5 papers
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
Lipeng Zu, Yu Qian, Shayok Chakraborty +1
Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback under distribution shift. This ch…
MetaErr: Towards Predicting Error Patterns in Deep Neural Networks
Varun Totakura, Shayok Chakraborty
Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. Unfortunately, deep learning sy…
An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Varun Totakura, Ankita Singh, Yushun Dong +1
Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a ma…
ACIL: Active Class Incremental Learning for Image Classification
Aditya R. Bhattacharya, Debanjan Goswami, Shayok Chakraborty
Continual learning (or class incremental learning) is a realistic learning scenario for computer vision systems, where deep neural networks are trained on episodic data, and the da…
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
Chutian Jiang, Hansong Zhou, Xiaonan Zhang +1
Federated learning (FL) enables clients to collaboratively train machine learning models under the coordination of a server in a privacy-preserving manner. One of the main challeng…