Search Results for author: Sungsoo Ahn

Found 15 papers, 5 papers with code

RoMA: Robust Model Adaptation for Offline Model-based Optimization

no code implementations NeurIPS 2021 Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin

We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.

Abstract Reasoning via Logic-guided Generation

no code implementations22 Jul 2021 Sihyun Yu, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin

Abstract reasoning, i. e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.

Self-Improved Retrosynthetic Planning

1 code implementation9 Jun 2021 Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin

Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself.

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

no code implementations3 May 2021 Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Eunho Yang, Sung-Ju Hwang, Jinwoo Shin

Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning.

Contrastive Learning

Layer-adaptive sparsity for the Magnitude-based Pruning

1 code implementation ICLR 2021 Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin

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 sparsity and performance.

Image Classification Network Pruning

Learning from Failure: Training Debiased Classifier from Biased Classifier

2 code implementations6 Jul 2020 Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, Jinwoo Shin

Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.

Guiding Deep Molecular Optimization with Genetic Exploration

2 code implementations NeurIPS 2020 Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin

De novo molecular design attempts to search over the chemical space for molecules with the desired property.

Imitation Learning

Learning What to Defer for Maximum Independent Sets

no code implementations ICML 2020 Sungsoo Ahn, Younggyo Seo, Jinwoo Shin

Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.

Combinatorial Optimization

Deep Auto-Deferring Policy for Combinatorial Optimization

no code implementations25 Sep 2019 Sungsoo Ahn, Younggyo Seo, Jinwoo Shin

Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.

Combinatorial Optimization

Variational Information Distillation for Knowledge Transfer

2 code implementations CVPR 2019 Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai

We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10.

Knowledge Distillation Transfer Learning

Gauged Mini-Bucket Elimination for Approximate Inference

no code implementations5 Jan 2018 Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller

Recently, so-called gauge transformations were used to improve variational lower bounds on $Z$.

Gauging Variational Inference

no code implementations NeurIPS 2017 Sungsoo Ahn, Michael Chertkov, Jinwoo Shin

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM).

Variational Inference

MCMC assisted by Belief Propagation

no code implementations29 May 2016 Sungsoo Ahn, Michael Chertkov, Jinwoo Shin

Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series.

Minimum Weight Perfect Matching via Blossom Belief Propagation

no code implementations NeurIPS 2015 Sungsoo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin

Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM).

Combinatorial Optimization

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