Dilated Deep Residual Network for Image Denoising

Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of noisy and clean images. Most existing CNN models for image denoising have many layers. In such cases, the models involve a large amount of parameters and are computationally expensive to train. In this paper, we develop a dilated residual CNN for Gaussian image denoising. Compared with the recently proposed residual denoiser, our method can achieve comparable performance with less computational cost. Specifically, we enlarge receptive field by adopting dilated convolution in residual network, and the dilation factor is set to a certain value. We utilize appropriate zero padding to make the dimension of the output the same as the input. It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem. Moreover, we present a formula to calculate receptive field size when dilated convolution is incorporated. Thus, the change of receptive field can be interpreted mathematically. To validate the efficacy of our approach, we conduct extensive experiments for both gray and color image denoising with specific or randomized noise levels. Both of the quantitative measurements and the visual results of denoising are promising comparing with state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201

Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM

Abstract: As stocks have been developing over decades, the trend and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid risks. In this research, we present a quantitative approach to predicting the trend of stocks in which a clustering model is employed to mine the stock trends patterns from historical stock price data. Stock series clustering is a special kind of time series clustering. We aim to find out the trend types, e.g. rising, falling and others, of a stock at time intervals, and then make use of the past trends to predict its future trend. The proposed prediction method is based on Graph Convolutional Neural Network for clustering and Long Short-Term Memory model for prediction. This method is suitable for the data clustering of unbalanced classes too. The experiments on real-world stock data demonstrate that our method can yield accurate forecasts. In the long run, the proposed method can be used to explore new possibilities in the research field of time series clustering, such as using other graph neural networks to predict stock trends.Comme les prix des actions évoluent au fil des décennies, la tendance et le prix d’une action sont souvent utilisés pour effectuer des prévisions en bourse. Bien anticiper la tendance future des actions peut non seulement aider les décideurs à mieux estimer les possibilités de profit, mais aussi les risques. Dans cette thèse, une approche quantitative est présentée pour prédire les fluctuations d’actions. L’approche se base sur une méthode de clustering pour identifier la tendance des actions à partir de leurs données historiques. C’est un type particulier de clustering appliqué sur des séries chronologiques. Il consiste à découvrir les tendances des actions sur des intervalles de temps, tel que des tendances haussières, des tendances baissières, et ensuite d’utiliser ces tendances pour prédire leurs états futurs. La méthode de prédiction proposée se base sur les réseaux de neurones convolutionnels graphiques et des réseaux récurrents mémoire pour la prédiction. Cette méthode fonctionne également sur des ensembles de données où la proportion des classes est déséquilibrée. Les données historiques des actions démontrent que la méthode proposée effectue des prévisions précises. La méthode proposée peut ouvrir une nouvelle perspective de recherche pour le clustering de séries chronologiques, notamment l’utilisation d‘autres réseaux de neurones graphiques pour prédire les tendances des actions

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor