3 papers
cs.LG2024
U-TELL: Unsupervised Task Expert Lifelong Learning
Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar +1
Continual learning (CL) models are designed to learn new tasks arriving sequentially without re-training the network. However, real-world ML applications have very limited label in…
cs.LG2023
S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning
Rajdeep Dutta, Qincheng Wang, Ankur Singh +3
This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks. The proposed algorithm leverages two…
cs.LG2022
Latent Preserving Generative Adversarial Network for Imbalance classification
Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama +3
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend…