| Dropnet: Reducing neural network complexity via iterative pruning CMJ Tan, M Motani International Conference on Machine Learning, 9356-9366, 2020 | 46 | 2020 |
| S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks S Liu, CMJ Tan, M Motani arXiv preprint arXiv:2110.08764, 2021 | 2 | 2021 |
| Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments JCM Tan, M Motani 2023 IEEE International Conference on Development and Learning (ICDL), 1-6, 2023 | 1 | 2023 |
| Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge JCM Tan, M Motani arXiv preprint arXiv:2310.05146, 2023 | 1 | 2023 |
| An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge TJC Min arXiv preprint arXiv:2306.03553, 2023 | 1 | 2023 |
| Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments JTC Min, M Motani arXiv preprint arXiv:2207.05991, 2022 | 1 | 2022 |
| Using hippocampal replay to consolidate experiences in memory-augmented reinforcement learning JCM Tan, M Motani Memory in Artificial and Real Intelligence workshop@ NeurIPS 2022, 2022 | 1 | 2022 |
| Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks S Liu, R Ghosh, JTC Min, M Motani arXiv preprint arXiv:2212.06144, 2022 | | 2022 |
| Go-Explore with a guide: Speeding up search in sparse reward settings with goal-directed intrinsic rewards CMJ Tan, M Motani | | 2022 |
| Thursday, November 9, 2023 CMJ Tan, M Motani, S Komura, K Maeyama, A Taniguchi, T Taniguchi, ... | | |
Mehul MotaniNational University of SingaporeVerified email at nus.edu.sg
