Learning Multiple Quantiles With Neural Networks

Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Learning Multiple Timescales in Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are powerful architectures for sequence learning. Recent advances on the vanishing gradient problem have led to improved results and an increased research interest. Among recent proposals are architectural innovations that allow the emergence of multiple timescales during training. This paper explores a number of architectures for sequence generation and predict...

Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels

In this paper, we consider parameter recovery for non-overlapping convolutional neural networks (CNNs) with multiple kernels. We show that when the inputs follow Gaussian distribution and the sample size is sufficiently large, the squared loss of such CNNs is locally strongly convex in a basin of attraction near the global optima for most popular activation functions, like ReLU, Leaky ReLU, Squ...

Nonparametric Estimation of Conditional Quantiles Using Neural Networks