As an efficient recurrent neural network (RNN) model, reservoir computing (RC) models, such as Echo State Networks, have attracted widespread attention in the last decade. However, while they have had great success with time series data [1], [2], many time series have a multiscale structure, which a single-hidden-layer RC model may have difficulty capturing. In this paper, we propose a novel hi...

Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabu...

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to app...

This paper discusses the problem of tracking from a deep learning approach. This experiment takes cues from how the brain is modeled to create deep convolutional networks that mimic how the human brain tracks objects. By using optical flow and deep networks to implement a dual appearance and motion stream, our tracker outperforms current state of the art methods.

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 paper first gives an overview of deep learning and th...

lack of sewage collection systems, percolation of surface waters, and seepage of wells have raised the groundwater table in shiraz area in the south of iran. the growing population generates environmental pollution resulting in the degradation of the quality of surface and groundwaters used for agriculture. inorganic and organic pollutants have been traced in shiraz water resources. heavy metal...

It is shown that coherent spin motion of electron–hole pairs localized in band gap states of silicon can influence charge carrier recombination. Based on this effect, a readout concept for silicon based solid–state spin–quantum computers as proposed by Kane is suggested. The P quantum bit (qbit) is connected via hyperfine coupling to the spin of the localized donor electron. When a second local...

We propose a novel deep web crawling framework based on reinforcement learning. The crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and submits a selected action (query) to the environment according to Q-value. Based on the framework we develop an adaptive crawling method. Experimental results show that it outperforms the state of ...

Deep cognitive activation occurs when a thought is so accessible as to have measurable effects on behavior or judgement, but is yet not consciously reportable. This state of mind has unique properties mimicking some characteristics of the psychoanalytic unconscious, but following theoretically from a consideration of processes of cognitive activation. The sources and consequences of deep cognit...

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as the inherent noise associated with EEG data collection. Herein, we explore the capabilities of the recent deep neural architectures for modeling cognitive events from EEG data. In this paper, we present recent ach...