35 citations · 50 across the 15 of their papers we have counts for
4 papers · 1 filter
Cooperative Deep -learning Framework for Environments Providing Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Sarangapani
In this paper, we address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach. In the proposed…
Learning to Control using Image Feedback
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
Learning to control complex systems using non-traditional feedback, e.g., in the form of snapshot images, is an important task encountered in diverse domains such as robotics, neur…
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Romain Egele, Romit Maulik, Krishnan Raghavan +3
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is co…
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
Krishnan Raghavan, Prasanna Balaprakash
We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game.…