no code implementations • 16 Jan 2024 • Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, Songhee You
This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions.
no code implementations • 22 Sep 2022 • Seonil Son, Jaeseo Lim, Youwon Jang, Jaeyoung Lee, Byoung-Tak Zhang
We compare our approach with Unlikelihood (UL) training in a text continuation task on commonsense natural language inference (NLI) corpora to show which method better models the coherence by avoiding unlikely continuations.
no code implementations • 20 Jan 2022 • Jaeyoung Lee, Sean Sedwards, Krzysztof Czarnecki
In this work, after describing and motivating our problem with a simple example, we present a suitable constrained reinforcement learning algorithm that prevents learning instability, using recursive constraints.
no code implementations • 26 Nov 2020 • Sanghwa Lee, Jaeyoung Lee, Ichiro Hasuo
Prioritized experience replay (PER) samples important transitions, rather than uniformly, to improve the performance of a deep reinforcement learning agent.
no code implementations • 29 Oct 2020 • Byungju Kim, Jaeyoung Lee, KyungSu Kim, Sungjin Kim, Junmo Kim
In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks.
no code implementations • 21 Aug 2019 • Marko Ilievski, Sean Sedwards, Ashish Gaurav, Aravind Balakrishnan, Atrisha Sarkar, Jaeyoung Lee, Frédéric Bouchard, Ryan De Iaco, Krzysztof Czarnecki
We explore the complex design space of behaviour planning for autonomous driving.
no code implementations • 11 Feb 2019 • Jaeyoung Lee, Aravind Balakrishnan, Ashish Gaurav, Krzysztof Czarnecki, Sean Sedwards
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge.
1 code implementation • 9 May 2017 • Jaeyoung Lee, Richard S. Sutton
Policy iteration (PI) is a recursive process of policy evaluation and improvement for solving an optimal decision-making/control problem, or in other words, a reinforcement learning (RL) problem.