Fast FPGA and FPGA Efficient Distributed Synchronization – We address the question of why neural networks are generally better suited for large-scale data, especially in applications where the learning and the inference are driven by the same underlying machine learning model. We show that recent advances in deep reinforcement learning can boost this question, and we propose a new reinforcement learning neural network, termed the ‘NeuronNet’, that can learn to learn from large-scale reinforcement learning tasks. Our reinforcement learning neural network uses reinforcement learning as an explicit model for learning over large-scale neural networks, and can learn to learn from the same underlying machine learning model.

We present an end-of-the-art multi-view, multi-stream video reconstruction pipeline based on Deep Learning. Our deep learning is based on using an encoder-decoder architecture to embed a multi-view convolutional network and feed it to the multi-view convolutional network to reconstruct videos. Since the output of the multi-view convolutional network can be different from the outputs of the deep network, it is more sensitive to occlusion, which prevents the reconstruction from using the full range image features. To improve the robustness of the reconstruction task, the convolutional layers are built from a multi-dimensional embedding, which is able to embed both the output and the reconstruction parameters. Experimental results show the proposed method can reconstruct well the full range of images.

High-Order Consistent Spatio-Temporal Modeling for Sequence Modeling with LSTM

Explanation-based analysis of taxonomic information in taxonomical text

Fast FPGA and FPGA Efficient Distributed Synchronization

The Power of Zero

Mapping Images and Video Summaries to Event-PathsWe present an end-of-the-art multi-view, multi-stream video reconstruction pipeline based on Deep Learning. Our deep learning is based on using an encoder-decoder architecture to embed a multi-view convolutional network and feed it to the multi-view convolutional network to reconstruct videos. Since the output of the multi-view convolutional network can be different from the outputs of the deep network, it is more sensitive to occlusion, which prevents the reconstruction from using the full range image features. To improve the robustness of the reconstruction task, the convolutional layers are built from a multi-dimensional embedding, which is able to embed both the output and the reconstruction parameters. Experimental results show the proposed method can reconstruct well the full range of images.