The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies.Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset.This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks.We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training.Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.

presentiamo i risultati allo stato dell'arte dell'uso delle reti neurali per diagnosticare le radiografie del torace


Semmelhack et al. (2014) have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM).Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but these powerful networks remain a black box.Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts.Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions.We find that our CNN makes predictions by analyzing the steadiness of the tail's trunk, which markedly differs from the manually extracted features used by Semmelhack et al. (2014).We further uncovered that the network paid attention to experimental artifacts.Removing these artifacts ensured the validity of predictions.After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%.Our work thus demonstrates the utility of AI explainability for CNNs.

Dimostriamo l'utilità di una recente tecnica di AI explainability visualizzando le feature apprese da una CNN addestrata sulla classificazione binaria dei movimenti di zebrafish.


When communicating, humans rely on internally-consistent language representations.That is, as speakers, we expect listeners to behave the same way we do when we listen.This work proposes several methods for encouraging such internal consistency in dialog agents in an emergent communication setting.We consider two hypotheses about the effect of internal-consistency constraints:1) that they improve agents’ ability to refer to unseen referents, and2) that they improve agents’ ability to generalize across communicative roles (e.g. performing as a speaker de- spite only being trained as a listener).While we do not find evidence in favor of the former, our results show significant support for the latter.

I vincoli di coerenza interna migliorano la capacità degli agenti di sviluppare protocolli emergenti che generalizzano attraverso i ruoli comunicativi.


Neural networks (NNs) are able to perform tasks that rely on compositional structure even though they lack obvious mechanisms for representing this structure.To analyze the internal representations that enable such success, we propose ROLE, a technique that detects whether these representations implicitly encode symbolic structure.ROLE learns to approximate the representations of a target encoder E by learning a symbolic constituent structure and an embedding of that structure into E’s representational vector space.The constituents of the approximating symbol structure are defined by structural positions — roles — that can be filled by symbols.We show that when E is constructed to explicitly embed a particular type of structure (e.g., string or tree), ROLE successfully extracts the ground-truth roles defining that structure.We then analyze a seq2seq network trained to perform a more complex compositional task (SCAN), where there is no ground truth role scheme available.For this model, ROLE successfully discovers an interpretable symbolic structure that the model implicitly uses to perform the SCAN task, providing a comprehensive account of the link between the representations and the behavior of a notoriously hard-to-interpret type of model.We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model’s output is also changed in the way predicted by our analysis.Finally, we use ROLE to explore whether popular sentence embedding models are capturing compositional structure and find evidence that they are not; we conclude by discussing how insights from ROLE can be used to impart new inductive biases that will improve the compositional abilities of such models.

Introduciamo una nuova tecnica di analisi che scopre la struttura compositiva ed interpretabile nelle reti neurali ricorrenti notoriamente difficili da interpretare.


The vertebrate visual system is hierarchically organized to process visual information in successive stages.Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation.There is currently no unified theory explaining these differences in representations across layers.Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing.The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck.Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks.This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli.These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system.

Abbiamo riprodotto le rappresentazioni neurali trovate nei sistemi visivi biologici simulando i loro vincoli di risorse neurali in un deep convolutional model.


While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian.We connect model generalization with the local property of a solution under the PAC-Bayes paradigm.In particular, we prove that model generalization ability is related to the Hessian, the higher-order "smoothness" terms characterized by the Lipschitz constant of the Hessian, and the scales of the parameters.Guided by the proof, we propose a metric to score the generalization capability of the model, as well as an algorithm that optimizes the perturbed model accordingly.

una teoria che collega l'Hessiano della soluzione e il potere di generalizzazione del modello


Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.Here we consider learning both an energy function and  an efficient approximate sampling mechanism for the corresponding distribution.Whereas the critic (or discriminator) in generative adversarial networks (GANs) learns to separate data and generator samples, introducing an entropy maximization regularizer on the generator can turn the interpretation of the critic into an energy function, which separates the training distribution from everything else, and thus can be used for tasks like anomaly or novelty detection. This paper is motivated by the older idea of sampling in latent space rather than data space because running a Monte-Carlo Markov Chain (MCMC) in latent space has been found to be easier and more efficient, and because a GAN-like generator can convert latent space samples to data space samples.For this purpose, we show how a Markov chain can be run in latent space whose samples can be mapped to data space, producing better samples.These samples are also used for the negative phase gradient required to estimate the log-likelihood gradient of the data space energy function.To maximize entropy at the output of the generator, we take advantage of recently introduced neural estimators of mutual information.We find that in addition to producing a useful scoring function for anomaly detection, the resulting approach produces sharp samples (like GANs) while covering the modes well, leading to high Inception and Fréchet scores.

Abbiamo introdotto la massimizzazione dell'entropia nelle GAN, portando a una reinterpretazione del critic come una funzione energetica.


Neural Style Transfer has become a popular technique forgenerating images of distinct artistic styles using convolutional neural networks.Thisrecent success in image style transfer has raised the question ofwhether similar methods can be leveraged to alter the “style” of musicalaudio.In this work, we attempt long time-scale high-quality audio transferand texture synthesis in the time-domain that captures harmonic,rhythmic, and timbral elements related to musical style, using examples thatmay have different lengths and musical keys.We demonstrate the abilityto use randomly initialized convolutional neural networks to transferthese aspects of musical style from one piece onto another using 3different representations of audio: the log-magnitude of the Short TimeFourier Transform (STFT), the Mel spectrogram, and the Constant-Q Transformspectrogram.We propose using these representations as a way ofgenerating and modifying perceptually significant characteristics ofmusical audio content.We demonstrate each representation'sshortcomings and advantages over others by carefully designingneural network structures that complement the nature of musical audio.Finally, we show that the mostcompelling “style” transfer examples make use of an ensemble of theserepresentations to help capture the varying desired characteristics ofaudio signals.

Presentiamo un algoritmo di transfer di stile audio musicale su lunga scala temporale che sintetizza l'audio nel dominio del tempo, ma utilizza rappresentazioni tempo-frequenza dell'audio.


To communicate with new partners in new contexts, humans rapidly form new linguistic conventions.Recent language models trained with deep neural networks are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do.We introduce a repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently understand their partner over time.We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.

Proponiamo un benchmark task di repeated reference e un approccio di continual learning regolarizzato per la comunicazione adattiva con gli esseri umani in domini non familiari


Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem.We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set.This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem.On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations.Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets.

