1 code implementation • 26 Oct 2023 • Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards.
1 code implementation • 11 Jul 2023 • Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
Agents build and use a local map to predict their observations; high surprisal leads to a "fragmentation event" that truncates the local map.
1 code implementation • 1 May 2023 • Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete
The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models.
3 code implementations • CVPR 2021 • Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han
We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task.
no code implementations • 30 Jan 2020 • Jaedong Hwang, Seohyun Kim, Jeany Son, Bohyung Han
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.
Image-level Supervised Instance Segmentation object-detection +4
no code implementations • ICLR 2019 • Te-Lin Wu, Jaedong Hwang, Jingyun Yang, Shaofan Lai, Carl Vondrick, Joseph J. Lim
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning.