69 citations · 259 across the 24 of their papers we have counts for
5 papers · 1 filter
Layer-adaptive sparsity for the Magnitude-based Pruning
Jaeho Lee, Sejun Park, Sangwoo Mo +2
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sp…
Guiding Deep Molecular Optimization with Genetic Exploration
Sungsoo Ahn, Junsu Kim, Hankook Lee +1
De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promisin…
Learning from Failure: Training Debiased Classifier from Biased Classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn +2
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased. While previous work tackles t…
Learning What to Defer for Maximum Independent Sets
Sungsoo Ahn, Younggyo Seo, Jinwoo Shin
Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields. Recently, deep reinforcement learning (DRL) frameworks have gained…
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Sungsoo Ahn, Roben Delos Reyes +2
QTRAN is a multi-agent reinforcement learning (MARL) algorithm capable of learning the largest class of joint-action value functions up to date. However, despite its strong theoret…