Sorting nell'encoder e annullamento del sorting nel decoder per evitare il problema della responsabilità nei set auto-encoder


We present a method for policy learning to navigate indoor environments.We adopt a hierarchical policy approach, where two agents are trained to work in cohesion with one another to perform a complex navigation task.A Planner agent operates at a higher level and proposes sub-goals for an Executor agent.The Executor reports an embedding summary back to the Planner as additional side information at the end of its series of operations for the Planner's next sub-goal proposal.The end goal is generated by the environment and exposed to the Planner which then decides which set of sub-goals to propose to the Executor.We show that this Planner-Executor setup drastically increases the sample efficiency of our method over traditional single agent approaches, effectively mitigating the difficulty accompanying long series of actions with a sparse reward signal.On the challenging Habitat environment which requires navigating various realistic indoor environments, we demonstrate that our approach offers a significant improvement over prior work for navigation.

Presentiamo un framework di hierarchical learning per la navigazione in un ambiente di embodied learning


Saliency methods aim to explain the predictions of deep neural networks.These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction.We use a simple and common pre-processing step ---adding a mean shift to the input data--- to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute.We define input invariance as the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input.We show, through several examples, that saliency methods that do not satisfy a input invariance property are unreliable and can lead to misleading and inaccurate attribution.

L'attribuzione a volte può essere fuorviante


Large Transformer models routinely achieve state-of-the-art results ona number of tasks but training these models can be prohibitively costly,especially on long sequences.We introduce two techniques to improvethe efficiency of Transformers.For one, we replace dot-product attentionby one that uses locality-sensitive hashing, changing its complexityfrom O(L^2) to O(L), where L is the length of the sequence.Furthermore, we use reversible residual layers instead of the standardresiduals, which allows storing activations only once in the trainingprocess instead of N times, where N is the number of layers.The resulting model, the Reformer, performs on par with Transformer modelswhile being much more memory-efficient and much faster on long sequences.

Trasformer efficiente con locality-sensitive hashing e reversible layers


Obtaining policies that can generalise to new environments in reinforcement learning is challenging.In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations.We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information.In addition, we propose txt2π, a model that captures three-way interactions between the goal, document, and observations.On RTFM, txt2π generalises to new environments with dynamics not seen during training via reading.Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM.Through curriculum learning, txt2π produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.

Mostriamo che la comprensione del linguaggio attraverso la lettura è un modo promettente per imparare policy che generalizzano a nuovi ambienti.


An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data.We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar examples are similar and so the overall gradient is stronger in certain directions where these reinforce each other.Thus changes to the network parameters during training are biased towards those that (locally) simultaneously benefit many examples when such similarity exists.We support this hypothesis with heuristic arguments and perturbative experiments and outline how this can explain several common empirical observations about Deep Learning.Furthermore, our analysis is not just descriptive, but prescriptive.It suggests a natural modification to gradient descent that can greatly reduce overfitting.

Proponiamo un'ipotesi sul perché la gradient descent generalizza basandosi su come i gradienti per-sample interagiscono tra loro.


Recent advances in deep learning have shown promising results in many low-level vision tasks.However, solving the single-image-based view synthesis is still an open problem.In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery.We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with "t-shaped" adaptive kernels equipped with globally and locally adaptive dilations.Our proposed network architecture, the monster-net, is devised with a novel t-shaped adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given.Extensive experiments were performed on the KITTI, CityScapes and our VXXLXX_STEREO indoors dataset to prove the efficacy of our method.Our monster-net significantly outperforms the state-of-the-art method, SOTA, by a large margin in all metrics of RMSE, PSNR, and SSIM.Our proposed monster-net is capable of reconstructing more reliable image structures in synthesized images with coherent geometry.Moreover, the disparity information that can be extracted from the "t-shaped" kernel is much more reliable than that of the SOTA for the unsupervised monocular depth estimation task, confirming the effectiveness of our method.

Nuova architettura per la sintesi della vista stereoscopica a spostamenti arbitrari della telecamera utilizzando kernel adattivi t-shaped con dilatazioni adattive.


Deep Neutral Networks(DNNs) require huge GPU memory when training on modern image/video databases.Unfortunately, the GPU memory as a hardware resource is always finite, which limits the image resolution, batch size, and learning rate that could be used for better DNN performance.In this paper, we propose a novel training approach, called Re-forwarding, that substantially reduces memory usage in training.Our approach automatically finds a subset of vertices in a DNN computation graph, and stores tensors only at these vertices during the first forward.During backward, extra local forwards (called the Re-forwarding process) are conducted to compute the missing tensors between the subset of vertices.The total memory cost becomes the sum of (1) the memory cost at the subset of vertices and (2) the maximum memory cost among local re-forwards.Re-forwarding trades training time overheads for memory and does not compromise any performance in testing.We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary computation graphs.Experiments show that Re-forwarding cuts down up-to 80% of training memory on popular DNNs such as Alexnet, VGG, ResNet, Densenet and Inception net.

Questo articolo propone la teoria fondamentale e gli algoritmi ottimali per il training di DNN, che riducono fino all'80% la memoria di training per le DNN popolari.


Compression is a key step to deploy large neural networks on resource-constrained platforms.As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight.In this paper, we study the representation power of quantized neural networks.First, we prove the universal approximability of quantized ReLU networks on a wide class of functions.Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures.Our results reveal that, to attain an approximation error bound of $\epsilon$, the number of weights needed by a quantized network is no more than $\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized network.This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques.To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.

Questo articolo dimostra l'approssimabilità universale delle reti neurali quantizzate ReLU e presenta il bound di complessità dato un errore arbitrario.


Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such asgames and recommender systems (RSs).When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient. However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem.While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation.Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard.In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers.In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution.To speed up training of CAQL, we develop three techniques, namely(i) dynamic tolerance,(ii) dual filtering, and(iii) clustering.To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy.To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.

Un framework generale di reinforcement learning basato sul valore per il controllo continuo


Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions.However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem.We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator.Recognizing this, our contributions are twofold.First, we show that GAN training makes for a more interesting and realistic benchmark for continual learning methods evaluation than some of the more canonical datasets.Second, we propose leveraging continual learning techniques to augment the discriminator, preserving its ability to recognize previous generator samples.We show that the resulting methods add only a light amount of computation, involve minimal changes to the model, and result in better overall performance on the examined image and text generation tasks.

Il training generativo delle reti adversariali è un problema di continual learning.


