135 citations · 229 across the 7 of their papers we have counts for
7 papers
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
Jeongwoo Jae, Jeonghoon Hong, Jinho Choo +1
Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. W…
CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only
Junhee Cho, Jihoon Kim, Daseul Bae +3
Software robots have long been used in Robotic Process Automation (RPA) to automate mundane and repetitive computer tasks. With the advent of Large Language Models (LLMs) and their…
Simulation-guided Beam Search for Neural Combinatorial Optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim +4
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches ca…
Matrix Encoding Networks for Neural Combinatorial Optimization
Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon +3
Machine Learning (ML) can help solve combinatorial optimization (CO) problems better. A popular approach is to use a neural net to compute on the parameters of a given CO problem a…
Efficient Active Search for Combinatorial Optimization Problems
André Hottung, Yeong-Dae Kwon, Kevin Tierney
Recently numerous machine learning based methods for combinatorial optimization problems have been proposed that learn to construct solutions in a sequential decision process via r…
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon +3
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consist…
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim +3
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a gr…