In recent years, new neural network models with deep architectures started to get more attention in the field of machine learning. These models contain larger number of layers (therefore ”deep”) than conventional multi-layered perceptron, which usually uses only two or three functional layers of neurons. To overcome the difficulties of training such complex networks, new learning algorithms hav...

To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented under the particle filter framework. The incremental deep classification neural network was composed of stacked denoising autoencoder, incremental feature learning and support vector machine ...

introduction: manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. various data mining techniques exist for prediction of thermostable proteins. furthermore, ann methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate ...

Visual tracking in mobile robots have to track various target objects in fast processing, but existing state-ofthe-art methods only use specific image feature which only suitable for certain target objects. In this paper, we proposed new approach without depend on specific feature. By using deep learning, we can learn essential features of many of the objects and scenes found in the real world....

In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM...

the present study was conducted to investigate the effects of deep-stacking process on temperature and pathogenic bacterial survival of broiler litter (bl). broiler litter, deep-stacked to 3 depths (30, 60 or 120 cm) and at 3 moisture levels (15, 25 or 35%) was made use of in a split plot design, experiment last for 21 days. daily temperature of the deep-stacked bl at different depths and at di...