Many problems with large-scale labeled training data have been impressively solved by deep learning.However, Unseen Class Categorization (UCC) with minimal information provided about target classes is the most commonly encountered setting in industry, which remains a challenging research problem in machine learning.Previous approaches to UCC either fail to generate a powerful discriminative feature extractor or fail to learn a flexible classifier that can be easily adapted to unseen classes.In this paper, we propose to address these issues through network reparameterization, \textit{i.e.}, reparametrizing the learnable weights of a network as a function of other variables, by which we decouple the feature extraction part and the classification part of a deep classification model to suit the special setting of UCC, securing both strong discriminability and excellent adaptability.Extensive experiments for UCC on several widely-used benchmark datasets in the settings of zero-shot and few-shot learning demonstrate that, our method with network reparameterization achieves state-of-the-art performance.

Un quadro unificato per l'apprendimento few shot e l'apprendimento zero-shot basato sulla riparametrizzazione della rete


Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure.Experimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency. In this work, we demonstrate significant improvements of the state-of-the-art for both hand-engineered and representation-learning approaches, as well as carefully evaluating the individual contributions of GraphQA.

GraphQA è un metodo basato sui grafi per la valutazione della qualità delle proteine che migliora lo stato dell'arte sia per gli approcci hand-engineered sia per quelli di representation-learning


We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles.We specifically consider the setting of batch active learning, in which multiple samples are selected as opposed to a single sample as in classical settings so as to reduce the training overhead.Our approach bridges between uniform randomness and score based importance sampling of clusters when selecting a batch of new samples.Experiments onbenchmark image classification datasets (MNIST, SVHN, and CIFAR10) shows improvement over existing active learning strategies.We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.

Affrontiamo active learning nel setting batch con oracoli rumorosi e usiamo l'incertezza del modello per codificare la qualità della decisione dell'algoritmo di active learning durante l'acquisizione.


Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't have control over the synthesized output.Moreover, the style transfer depends on the hyper-parameters of the model with varying ``optimum" for different input images.Therefore, if the stylized output is not appealing to the user, she/he has to try multiple models or retrain one with different hyper-parameters to get a favorite stylization.In this paper, we address these issues by proposing a novel method which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters.These parameters enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image.Our quantitative and qualitative experiments indicate how adjusting these parameters is comparable to retraining the model with different hyper-parameters.We also demonstrate how these parameters can be randomized to generate results which are diverse but still very similar in style and content.

Stochastic style transfer con feature regolabili.


Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales.To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples.  As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data.This framework effectively separates the representation of what instructions require from how they can be executed.In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements.We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.

Proponiamo AGILE, un framework per il training degli agenti per eseguire istruzioni a partire da esempi dei rispettivi goal-state.


We present Multitask Soft Option Learning (MSOL), a hierarchical multi-task framework based on Planning-as-Inference.MSOL extends the concept of Options, using separate variational posteriors for each task, regularized by a shared prior.The learned soft-options are temporally extended, allowing a higher-level master policy to train faster on new tasks by making decisions with lower frequency.Additionally, MSOL allows fine-tuning of soft-options for new tasks without unlearning previously useful behavior, and avoids problems with local minima in multitask training.We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines in challenging multi-task environments.

In Hierarchical RL, introduciamo la nozione di opzione 'soft', cioè adattabile, e mostriamo che questo aiuta l'apprendimento in ambienti multitask.


We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE.We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE.In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables.Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning.On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.

Apprendimento di un modello generativo controllabile mediante il disentanglement learning di latent representation.


Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences.Trained on huge corpus, NLMs are pushing the limit of modeling accuracy.Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR).By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate.However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance.This work proposes an approach to adapt a generative NLM to a discriminative one.Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences.It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text.Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.

Migliorare il language model per i task di apprendimento supervised


Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters.However, the variations in images pose a challenge to this fashion.In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.Since the filters are generated on-the-fly, the model becomes more flexible and can better fit the training data compared to traditional CNNs.In order to obtain sample-specific features, we extract the intermediate feature maps from an autoencoder.As filters are usually high dimensional, we propose to learn a set of coefficients instead of a set of filters.These coefficients are used to linearly combine the base filters from a filter repository to generate the final filters for a CNN.The proposed method is evaluated on MNIST, MTFL and CIFAR10 datasets.Experiment results demonstrate that the classification accuracy of the baseline model can be improved by using the proposed filter generation method.

generare dinamicamente filtri condizionati sull'immagine di ingresso per le CNN in ogni forward pass


We propose a new anytime neural network which allows partial evaluation by subnetworks with different widths as well as depths.Compared to conventional anytime networks only with the depth controllability, the increased architectural diversity leads to higher resource utilization and consequent performance improvement under various and dynamic resource budgets.We highlight architectural features to make our scheme feasible as well as efficient, and show its effectiveness in image classification tasks.

Proponiamo una nuova rete neurale anytime che permette una valutazione parziale tramite sottoreti con diverse larghezze e profondità.


We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.Given a target compound, the task is to predict the likely chemical reactants to produce the target.This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem.Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions.On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.

Proponiamo un nuovo modello per fare previsioni di reazioni retrosintetiche generalizzabili e diverse.


Unsupervised learning of disentangled representations is an open problem in machine learning.The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms.In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods.The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals.It also evaluates the encodings based on various disentanglement metrics.The library, so far, includes implementations of the following unsupervised algorithms VAE, Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE, as well as conditional approaches such as CVAE and IFCVAE.The library is compatible with the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd and was used to compete in the first and second stages of the challenge, where it was ranked among the best few participants.

Disentanglement-PyTorch è una libreria per il representation learning variazionale


Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL).While current trust region strategies are effective for continuous control, they typically require a large amount of on-policy interaction with the environment.To address this problem, we propose an off-policy trust region method, Trust-PCL, which exploits an observation that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path.The introduction of relative entropy regularization allows Trust-PCL to maintain optimization stability while exploiting off-policy data to improve sample efficiency.When evaluated on a number of continuous control tasks, Trust-PCL significantly improves the solution quality and sample efficiency of TRPO.

Estendiamo le recenti intuizioni relative alla softmax consistency per ottenere risultati allo stato dell'arte nel controllo continuo.


Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a family of combinatorial games, arising from work of Erdos, Selfridge, and Spencer, and we propose their use as environments for evaluating and comparing different approaches to reinforcement learning.These games have a number of appealing features: they are challenging for current learning approaches, but they form(i) a low-dimensional, simply parametrized environment where(ii) there is a linear closed form solution for optimal behavior from any state, and(iii) the difficulty of the game can be tuned by changing environment parameters in an interpretable way.We use these Erdos-Selfridge-Spencer games not only to compare different algorithms, but also to compare approaches based on supervised and reinforcement learning, to analyze the power of multi-agent approaches in improving performance, and to evaluate generalization to environments outside the training set.

