversary to guess the specific configuration of the learner. We theoretically analyze the effects of adversarial perturbations on the separating margin of OCSVMs and provide empirical evidence on several benchmark datasets, which show that by carefully contracting the data in low dimensional spaces, we can successfully identify adversarial samples that would not have been identifiable in the original dimensional space. The numerical results show that the proposed method improves OCSVMs performance significantly (2-7%)

Un nuovo metodo per aumentare la resistenza degli OCSVM contro attacchi mirati e di integrità mediante trasformazioni selettive non lineari dei dati a dimensioni inferiori.

Gli autori propongono una difesa contro gli attacchi alla sicurezza dei rilevatori di anomalie basati su un SVM one-class

Questo articolo esplora come le proiezioni casuali possono essere utilizzate per rendere OCSVM robusta ai dati di allenamento con adversarial perturbation.


In this paper, we present a layer-wise learning of stochastic neural networks (SNNs) in an information-theoretic perspective. In each layer of an SNN, the compression and the relevance are defined to quantify the amount of information that the layer contains about the input space and the target space, respectively. We jointly optimize the compression and the relevance of all parameters in an SNN to better exploit the neural network's representation. Previously, the Information Bottleneck (IB) framework (\cite{Tishby99}) extracts relevant information for a target variable. Here, we propose Parametric Information Bottleneck (PIB) for a neural network by utilizing (only) its model parameters explicitly to approximate the compression and the relevance. We show that, as compared to the maximum likelihood estimate (MLE) principle, PIBs : (i) improve the generalization of neural networks in classification tasks, (ii) push the representation of neural networks closer to the optimal information-theoretical representation in a faster manner.

Apprendimento di una migliore rappresentazione delle reti neurali con il principio dell'Information Bottleneck

Propone un metodo di apprendimento basato sul framework dell'information bottleneck, dove gli hidden layer delle deep network comprimono l'input X mantenendo informazioni sufficienti per prevedere l'output Y.

Questo articolo presenta un nuovo modo di addestrare una rete neurale stocastica seguendo un framework di rilevanza/compressione delle informazioni simile all'Information Bottleneck.


The maximum mean discrepancy (MMD) between two probability measures P  and Q is a metric that is zero if and only if all moments of the two measures  are equal, making it an appealing statistic for two-sample tests. Given i.i.d. samples  from P and Q, Gretton et al. (2012) show that we can construct an unbiased  estimator for the square of the MMD between the two distributions. If P is a  distribution of interest and Q is the distribution implied by a generative neural  network with stochastic inputs, we can use this estimator to train our neural network.  However, in practice we do not always have i.i.d. samples from our target  of interest. Data sets often exhibit biases—for example, under-representation of  certain demographics—and if we ignore this fact our machine learning algorithms  will propagate these biases. Alternatively, it may be useful to assume our data has  been gathered via a biased sample selection mechanism in order to manipulate  properties of the estimating distribution Q.  In this paper, we construct an estimator for the MMD between P and Q when we  only have access to P via some biased sample selection mechanism, and suggest  methods for estimating this sample selection mechanism when it is not already  known. We show that this estimator can be used to train generative neural networks  on a biased data sample, to give a simulator that reverses the effect of that  bias.

Proponiamo uno stimatore per la massima discrepanza media, appropriato quando una distribuzione di destinazione è accessibile solo attraverso una procedura di sample selection biased, e mostriamo che può essere utilizzato in una rete generativa per correggere questo bias.

Propone uno stimatore importance-weighted della MMD per stimare la MMD tra distribuzioni basate su sample biased secondo uno schema noto o stimato.

Gli autori affrontano il problema del bias della selezione del sample nelle MMD-GAN e propongono una stima della MMD tra due distribuzioni utilizzando la discrepanza media massima pesata.

Questo articolo presenta una modifica dell'obiettivo utilizzato per addestrare le reti generative con un avversario MMD


We propose Bayesian Deep Q-Network  (BDQN), a  practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at the output layer of the network through a Bayesian Linear Regression (BLR) model, which can be trained with fast closed-form updates and its samples can be drawn efficiently through the Gaussian distribution. We apply our method to a wide range of Atari Arcade Learning Environments. Since BDQN carries out more efficient exploration, it is able to reach higher rewards substantially faster than a key baseline, DDQN.

