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Chia-Wen Lin, Wangmeng Zuo, Yong Xu, Wenxian Zheng, Lunke Fei, Chunwei Tian - 2019
Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimatin...
Jugal Kalita, Marc Moreno Lopez - 2017
Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will ...
George D Magoulas, Alan Mosca - 2017
This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We ...
David Silver, Arthur Guez, Hado van Hasselt - 2015
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show th...
Daan Wierstra, David Silver, Yuval Tassa, Tom Erez, Nicolas Heess, Alexander Pritzel, Jonathan J. Hunt, Timothy P. Lillicrap - 2015
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more t...
Naiomi Soto, Patricio Loncomilla, Javier Ruiz-del-Solar - 2018
Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer visio...
Martin Riedmiller, Daan Wierstra, Ioannis Antonoglou, Alex Graves, David Silver, Koray Kavukcuoglu, Volodymyr Mnih - 2013
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method ...
Marco A. Wiering, Pierre Geurts, Gilles Louppe, Matthia Sabatelli - 2018
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and uses this network for training a second Quality-value network that learns to estimate state-action values. We first test DQV's update rules with Multilayer Perceptrons...
Ling Zhou, Jiasong Wu, Xiao Dong - 2017
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this ...
Bing Liu, Shuai Wang, Lei Zhang - 2018
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This pape...