Adattiamo una famiglia di giochi combinatori con difficoltà regolabile e una policy ottimale esprimibile come rete lineare, sviluppandola come un ricco ambiente per il reinforcement learning, mostrando contrasti nelle prestazioni con l'apprendimento supervised, e analizzando l'apprendimento e la generalizzazione multiagente.


Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand.Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed.We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring.By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training.We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.

Un metodo aggiuntivo per il deep learning per rilevare gli outlier durante la predizione


This work introduces a simple network for producing character aware word embeddings.Position agnostic and position aware character embeddings are combined to produce an embedding vector for each word.The learned word representations are shown to be very sparse and facilitate improved results on language modeling tasks, despite using markedly fewer parameters, and without the need to apply dropout.A final experiment suggests that weight sharing contributes to sparsity, increases performance, and prevents overfitting.

Un'architettura fully connected è usata per produrre word embedding da rappresentazioni di caratteri, supera gli embedding tradizionali e fornisce informazioni sulla sparsità e sul dropout.


Neural networks with low-precision weights and activations offer compellingefficiency advantages over their full-precision equivalents.The two mostfrequently discussed benefits of quantization are reduced memory consumption,and a faster forward pass when implemented with efficient bitwiseoperations.We propose a third benefit of very low-precision neural networks:improved robustness against some adversarial attacks, and in the worst case,performance that is on par with full-precision models.We focus on the verylow-precision case where weights and activations are both quantized to $\pm$1,and note that stochastically quantizing weights in just one layer can sharplyreduce the impact of iterative attacks.We observe that non-scaled binary neuralnetworks exhibit a similar effect to the original \emph{defensive distillation}procedure that led to \emph{gradient masking}, and a false notion of security.We address this by conducting both black-box and white-box experiments withbinary models that do not artificially mask gradients.

Conduciamo attacchi adversarial contro le reti neurali binarizzate e mostriamo che riduciamo l'impatto degli attacchi più forti, mantenendo una precisione comparabile in un setting black-box


Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages.One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs.For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces.We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization.We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability.Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.

Un'indagine empirica dell'allineamento basato su GAN di spazi vettoriali di parole, concentrandosi sui casi in cui le trasformazioni lineari esistono provatamente, ma il training è instabile.


Owing to the ubiquity of computer software, software vulnerability detection (SVD) has become an important problem in the software industry and in the field of computer security.One of the most crucial issues in SVD is coping with the scarcity of labeled vulnerabilities in projects that require the laborious manual labeling of code by software security experts.One possible way to address is to employ deep domain adaptation which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources.The general idea is to map both source and target data into a joint feature space and close the discrepancy gap of those data in this joint feature space.Generative adversarial network (GAN) is a technique that attempts to bridge the discrepancy gap and also emerges as a building block to develop deep domain adaptation approaches with state-of-the-art performance.However, deep domain adaptation approaches using the GAN principle to close the discrepancy gap are subject to the mode collapsing problem that negatively impacts the predictive performance.Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects in the context of SVD in order to resolve the mode collapsing problem faced in previous approaches.The experimental results on real-world software projects show that our proposed method outperforms state-of-the-art baselines by a wide margin.

Il nostro obiettivo in questo articolo è quello di proporre un nuovo approccio per affrontare il problema del transfer learning da progetti software etichettati a progetti software non etichettati nel contesto di SVD al fine di risolvere il problema del mode collapse affrontato negli approcci precedenti.


Representation learning is one of the foundations of Deep Learning and allowed important improvements on several Machine Learning tasks, such as Neural Machine Translation, Question Answering and Speech Recognition.Recent works have proposed new methods for learning representations for nodes and edges in graphs.Several of these methods are based on the SkipGram algorithm, and they usually process a large number of multi-hop neighbors in order to produce the context from which node representations are learned.In this paper, we propose an effective and also efficient method for generating node embeddings in graphs that employs a restricted number of permutations over the immediate neighborhood of a node as context to generate its representation, thus ego-centric representations.We present a thorough evaluation showing that our method outperforms state-of-the-art methods in six different datasets related to the problems of link prediction and node classification, being one to three orders of magnitude faster than baselines when generating node embeddings for very large graphs.

Un metodo più veloce per generare gli embedding dei nodi che impiega una serie di permutazioni sul neighborhood immediato di un nodo come contesto per generare la sua rappresentazione.


Orthogonal recurrent neural networks address the vanishing gradient problem by parameterizing the recurrent connections using an orthogonal matrix.This class of models is particularly effective to solve tasks that require the memorization of long sequences.We propose an alternative solution based on explicit memorization using linear autoencoders for sequences.We show how a recently proposed recurrent architecture, the Linear Memory Network, composed of a nonlinear feedforward layer and a separate linear recurrence, can be used to solve hard memorization tasks.We propose an initialization schema that sets the weights of a recurrent architecture to approximate a linear autoencoder of the input sequences, which can be found with a closed-form solution.The initialization schema can be easily adapted to any recurrent architecture.    We argue that this approach is superior to a random orthogonal initialization due to the autoencoder, which allows the memorization of long sequences even before training.The empirical analysis show that our approach achieves competitive results against alternative orthogonal models, and the LSTM, on sequential MNIST, permuted MNIST and TIMIT.

Mostriamo come inizializzare le architetture ricorrenti con la soluzione in forma chiusa di un autoencoder lineare per sequenze. Mostriamo i vantaggi di questo approccio rispetto alle RNN ortogonali.


This paper improves upon the line of research that formulates named entity recognition (NER) as a sequence-labeling problem.We use so-called black-box long short-term memory (LSTM) encoders to achieve state-of-the-art results while providing insightful understanding of what the auto-regressive model learns with a parallel self-attention mechanism.Specifically, we decouple the sequence-labeling problem of NER into entity chunking, e.g., Barack_B Obama_E was_O elected_O, and entity typing, e.g., Barack_PERSON Obama_PERSON was_NONE elected_NONE, and analyze how the model learns to, or has difficulties in, capturing text patterns for each of the subtasks.The insights we gain then lead us to explore a more sophisticated deep cross-Bi-LSTM encoder, which proves better at capturing global interactions given both empirical results and a theoretical justification.

Forniamo una comprensione approfondita della NER con etichettatura di sequenza e proponiamo di usare due tipi di strutture incrociate, entrambe le quali portano miglioramenti teorici ed empirici.


