Learning Topology-Specific Experts for Molecular Property Prediction

Learning the Experts for Online Sequence Prediction

Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made on the origin of the sequence. Individual sequence prediction algorithms work quite well for long sequences, where the algorithm has enough time to learn the...

Selection of Molecular Descriptor Subsets for Property Prediction

Learning and Prediction of Complex Molecular Structure- Property Relationships: Issues and Strategies for Modeling Intestinal Absorption for Drug Discovery

The problem of modeling and predicting complex structure-property relationships, such as the absorption, distribution, metabolism, and excretion of putative drug molecules is a fundamental one in contemporary drug discovery. An accurate model can not only be used to predict the behavior of a molecule and understand how structural variations may influence molecular property, but also to identify...

Branch Meta-Prediction by Experts

Predicting the direction of conditional branches with high accuracy has become critical to the success of new deeper and wider processor pipelines. However, choosing a branch predictor is hard because the best choice is workload-dependent. Meta(or Hybrid-) predictors provide a rudimentary FSA-based heuristic for choosing between two predictors on-line. Such approaches have little or no theoreti...

Soil Property Prediction: An Extreme Learning Machine Approach

In this paper, we propose a method for predicting functional properties of soil samples from a number of measurable spatial and spectral features of those samples. Our method is based on Savitzky-Golay filter for preprocessing and a relatively recent evolution of single hiddenlayer feed-forward network (SLFN) learning technique called extreme learning machine (ELM) for prediction. We tested our...