Learning Video Cascade with Partially-Aware Spatial Transformer Networks – In this paper we propose a new deep learning method for video recognition. The method learns to predict the pose of the camera in each frame and to model the interaction among the cameras in each frame. Based on this model, we use the model to represent the interactions between the camera and the frame. By using the model as a representation of interactions, we can better model interactions of different frame types. Using a convolutional-deconvolutional neural network (CNN), we use the Convolutional Pyramid Network to learn the pose of the cameras. Our proposed method is shown to be efficient for both video recognition and classification tasks. Experimental results on MNIST, CIFAR-10, and Caltech VOC show the performance of the proposed model compared to the previous state-of-the-art deep network methods.
There are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations between words in an input text, and by using the proposed multi-channel recurrent neural network. Several experiments have been conducted on four related tasks: semantic segmentation, topic modeling, recognition and classification. The performance of the proposed multi-channel neural network is comparable to CNNs for semantic segmentation tasks. The results are compared with CNNs and DNNs for semantic segmentation tasks and have very good results.
Flexible Semi-supervised Learning via a Modular Greedy Mass Indexing Method
Learning from the Hindsight Plan: On Learning from Exact Time-series Data
Learning Video Cascade with Partially-Aware Spatial Transformer Networks
Predicting the outcomes of games
Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech TaggingThere are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations between words in an input text, and by using the proposed multi-channel recurrent neural network. Several experiments have been conducted on four related tasks: semantic segmentation, topic modeling, recognition and classification. The performance of the proposed multi-channel neural network is comparable to CNNs for semantic segmentation tasks. The results are compared with CNNs and DNNs for semantic segmentation tasks and have very good results.