Knowledge Graph Embedding (KGE) is the task of jointly learning entity and relation embeddings for a given knowledge graph.Existing methods for learning KGEs can be seen as a two-stage process where(a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and(b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.Unfortunately, prior proposals for the scoring functions in the first step have been heuristically motivated, and it is unclear as to how the scoring functions in KGEs relate to the generation process of the underlying knowledge graph.To address this issue, we propose a generative account of the KGE learning task.Specifically, given a knowledge graph represented by a set of relational triples (h, R, t), where the semantic relation R holds between the two entities h (head) and t (tail), we extend the random walk model (Arora et al., 2016a) of word embeddings to KGE.We derive a theoretical relationship between the joint probability p(h, R, t) and the embeddings of h, R and t.Moreover, we show that marginal loss minimisation, a popular objective used by much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship.We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.The KGEs learnt by our proposed method obtain state-of-the-art performance on FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.

Presentiamo un modello generativo teoricamente solido di embedding dei knowledge graph.


Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.Unfortunately, neither of these techniques is applicable to the training of large neural networks due to their large computational and communication overheads.As a scalable technique for shared model governance, we propose splitting deep learning model between multiple parties.This paper empirically investigates the security guarantee of this technique, which is introduced as the problem of model completion:  Given the entire training data set or an environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model’s original performance?  We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind Lab.Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent’s trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location.  Our results suggest that model splitting might be a feasible technique for shared model governance in some settings where training is very expensive.

Studiamo empiricamente quanto sia difficile recuperare le parti mancanti dei modelli addestrati


This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference.Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages.Firstly, the  samples from the target are not required.Instead, the framework requires one known source as a prior $p(x)$ and binary discriminators, $p(\mathcal{D}_i|x)$, discriminating the target domain $\mathcal{D}_i$ from others.Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, $p(x|\mathcal{D}_i) \propto p(\mathcal{D}_i|x)p(x)$, as exhibited by a further proposed model of dual variational autoencoder (DualVAE).Secondly, the framework is scablable to large-scale domains.As well as VAE encodes a sample $x$ as a mode on a latent space: $\mu(x) \in \mathcal{Z}$, DualVAE encodes a domain $\mathcal{D}_i$ as a mode on the dual latent space $\mu^*(\mathcal{D}_i) \in \mathcal{Z}^*$, named domain embedding.It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE.Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.

Questo articolo propone l'adattamento variazionale del dominio, un framework univoco, scalabile e semplice per l'apprendimento di distribuzioni multiple attraverso l'inferenza variazionale


We propose a new method to train neural networks based on a novel combination of adversarial training and provable defenses.The key idea is to model training as a procedure which includes both, the verifier and the adversary.In every iteration, the verifier aims to certify the network using convex relaxation while the adversary tries to find inputs inside that convex relaxation which cause verification to fail.We experimentally show that this training method is promising and achieves the best of both worlds – it produces a model with state-of-the-art accuracy (74.8%) and certified robustness (55.9%) on the challenging CIFAR-10 dataset with a 2/255 L-infinity perturbation.This is a significant improvement over the currently known best results of 68.3% accuracy and 53.9% certified robustness, achieved using a 5 times larger network than our work.

Proponiamo una nuova combinazione di training adversarial e difese dimostrabili che produce un modello con accuratezza allo stato dell'arte e robustezza certificata su CIFAR-10.


Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered.We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs.We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location.Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases.Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.

I programmi hanno una struttura che può essere rappresentata come grafo, e le reti neurali a grafo possono imparare a trovare i bug su tali grafi


Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set.Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set.Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points.Otherwise the model overfits. We introduce two criteria for detecting overfitting and one to detect underfitting.We analyze early stopping, the regularization factor, and network depth.In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit.The methods of this paper allow characterizing and identifying such models.

Introduciamo e analizziamo diversi criteri per rilevare l'overfitting.


Partially observable Markov decision processes (POMDPs) are a widely-used framework to model decision-making with uncertainty about the environment and under stochastic outcome.In conventional POMDP models, the observations that the agent receives originate from fixed known distribution.However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive.Due to combinatorial nature of such selection process, it is computationally intractable to integrate the perception decision with the planning decision.To prevent such expansion of the action space, we propose a greedy strategy for observation selection that aims to minimize the uncertainty in state. We develop a novel point-based value iteration algorithm that incorporates the greedy strategy to achieve near-optimal uncertainty reduction for sampled belief points.This in turn enables the solver to efficiently approximate the reachable subspace of belief simplex by essentially separating computations related to perception from planning.Lastly, we implement the proposed solver and demonstrate its performance and computational advantage in a range of robotic scenarios where the robot simultaneously performs active perception and planning.

Sviluppiamo un solutore di iterazione del valore point-based per POMDP con task di percezione e pianificazione attiva.


Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements.This success can be attributed in part to their ability to represent and generate natural images well.Contrary to classical tools such as wavelets, image-generating deep neural networks have a large number of parameters---typically a multiple of their output dimension---and need to be trained on large datasets. In this paper, we propose an untrained simple image model, called the deep decoder, which is a deep neural network that can generate natural images from very few weight parameters.The deep decoder has a simple architecture with no convolutions and fewer weight parameters than the output dimensionality.This underparameterization enables the deep decoder to compress images into a concise set of network weights, which we show is on par with wavelet-based thresholding.Further, underparameterization provides a barrier to overfitting, allowing the deep decoder to have state-of-the-art performance for denoising.The deep decoder is simple in the sense that each layer has an identical structure that consists of only one upsampling unit, pixel-wise linear combination of channels, ReLU activation, and channelwise normalization.This simplicity makes the network amenable to theoretical analysis, and it sheds light on the aspects of neural networks that enable them to form effective signal representations.

Introduciamo una deep neural network semplice, non rivoluzionaria e sottoparametrizzata che può, senza training, rappresentare efficacemente le immagini naturali e risolvere task di elaborazione delle immagini come la compressione e il denoising in modo competitivo.


In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU).We give an algorithm to train a ReLU DNN with one hidden layer to {\em global optimality} with runtime polynomial in the data size albeit exponential in the input dimension.Further, we improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net.Our gap theorems hold for smoothly parametrized families of ``hard'' functions, contrary to countable, discrete families known in the literature.  An example consequence of our gap theorems is the following: for every natural number $k$ there exists a function representable by a ReLU DNN with $k^2$ hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$ hidden layers will require at least $\frac12k^{k+1}-1$ total nodes.Finally, for the family of $\R^n\to \R$ DNNs with ReLU activations, we show a new lowerbound on the number of affine pieces, which is larger than previous constructions in certain regimes of the network architecture and most distinctively our lowerbound is demonstrated by an explicit construction of a \emph{smoothly parameterized} family of functions attaining this scaling.Our construction utilizes the theory of zonotopes from polyhedral theory.

