51 citations · 118 across the 22 of their papers we have counts for
6 papers · 1 filter
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Amin Ghafourian, Huanyi Shui, Devesh Upadhyay +3
Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the a…
KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks
Hemanth Manjunatha, Andrey Pak, Dimitar Filev +1
Autonomous driving has received a great deal of attention in the automotive industry and is often seen as the future of transportation. The development of autonomous driving techno…
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Andrey Pak, Hemanth Manjunatha, Dimitar Filev +1
Autonomous driving has received a lot of attention in the automotive industry and is often seen as the future of transportation. Passenger vehicles equipped with a wide array of se…
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
Lu Wen, Songan Zhang, H. Eric Tseng +3
Meta Reinforcement Learning (Meta-RL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniq…
Safe Reinforcement Learning Using Robust Action Governor
Yutong Li, Nan Li, H. Eric Tseng +3
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the ap…
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
Yashesh Dhebar, Kalyanmoy Deb, Subramanya Nageshrao +2
Black-box AI induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represente…