Si usa la regressione bayesiana per stimare il posteriore sulle funzioni Q e si utilizza il sampling di Thompson come una strategia di esplorazione mirata con un efficiente trade-off tra exploration e exploitation

Gli autori propongono un nuovo algoritmo per l'esplorazione in Deep RL dove applicano la regressione lineare bayesiana con le feature dell'ultimo layer di una rete DQN per stimare la funzione Q per ogni azione.

Gli autori descrivono come usare le reti neurali bayesiane con il sampling di Thompson per un'esplorazione efficiente in q-learning e propongono un approccio che supera gli approcci di esplorazione epsilon-greedy.


In this work, we propose the polynomial convolutional neural network (PolyCNN), as a new design of a weight-learning efficient variant of the traditional CNN. The biggest advantage of the PolyCNN is that at each convolutional layer, only one convolutional filter is needed for learning the weights, which we call the seed filter, and all the other convolutional filters are the polynomial transformations of the seed filter, which is termed as an early fan-out. Alternatively, we can also perform late fan-out on the seed filter response to create the number of response maps needed to be input into the next layer. Both early and late fan-out allow the PolyCNN to learn only one convolutional filter at each layer, which can dramatically reduce the model complexity by saving 10x to 50x parameters during learning. While being efficient during both training and testing, the PolyCNN does not suffer performance due to the non-linear polynomial expansion which translates to richer representational power within the convolutional layers. By allowing direct control over model complexity, PolyCNN provides a flexible trade-off between performance and efficiency. We have verified the on-par performance between the proposed PolyCNN and the standard CNN on several visual datasets, such as MNIST, CIFAR-10, SVHN, and ImageNet.

PolyCNN ha solo bisogno di imparare un filtro convoluzionario seed ad ogni layer. Questa è una variante efficiente della CNN tradizionale, con prestazioni alla pari.

I tentativi di ridurre il numero di parametri del modello CNN utilizzando la trasformazione polinomiale dei filtri per creare blow-up delle risposte dei filtri.

Gli autori propongono un'architettura di weight sharing per ridurre il numero di parametri delle reti neurali convoluzionali con seed filter


Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for the two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.

In questo articolo, proponiamo KL-CPD, un nuovo framework di kernel learning per la CPD di serie temporali che ottimizza un limite inferiore del test power attraverso un modello generativo ausiliario come surrogato della distribuzione anomala.

Descrive un nuovo approccio per ottimizzare la scelta del kernel verso una maggiore test power e dimostra di offrire miglioramenti rispetto alle alternative.


Theories in cognitive psychology postulate that humans use similarity as a basis  for object categorization. However, work in image classification generally as-  sumes disjoint and equally dissimilar classes to achieve super-human levels of  performance on certain datasets. In our work, we adapt notions of similarity using  weak labels over multiple hierarchical levels to boost classification performance.  Instead of pitting clustering directly against classification, we use a warm-start  based evaluation to explicitly provide value to a clustering representation by its  ability to aid classification. We evaluate on CIFAR10 and a fine-grained classifi-  cation dataset to show improvements in performance with the procedural addition  of intermediate losses and weak labels based on multiple hierarchy levels. Further-  more, we show that pretraining AlexNet on hierarchical weak labels in conjunc-  tion with intermediate losses outperforms a classification baseline by over 17% on  a subset of Birdsnap dataset. Finally, we show improvement over AlexNet trained  using ImageNet pre-trained weights as initializations which further supports our   claim of the importance of similarity.

Cluster prima di classificare; usare weak label per migliorare la classificazione

Propone l'uso di una loss basata sul clustering a più livelli di una deepnet e l'uso della struttura gerarchica dello spazio delle label per allenare rappresentazioni migliori.

Questo articolo usa le informazioni gerarchiche sulle label per imporre ulteriori loss sulle rappresentazioni intermedie nel training delle reti neurali.


Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial or non-Markovian observations by using finite-length frame-history observations or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to a cumulative clipped advantage function and is robust to partially observed state. We demonstrate that on several partially observed reinforcement learning tasks, this new class of algorithms can substantially outperform strong baseline methods: on Pong with single-frame observations, and on the challenging Doom (ViZDoom) and Minecraft (Malmö) first-person navigation benchmarks.

Advantage-based regret minimization è un nuovo algoritmo di deep reinforcement learning che è particolarmente efficace su task parzialmente osservabili, come la navigazione in prima persona in Doom e Minecraft.

Questo articolo introduce i concetti di minimizzazione del counterfactual regret nel campo del deep RL e un algoritmo chiamato ARM che può trattare meglio l'osservabilità parziale.

