Long-term prediction of chaotic systems A new deep recurrent architecture capable of learning the state evolution of various chaotic dynamical systems, substantially extending the prediction horizon. Clinical Trial Prediction A Biological Network-Based Regularized Artificial Neural Network Model for Robust Phenotype Prediction from Gene Expression Data. Cancer Subtype Clustering Clustering on Sparse Data in Non-Overlapping Feature Space with Applications to Cancer Subtyping. TimeCLR: A Contrastive Learning Based Framework for Video Classification using new deep architectures to learn the state evolution of a variety of chaotic dynamical systems and significantly extend the prediction time. Deep symbolic regression Using a suitable reward mechanism with the reinforcement learning to identify the correct mathematical expression from an exponentially growing space of expressions that may describe a given dataset. Interpretable Spatio-Temporal Modeling Combined ensemble learning and the multi-Markov-Blankets concept in Bayesian probability theory to provide accurate predictions of extreme precipitation events. Crime Hot Spot Forecasting Integrated transposed convolutions and deep recurrent networks to capture complex spatio-temporal patterns in crime data and generate accurate forecasts of crime hotspots.
