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Chong Min John Tan
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2020 – today
- 2023
- [c2]Chong Min John Tan, Mehul Motani:
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments. ICDL 2023: 1-6 - [i7]Chong Min John Tan, Mehul Motani:
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments. CoRR abs/2301.13758 (2023) - [i6]Chong Min John Tan:
An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge. CoRR abs/2306.03553 (2023) - [i5]Chong Min John Tan, Mehul Motani:
Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge. CoRR abs/2310.05146 (2023) - 2022
- [i4]Chong Min John Tan, Mehul Motani:
Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments. CoRR abs/2207.05991 (2022) - [i3]Chong Min John Tan, Mehul Motani:
DropNet: Reducing Neural Network Complexity via Iterative Pruning. CoRR abs/2207.06646 (2022) - [i2]Shiyu Liu, Rohan Ghosh, Chong Min John Tan, Mehul Motani:
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks. CoRR abs/2212.06144 (2022) - 2021
- [i1]Shiyu Liu, Chong Min John Tan, Mehul Motani:
S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks. CoRR abs/2110.08764 (2021) - 2020
- [c1]Chong Min John Tan, Mehul Motani:
DropNet: Reducing Neural Network Complexity via Iterative Pruning. ICML 2020: 9356-9366
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