Questo articolo 1) caratterizza le funzioni rappresentabili da DNN con ReLU, 2) studia formalmente il beneficio della profondità in tali architetture, 3) fornisce un algoritmo per implementare ERM per l'ottimalità globale per reti ReLU a due strati.


The backpropagation of error algorithm (BP) is often said to be impossible to implement in a real brain.The recent success of deep networks in machine learning and AI, however, has inspired a number of proposals for understanding how the brain might learn across multiple layers, and hence how it might implement or approximate BP.As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks.Here we present the first results on scaling up a biologically motivated model of deep learning to datasets which need deep networks with  appropriate architectures to achieve good performance.We present results on CIFAR-10 and ImageNet.  For CIFAR-10 we show that our algorithm, a straightforward, weight-transport-free variant of difference target-propagation (DTP) modified to remove backpropagation from the penultimate layer, is competitive with BP in training deep networks with locally defined receptive fields that have untied weights.  For ImageNet we find that both DTP and our algorithm perform significantly worse than BP, opening questions about whether different architectures or algorithms are required to scale these approaches.Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.

Benchmark per algoritmi di apprendimento biologicamente plausibili su dataset e architetture complesse


Deep neural networks (DNNs) usually contain millions, maybe billions, of parameters/weights, making both storage and computation very expensive.This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers.  Another well-known approach for controlling the complexity of DNNs is parameter sharing/tying, where certain sets of weights are forced to share a common value.Some forms of weight sharing are hard-wired to express certain in- variances, with a notable example being the shift-invariance of convolutional layers.However, there may be other groups of weights that may be tied together during the learning process, thus further re- ducing the complexity of the network.In this paper, we adopt a recently proposed sparsity-inducing regularizer, named GrOWL (group ordered weighted l1), which encourages sparsity and, simulta- neously, learns which groups of parameters should share a common value.GrOWL has been proven effective in linear regression, being able to identify and cope with strongly correlated covariates.Unlike standard sparsity-inducing regularizers (e.g., l1 a.k.a.Lasso), GrOWL not only eliminates unimportant neurons by setting all the corresponding weights to zero, but also explicitly identifies strongly correlated neurons by tying the corresponding weights to a common value.This ability of GrOWL motivates the following two-stage procedure: (i) use GrOWL regularization in the training process to simultaneously identify significant neurons and groups of parameter that should be tied together; (ii) retrain the network, enforcing the structure that was unveiled in the previous phase, i.e., keeping only the significant neurons and enforcing the learned tying structure.We evaluate the proposed approach on several benchmark datasets, showing that it can dramatically compress the network with slight or even no loss on generalization performance.

Abbiamo proposto di utilizzare il recente regolatore GrOWL per la simultanea sparsità dei parametri e il tying nell'apprendimento DNN.


Weight-sharing—the simultaneous optimization of multiple neural networks using the same parameters—has emerged as a key component of state-of-the-art neural architecture search.However, its success is poorly understood and often found to be surprising.We argue that, rather than just being an optimization trick, the weight-sharing approach is induced by the relaxation of a structured hypothesis space, and introduces new algorithmic and theoretical challenges as well as applications beyond neural architecture search.Algorithmically, we show how the geometry of ERM for weight-sharing requires greater care when designing gradient- based minimization methods and apply tools from non-convex non-Euclidean optimization to give general-purpose algorithms that adapt to the underlying structure.We further analyze the learning-theoretic behavior of the bilevel optimization solved by practical weight-sharing methods.Next, using kernel configuration and NLP feature selection as case studies, we demonstrate how weight-sharing applies to the architecture search generalization of NAS and effectively optimizes the resulting bilevel objective.Finally, we use our optimization analysis to develop a simple exponentiated gradient method for NAS that aligns with the underlying optimization geometry and matches state-of-the-art approaches on CIFAR-10.

Un'analisi delle strutture di apprendimento e di ottimizzazione della ricerca dell'architettura nelle reti neurali e oltre.


Deep latent variable models have seen recent success in many data domains.Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner.We present '`Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate.We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE.Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time.We make our implementation available open source at https://github.com/bits-back/bits-back .

Facciamo la compressione senza perdite di grandi dataset di immagini usando un VAE, battendo gli algoritmi di compressione esistenti.


Hyperparameter tuning is arguably the most important ingredient for obtaining state of art performance in deep networks.  We focus on hyperparameters  that are related to the optimization algorithm, e.g.  learning rates, which have a large impact on the training speed and the resulting accuracy.Typically, fixed learning rate schedules are employed during training.We propose Hyperdyn a dynamic hyperparameter optimization method that selects new learning rates on the fly at the end of each epoch.Our explore-exploit   framework  combines Bayesian optimization (BO) with a rejection strategy, based on a simple probabilistic wait and watch test.   We obtain state of  art accuracy results on CIFAR and Imagenet datasets, but with significantly faster training, when compared with the best manually tuned networks.

Ottimizzazione bayesiana basata sull'ottimizzazione online degli iperparametri.


Multi-hop text-based question-answering is a current challenge in machine comprehension. This task requires to sequentially integrate facts from multiple passages to answer complex natural language questions.In this paper, we propose a novel architecture, called the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.LQR-net is composed of an association of \textbf{reading modules} and \textbf{reformulation modules}.The purpose of the reading module is to produce a question-aware representation of the document.From this document representation, the reformulation module extracts essential elements to calculate an updated representation of the question.This updated question is then passed to the following hop.We evaluate our architecture on the \hotpotqa question-answering dataset designed to assess multi-hop reasoning capabilities.Our model achieves competitive results on the public leaderboard and outperforms the best current \textit{published} models in terms of Exact Match (EM) and $F_1$ score.Finally, we show that an analysis of the sequential reformulations can provide interpretable reasoning paths.

In questo articolo, proponiamo la Latent Question Reformulation Network (LQR-net), una rete attenta, multi-hop e parallela, progettata per task di question answering che richiedono capacità di ragionamento.


We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations.We define the importance of each observation as the change in the model output caused by replacing the observation with a generated one.Our method can be applied to arbitrarily complex time series models.We compare the generated feature importance to existing methods like sensitivity analyses, feature occlusion, and other explanation baselines to show that our approach generates more precise explanations and is less sensitive to noise in the input signals.