L'articolo fornisce una variante ispirata alla teoria dei giochi dell'algoritmo policy-gradient basata sull'idea di minimizzazione del counterfactual regret e sostiene che l'approccio può trattare il dominio parziale osservabile meglio dei metodi standard.


Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks. Nonetheless, several major issues of deep MTL, including the effectiveness of sharing mechanisms, the efficiency of model complexity and the flexibility of network architectures, still remain largely unaddressed. To this end, we propose a novel generalized latent-subspace based knowledge sharing mechanism for linking task-specific models, namely tensor ring multi-task learning (TRMTL). TRMTL has a highly compact representation, and it is very effective in transferring task-invariant knowledge while being super flexible in learning task-specific features, successfully mitigating the dilemma of both negative-transfer in lower layers and under-transfer in higher layers. Under our TRMTL, it is feasible for each task to have heterogenous input data dimensionality or distinct feature sizes at different hidden layers. Experiments on a variety of datasets demonstrate our model is capable of significantly improving each single task’s performance, particularly favourable in scenarios where some of the tasks have insufficient data.

Un modello di deep multi-task learning che adatta la rappresentazione dell'anello dei tensori

Una variante della formulazione dell'anello dei tensori per multi-task learning condividendo alcuni dei nuclei TT per l'apprendimento del "task comune" mentre si imparano singoli nuclei TT per ogni task separato


Neural Processes (NPs) (Garnelo et al., 2018) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.

Un modello di regressione che impara le distribuzioni condizionali di un processo stocastico, incorporando l'attention nei processi neurali.

Propone di risolvere il problema dell'underfitting nel metodo del processo neurale aggiungendo un meccanismo di attention al percorso deterministico.

Un'estensione del framework dei Processi Neurali che aggiunge un meccanismo di condizionamento basato sull'attention, permettendo al modello di catturare meglio le dipendenze nel conditioning set.

Gli autori estendono i processi neurali incorporando la self-attention per arricchire le feature dei punti di contesto e la cross-attention per produrre una rappresentazione specifica della query. Risolvono il problema dell'underfitting delle NP e mostrano che le ANP convergono meglio e più velocemente delle NP.


Deconvolutional layers have been widely used in a variety of deep  models for up-sampling, including encoder-decoder networks for  semantic segmentation and deep generative models for unsupervised  learning. One of the key limitations of deconvolutional operations  is that they result in the so-called checkerboard problem. This is  caused by the fact that no direct relationship exists among adjacent  pixels on the output feature map. To address this problem, we  propose the pixel deconvolutional layer (PixelDCL) to establish  direct relationships among adjacent pixels on the up-sampled feature  map. Our method is based on a fresh interpretation of the regular  deconvolution operation. The resulting PixelDCL can be used to  replace any deconvolutional layer in a plug-and-play manner without  compromising the fully trainable capabilities of original models.  The proposed PixelDCL may result in slight decrease in efficiency,  but this can be overcome by an implementation trick. Experimental  results on semantic segmentation demonstrate that PixelDCL can  consider spatial features such as edges and shapes and yields more  accurate segmentation outputs than deconvolutional layers. When used  in image generation tasks, our PixelDCL can largely overcome the  checkerboard problem suffered by regular deconvolution operations.

Risolvere il problema della scacchiera nel layer di deconvoluzione costruendo dipendenze tra i pixel

Questo lavoro propone layer di deconvoluzione dei pixel per le reti neurali convoluzionali come un modo per alleviare l'effetto scacchiera.

Una nuova tecnica per generalizzare le operazioni di deconvoluzione utilizzate nelle architetture CNN standard, che propone di fare la predizione sequenziale delle feature dei pixel adiacenti, ottenendo output più spazialmente smooth per i layer di deconvoluzione.


In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).

Quantizziamo e facciamo pruning dei pesi delle reti neurali usando l'inferenza bayesiana variazionale con un prior multimodale che induce la sparsità.

Propone di utilizzare una miscela di spike continui come prior per una rete neurale bayesiana e dimostra le buone prestazioni con convnet relativamente sparsificate per minst e cifar-10.

Questo articolo presenta un approccio variazionale bayesiano per quantizzare i pesi delle reti neurali a valori ternari dopo il training.


Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approaches. This work proposes a progressive weight pruning approach based on ADMM (Alternating Direction Method of Multipliers), a powerful technique to deal with non-convex optimization problems with potentially combinatorial constraints. Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates. Therefore, it resolves the accuracy degradation and long convergence time problems when pursuing extremely high pruning ratios. It achieves up to 34× pruning rate for ImageNet dataset and 167× pruning rate for MNIST dataset, significantly higher than those reached by the literature work. Under the same number of epochs, the proposed method also achieves faster convergence and higher compression rates. The codes and pruned DNN models are released in the anonymous link bit.ly/2zxdlss.

Implementiamo un approccio di pruning dei pesi di DNN che raggiunge i più alti tassi di pruning.

Questo articolo si concentra sul pruning dei pesi per la compressione delle reti neurali, raggiungendo un tasso di compressione pari a 30 per AlexNet e VGG su ImageNet.

Una tecnica di pruning progressivo che impone un vincolo di sparsità strutturale sul parametro del peso e riscrive l'ottimizzazione come un framework ADMM, ottenendo una maggiore accuratezza rispetto alla projected gradient descent.


In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models.

Questo articolo presenta una nuova architettura di deep learning per affrontare il problema dell'apprendimento supervisionato con serie temporali multivariate sparse e campionate in modo irregolare.

Propone una framework per fare predizioni su dati di serie temporali sparsi e campionati in modo irregolare usando un modulo di interpolazione che modella i valori mancanti usando interpolazione smooth, interpolazione non smooth e intensità.

Risolve il problema dell'apprendimento supervisionato con serie temporali multivariate sparse e campionate in modo irregolare usando una rete di interpolazione semi-parametrica seguita da una rete di predizione.


We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.

Loss invariante alla permutazione per la predizione di set di punti.

Propone una nuova loss per la point registration (allineamento di due insiemi di punti) con una proprietà desiderabile di invarianza alla permutazione.

Questo articolo introduce una nuova funzione di distanza tra insiemi di punti, applica altre due distanze di permutazione in un task di object detection end-to-end, e mostra che in due dimensioni tutti i minimi locali della loss olografica sono minimi globali.

Propone funzioni di loss invarianti alla permutazione che dipendono dalla distanza degli insiemi.


We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph operations to groups and to allocate those groups to available devices. The grouping and device allocations are learned jointly. The proposed method is trained with policy gradient and requires no human intervention. Experiments with widely-used  computer vision and natural language models show that our algorithm can find optimized, non-trivial placements for TensorFlow computational graphs with over 80,000 operations. In addition, our approach outperforms placements by human  experts as well as a previous state-of-the-art placement method based on deep reinforcement learning. Our method achieves runtime reductions of up to 60.6% per training step when applied to models such as Neural Machine Translation.

Introduciamo un modello gerarchico per il posizionamento efficiente, end-to-end, di grafi computazionali su dispositivi hardware.

Propone di apprendere congiuntamente gruppi di operatori per collocolare e piazzare i gruppi appresi sui dispositivi per distribuire le operazioni per il deep learning tramite reinforcement learning.

Gli autori hanno lo scopo di presentare una rete fully connected per sostituire lo step di co-location in un metodo di auto-placement proposto per accelerare il tempo di esecuzione di un modello su TensorFlow.

Propone un algoritmo di device placement per collocare le operazioni di tensorflow sui dispositivi.


Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a model of motion based on elementary group properties of transformations and use it to train a representation of image motion. While most methods of estimating motion are based on pixel-level constraints, we use these group properties to constrain the abstract representation of motion itself. We demonstrate that a deep neural network trained using this method captures motion in both synthetic 2D sequences and real-world sequences of vehicle motion, without requiring any labels. Networks trained to respect these constraints implicitly identify the image characteristic of motion in different sequence types. In the context of vehicle motion, this method extracts information useful for localization, tracking, and odometry. Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.

Proponiamo un metodo per usare le proprietà del gruppo per imparare una rappresentazione del movimento senza label e dimostriamo l'uso di questo metodo per rappresentare il movimento 2D e 3D.

Propone di imparare il gruppo di movimento rigido da una rappresentazione latente di sequenze di immagini senza la necessità di label esplicite e dimostra sperimentalmente il metodo su sequenze di cifre MINST e il dataset KITTI.

Questo articolo propone un approccio per l'apprendimento delle feature del movimento video in modo unsupervised, utilizzando i vincoli per ottimizzare la rete neurale per produrre feature che possono essere utilizzate per fare regression per l'odometria.