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

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

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

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

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

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

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

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

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

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a... En savoir plus