Spiegare i modelli multivariati di serie temporali trovando osservazioni importanti nel tempo usando i controfattuali


This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability.The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously.  Each self-supervised task brings the two domains closer together along the direction relevant to that task.Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain.  The presented objective is straightforward to implement and easy to optimize.We achieve state-of-the-art results on four out of seven standard benchmarks, and competitive results on segmentation adaptation.We also demonstrate that our method composes well with another popular pixel-level adaptation method.

Usiamo la self-supervision su entrambi i domini per allinearli per un domain adaptation unsupervised.


We introduce simple, efficient algorithms for computing a MinHash of a probability distribution, suitable for both sparse and dense data, with equivalent running times to the state of the art for both cases.The collision probability of these algorithms is a new measure of the similarity of positive vectors which we investigate in detail.We describe the sense in which this collision probability is optimal for any Locality Sensitive Hash based on sampling.We argue that this similarity measure is more useful for probability distributions than the similarity pursued by other algorithms for weighted MinHash, and is the natural generalization of the Jaccard index.

Il minimo di un insieme di hash distribuiti esponenzialmente ha una probabilità di collisione molto utile che generalizza l'indice Jaccard alle distribuzioni di probabilità.


Recently, progress has been made towards improving relational reasoning in machine learning field.Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning.In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction.In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs.We verify GP-GNNs in relation extraction from text.Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to the baselines.We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.

Una graph neural network con parametri generati da linguaggi naturali, che può eseguire ragionamenti a più vie.


Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks.Normally, the critic’s action-value function is updated using temporal-difference, and the critic in turn provides a loss for the actor that trains it to take actions with higher expected return.In this paper, we introduce a novel and flexible meta-critic that observes the learning process and meta-learns an additional loss for the actor that accelerates and improves actor-critic learning.Compared to the vanilla critic, the meta-critic network is explicitly trained to accelerate the learning process; and compared to existing meta-learning algorithms, meta-critic is rapidly learned online for a single task, rather than slowly over a family of tasks.Crucially, our meta-critic framework is designed for off-policy based learners, which currently provide state-of-the-art reinforcement learning sample efficiency.We demonstrate that online meta-critic learning leads to improvements in a variety of continuous control environments when combined with contemporary Off-PAC methods DDPG, TD3 and the state-of-the-art SAC.

Presentiamo Meta-Critic, un modulo critic ausiliario per i metodi off-policy actor-critic che può essere appreso tramite meta-learning online durante l'apprendimento di un singolo task.


Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data.Nevertheless, these networks often generalize well in practice.It has also been observed that trained networks can often be ``compressed to much smaller representations.The purpose of this paper is to connect these two empirical observations.Our main technical result is a generalization bound for compressed networks based on the compressed size that, combined with off-the-shelf compression algorithms, leads to state-of-the-art generalization guarantees.In particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem.Additionally, we show that compressibility of models that tend to overfit is limited.Empirical results show that an increase in overfitting increases the number of bits required to describe a trained network.

Otteniamo limiti di generalizzazione non banali sulle deep neural network su scala ImageNet combinando un limite PAC-Bayes originale e un metodo di compressione delle reti neurali disponibili.


Adversarial examples can be defined as inputs to a model which induce a mistake -- where the model output is different than that of an oracle, perhaps in surprising or malicious ways.Original models of adversarial attacks are primarily studied in the context of classification and computer vision tasks.While several attacks have been proposed in natural language processing (NLP) settings, they often vary in defining the parameters of an attack and what a successful attack would look like.The goal of this work is to propose a unifying model of adversarial examples suitable for NLP tasks in both generative and classification settings.We define the notion of adversarial gain: based in control theory, it is a measure of the change in the output of a system relative to the perturbation of the input (caused by the so-called adversary) presented to the learner.This definition, as we show, can be used under different feature spaces and distance conditions to determine attack or defense effectiveness across different intuitive manifolds.This notion of adversarial gain not only provides a useful way for evaluating adversaries and defenses, but can act as a building block for future work in robustness under adversaries due to its rooted nature in stability and manifold theory.

Proponiamo una misura alternativa per determinare l'efficacia degli attacchi adversarial nei modelli NLP secondo un metodo basato su una misura di distanza come L2-gain incrementale nella teoria del controllo.


We propose a Warped Residual Network (WarpNet) using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network.We apply a perturbation theory on residual networks and decouple the interactions between residual units.The resulting warp operator is a first order approximation of the output over multiple layers.The first order perturbation theory exhibits properties such as binomial path lengths and exponential gradient scaling found experimentally by Veit et al (2016). We demonstrate through an extensive performance study that the proposed network achieves comparable predictive performance to the original residual network with the same number of parameters, while achieving a significant speed-up on the total training time.As WarpNet performs model parallelism in residual network training in which weights are distributed over different GPUs, it offers speed-up and capability to train larger networks compared to original residual networks.

Proponiamo la Warped Residual Network utilizzando un operatore di curvatura parallelizzabile per forwoard e backward pass verso layer lontani che si allena più velocemente della rete neurale con residual connection originale.


A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community.Yet, measuring the quality of the generated explanations is largely unexplored, making quantitative comparisons non-trivial.In this work, we propose a suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations.These metrics are computationally inexpensive, do not require model-retraining and can be used across different data modalities.We evaluate them on common explainers such as Grad-CAM, SmoothGrad, LIME and Integrated Gradients.Our experiments show that the proposed metrics reflect qualitative observations reported in earlier works.

Proponiamo una suite di metriche che catturano le proprietà desiderate degli algoritmi di explainability e la utilizziamo per confrontare e valutare oggettivamente tali metodi


Neural networks are known to produce unexpected results on inputs that are far from the training distribution.One approach to tackle this problem is to detect the samples on which the trained network can not answer reliably.ODIN is a recently proposed method for out-of-distribution detection that does not modify the trained network and achieves good performance for various image classification tasks.In this paper we adapt ODIN for sentence classification and word tagging tasks.We show that the scores produced by ODIN can be used as a confidence measure for the predictions on both in-distribution and out-of-distribution datasets.

Un recente metodo di rilevamento di out-of-distribution aiuta a misurare la fiducia delle previsioni RNN per alcuni task NLP


Some recent work has shown separation between the expressive power of depth-2 and depth-3 neural networks.These separation results are shown by constructing functions and input distributions, so that the function is well-approximable by a depth-3 neural network of polynomial size but it cannot be well-approximated under the chosen input distribution by any depth-2 neural network of polynomial size.These results are not robust and require carefully chosen functions as well as input distributions.We show a similar separation between the expressive power of depth-2 and depth-3 sigmoidal neural networks over a large class of input distributions, as long as the weights are polynomially bounded.While doing so, we also show that depth-2 sigmoidal neural networks with small width and small weights can be well-approximated by low-degree multivariate polynomials.

depth-2-vs-3 separation per reti neurali sigmoidali su distribuzioni generali


The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph.In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning.However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons:  First, they are computationally expensive, often requiring operations on large matrices.Second, these methods lack adequate justification beyond simple, tabular, finite-state settings.In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context.We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting.Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals.Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.

