DropNet: Reducing Neural Network Complexity via Iterative Pruning. - Chong Min John Tan; Mehul Motani (ICML, 2020)

S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks. - Shiyu Liu; Chong Min John Tan; Mehul Motani (CoRR, 2021)

Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments. - Chong Min John Tan; Mehul Motani (CoRR, 2022)

Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments. - Chong Min John Tan; Mehul Motani (ICDL, 2023)

An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge. - Chong Min John Tan (CoRR, 2023)

Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge. - Chong Min John Tan; Mehul Motani (CoRR, 2023)

Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks. - Shiyu Liu; Rohan Ghosh; Chong Min John Tan; Mehul Motani (Trans. Mach. Learn. Res., 2023)

TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON. - Chong Min John Tan; Prince Saroj; Bharat Runwal; Hardik Maheshwari; Brian Lim Yi Sheng; Richard Cottrill; Alankrit Chona; Ambuj Kumar; Mehul Motani (CoRR, 2024)