Advances in Probabilistic Modeling of Knowledge – We propose a novel probabilistic learning framework to model the relationship between an entity and its world. We show that our framework can be applied to the problem of dependency tree processing. We then provide a comprehensive analysis of the proposed framework based on a principled notion of dependency between entities and a Bayesian framework for estimating the dependency structure. Our empirical evaluation confirms that both our framework and the Bayesian framework converge to a state of the art accuracy of 97.2% on average.
In this paper, we present an approach for evaluating the quality of a query (an image sequence) using a small number of predictions over the sequence. We propose a novel algorithm for predicting the quality score (a prediction) of a visual sequence, based on a simple Bayesian inference framework. Our algorithm generates a set of prediction estimates based on a low-rank matrix and combines these with a new low-rank function to estimate the quality score. We train a new dataset of images and use it to improve the prediction accuracy. We show that our algorithm outperforms the previous state-of-the-art on a variety of benchmarks using different datasets. We give our intuition on the validity of our algorithm and show that it has good predictions on the benchmarks.
Constrained Two-Stage Multiple Kernel Learning for Graph Signals
Advances in Probabilistic Modeling of Knowledge
Deep Convolutional Neural Network for Brain Decoding
Stability in Monte-Carlo Tree SearchIn this paper, we present an approach for evaluating the quality of a query (an image sequence) using a small number of predictions over the sequence. We propose a novel algorithm for predicting the quality score (a prediction) of a visual sequence, based on a simple Bayesian inference framework. Our algorithm generates a set of prediction estimates based on a low-rank matrix and combines these with a new low-rank function to estimate the quality score. We train a new dataset of images and use it to improve the prediction accuracy. We show that our algorithm outperforms the previous state-of-the-art on a variety of benchmarks using different datasets. We give our intuition on the validity of our algorithm and show that it has good predictions on the benchmarks.