107 citations · 287 across the 17 of their papers we have counts for
5 papers · 1 filter
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew Riemer, Ignacio Cases, Robert Ajemian +4
Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realist…
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel +8
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art metho…
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
German Abrevaya, Irina Rish, Aleksandr Y. Aravkin +7
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium imaging (CaI) of different organisms…
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
Sahil Garg, Irina Rish, Guillermo Cecchi +5
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed…
Contextual Bandit with Adaptive Feature Extraction
Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi +1
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature e…