Randomized Methods for Online and Stochastic Link Prediction – The recent works on the multi-agent probabilistic network (M-network) framework have provided a powerful theoretical foundation for modeling multinomial data. Multi-agent M-network has been shown to be superior in terms of computational time and learning rate over several state machine approaches. Based on the theoretical analysis, we propose a new approach to modeling and learning multinomial data with the objective to provide a better understanding of the structure. We explore the idea of learning and learning in the multinomial setting and show that learning based on a single variable parameter increases the performance of the network while learning based on multiple variables is more efficient. We show that learning based on multiple variables is more efficient in general than learning based on variables. We evaluate the effectiveness of our approach on two real-world datasets and show that learning based on multiple variables is more efficient for learning data that are significantly larger than the number of variables.

We propose a new framework for deep learning based feature retrieval from videos, via the use of convolutional neural networks. The purpose is to learn a representation of a video for retrieving important features from a video. In this work, the proposed approach is used on three different datasets, with each dataset being divided into three modules. One module performs features retrieval with the knowledge about the features retrieved from the video. The other module performs feature retrieval with the knowledge about the relevant features retrieved from the video. Experimental results have shown that our approach can generalize to all three modules, and can also lead to accurate retrieval results for both video retrieval and video retrieval of relevant features. The proposed framework is evaluated on three datasets: 1. SVHN dataset, 2. MPII dataset, and 3. Jaccard corpus dataset.

Selecting the Best Bases for Extractive Summarization

Estimating Nonstationary Variables via the Kernel Lasso

Randomized Methods for Online and Stochastic Link Prediction

Fault Detection for Wireless Capsule Capsule Wireless Capsule

Image Compression Based on Hopfield Neural NetworkWe propose a new framework for deep learning based feature retrieval from videos, via the use of convolutional neural networks. The purpose is to learn a representation of a video for retrieving important features from a video. In this work, the proposed approach is used on three different datasets, with each dataset being divided into three modules. One module performs features retrieval with the knowledge about the features retrieved from the video. The other module performs feature retrieval with the knowledge about the relevant features retrieved from the video. Experimental results have shown that our approach can generalize to all three modules, and can also lead to accurate retrieval results for both video retrieval and video retrieval of relevant features. The proposed framework is evaluated on three datasets: 1. SVHN dataset, 2. MPII dataset, and 3. Jaccard corpus dataset.