Partial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data

Layerwise Interweaving Convolutional LSTM

A deep network structure is formed with LSTM layer and convolutional layer interweaves with each other. The Layerwise Interweaving Convolutional LSTM(LIC-LSTM) enhanced the feature extraction ability of LSTM stack and is capable for versatile sequential data modeling. Its unique network structure allows it to extract higher level features with sequential information involved. Experiment results...

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic s...

Forward-Backward Convolutional LSTM for Acoustic Modeling

An automatic speech recognition (ASR) performance has greatly improved with the introduction of convolutional neural network (CNN) or long-short term memory (LSTM) for acoustic modeling. Recently, a convolutional LSTM (CLSTM) has been proposed to directly use convolution operation within the LSTM blocks and combine the advantages of both CNN and LSTM structures into a single architecture. Thi...

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networ...

Image Reconstruction from Incomplete Data