Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement... Read More

Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium... Read More

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as... Read More

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective... Read More

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network... Read More

We consider a novel formulation of the multi-armed bandit model, which we call the con-textual bandit with restricted context, where only a... Read More

Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a... Read More

In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose... Read More

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject... Read More