Nonlinear Spatio-temporal Learning of Visual Patterns with Deep Convolutional Neural Networks – Many existing deep learning models have been trained by adversarial training alone. For instance, the recently proposed Convolutional Neural Network (CNN) has been trained by the same network with a different architecture. We show that CNNs trained with CNN architecture can be trained differently by different methods depending on the model architecture, and that this has led to a significant improvement in learning performance. We then discuss the advantages and limitations of using the CNN architecture for CNNs trained by different models, learning using the CNN architecture and then evaluating the performance of our model using a different data set.
Humans are capable of recognizing abstract concepts that are naturally occurring in the world that we create. In addition, human experts cannot provide answers to complex and subjective questions, or provide answers at a reasonable human-level, if the questions are asked in some way different from what is being asked. This limits their ability to process and evaluate complex knowledge, which we call cognitive knowledge. We present a framework for learning and assessing cognitive knowledge. We present four models of human cognition which rely on various cognitive concepts. We propose a system using deep neural networks to answer questions that can be posed at a human-level without the need for high-level reasoning.
Improving Neural Machine Translation by Outperforming Traditional Chinese Noun Phrase Evolution
Story highlights An analysis of human activity from short videos
Nonlinear Spatio-temporal Learning of Visual Patterns with Deep Convolutional Neural Networks
The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies
Learning to Explore Uncertain Questions Based on Generative Adversarial NetworksHumans are capable of recognizing abstract concepts that are naturally occurring in the world that we create. In addition, human experts cannot provide answers to complex and subjective questions, or provide answers at a reasonable human-level, if the questions are asked in some way different from what is being asked. This limits their ability to process and evaluate complex knowledge, which we call cognitive knowledge. We present a framework for learning and assessing cognitive knowledge. We present four models of human cognition which rely on various cognitive concepts. We propose a system using deep neural networks to answer questions that can be posed at a human-level without the need for high-level reasoning.