First Order Re urrent Neural Networks Learn To Predi t AMildly Context - Sensitive LanguageStephan

First Order Recurrent Neural Networks Learn To Predict A Mildly Context-Sensitive Language

Incremental training of first order recurrent neural networks to predict a context-sensitive language

In recent years it has been shown that first order recurrent neural networks trained by gradient-descent can learn not only regular but also simple context-free and context-sensitive languages. However, the success rate was generally low and severe instability issues were encountered. The present study examines the hypothesis that a combination of evolutionary hill climbing with incremental lea...

Utilizing Bias to Evolve Re urrent Neural Networks

Convolutional Gating Network for Object Tracking

Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem.  The paper presents a new model for combining convolutiona...

LSTM recurrent networks learn simple context-free and context-sensitive languages

Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). We demonstrate LSTMs superior performance on context-free language benchmarks for RNNs, and show that it works even better than previous hardwired or highly specialized architectures. To the best of our knowledge, LSTM ...