Proponiamo un metodo scalabile per approssimare gli autovettori del Laplaciano nel contesto del reinforcement learning e mostriamo che le rappresentazioni apprese possono migliorare le prestazioni di un agente RL.


Our work offers a new method for domain translation from semantic label mapsand Computer Graphic (CG) simulation edge map images to photo-realistic im-ages.We train a Generative Adversarial Network (GAN) in a conditional way togenerate a photo-realistic version of a given CG scene.Existing architectures ofGANs still lack the photo-realism capabilities needed to train DNNs for computervision tasks, we address this issue by embedding edge maps, and training it in anadversarial mode.We also offer an extension to our model that uses our GANarchitecture to create visually appealing and temporally coherent videos.

Traduzione da simulazione a immagini reali e generazione di video


Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.Numerous model compression algorithms have been proposed, however, it is often difficult and time-consuming to choose proper hyper-parameters to obtain an efficient compressed model.In this paper, we propose an automated framework for model compression and acceleration, namely PocketFlow.This is an easy-to-use toolkit that integrates a series of model compression algorithms and embeds a hyper-parameter optimization module to automatically search for the optimal combination of hyper-parameters.Furthermore, the compressed model can be converted into the TensorFlow Lite format and easily deployed on mobile devices to speed-up the inference.PocketFlow is now open-source and publicly available at https://github.com/Tencent/PocketFlow.

Proponiamo PocketFlow, un framework automatizzato per la compressione e l'accelerazione dei modelli, per facilitare l'implementazione dei modelli di deep learning sui dispositivi mobili.


Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest.However, current techniques for training generative models require access to fully-observed samples.In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy observations.We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest.We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models.Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN.On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements.Generative models trained with our method can obtain $2$-$4$x higher inception scores than the baselines.

Come addestrare le GAN da osservazioni rumorose, distorte e parziali


Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet.  Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints.  Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization.  For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a "size scale" separating signal from noise.  For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems.  This implicit Self-Regularization can depend strongly on the many knobs of the training process.  By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.

Vedere l'abstract.  (Per la revisione, il paper è identico, tranne che per un materiale supplementare di 59 pagine, che può servire come un technical report stand-along del paper).


We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs.We apply the proposed explanation-based attention to MNIST and SVHN classification.The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.

Introduciamo un meccanismo di attention per migliorare l'estrazione delle feature per deep active learning nel setting semi-supervised.


We apply canonical forms of gradient complexes (barcodes) to explore neural networks loss surfaces.We present an algorithm for calculations of the objective function's barcodes of minima.  Our experiments confirm two principal observations: (1) the barcodes of minima are located in a small lower part of the range of values of objective function and (2) increase of the neural network's depth brings down the minima's barcodes.This has natural implications for the neural network learning and the ability to generalize.

Applichiamo forme canoniche di complessi di gradiente (barcode) per esplorare le superfici delle loss delle reti neurali.


New types of compute hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs.However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon.In particular, models that exploit structured input via complex and instance-dependent control flow are difficult to accelerate using existing algorithms and hardware that typically rely on minibatching.We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices.Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently, even for small minibatch sizes, resulting in shorter overall training times.Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.

Utilizzo di aggiornamenti asincroni del gradiente per accelerare il training dinamico delle reti neurali


In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem.The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior.Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.In this paper, we study reward design problem in cooperative MARL based on packet routing environments.Firstly, we show that the above two reward signals are prone to produce suboptimal policies.Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies.Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments.Other reward signals are also discussed in this paper.As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.

Studiamo il problema della progettazione delle reward nelle MARL cooperative basate su ambienti di routing dei pacchetti. I risultati sperimentali ci ricordano di essere attenti a progettare le reward, poiché sono davvero importanti per guidare il comportamento dell'agente.


Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem.Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem.We also investigate an extension of the Neumann network that incorporates a more sample efficient patch-based regularization approach.

Le reti di Neumann sono un approccio di apprendimento end-to-end, efficiente in termini di sample, per risolvere problemi inversi lineari nell'imaging che sono compatibili con l'approccio ottimale MSE e ammettono un'estensione all'apprendimento basato su patch.


End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework.We propose the global-to-local memory pointer (GLMP) networks to address this issue.In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge.The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer.The decoder first generates a sketch response with unfilled slots.Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers.We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem.As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.

GLMP: un encoder di memoria globale (context RNN, puntatore globale) e un decoder di memoria locale (sketch RNN, puntatore locale) che condividono la conoscenza esterna (MemNN) sono proposti per rafforzare la generazione di risposte nel task-oriented dialogue.


The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.The origins and solutions of checkerboard artifacts in the pixel space have been studied for a long time, but their effects on the gradient space have rarely been investigated.In this paper, we revisit the checkerboard artifacts in the gradient space which turn out to be the weak point of a network architecture.We explore image-agnostic property of gradient checkerboard artifacts and propose a simple yet effective defense method by utilizing the artifacts.We introduce our defense module, dubbed Artificial Checkerboard Enhancer (ACE), which induces adversarial attacks on designated pixels.This enables the model to deflect attacks by shifting only a single pixel in the image with a remarkable defense rate.We provide extensive experiments to support the effectiveness of our work for various attack scenarios using state-of-the-art attack methods.Furthermore, we show that ACE is even applicable to large-scale datasets including ImageNet dataset and can be easily transferred to various pretrained networks.

Proponiamo un nuovo modulo aritificial checkerboard enhancer (ACE) che guida gli attacchi in uno spazio di pixel prestabilito e lo difende con successo con una semplice operazione di padding.


Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, and understanding the dynamics of (stochastic) gradient algorithms for solving such formulations has been a grand challenge.As a first step, we restrict to bilinear zero-sum games and give a systematic analysis of popular gradient updates, for both simultaneous and alternating versions.We provide exact conditions for their convergence and find the optimal parameter setup and convergence rates.In particular, our results offer formal evidence that alternating updates converge "better" than simultaneous ones.

Analizziamo sistematicamente il comportamento di convergenza di popolari algoritmi basati sul gradiente per la risoluzione di giochi bilineari, con aggiornamenti sia simultanei che alternati.