b'Se-Young Yun'

research

∙ 08/29/2023

Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval

Contrastive language-image pre-training (CLIP) has demonstrated remarkab...

0 Seongha Eom, et al. ∙

research

∙ 08/24/2023

FedSoL: Bridging Global Alignment and Local Generality in Federated Learning

Federated Learning (FL) aggregates locally trained models from individua...

0 Gihun Lee, et al. ∙

research

∙ 06/19/2023

Enhancing Generalization and Plasticity for Sample Efficient Reinforcement Learning

In Reinforcement Learning (RL), enhancing sample efficiency is crucial, ...

0 Hojoon Lee, et al. ∙

research

∙ 06/18/2023

Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model

We consider the problem of recovering hidden communities in the Labeled ...

0 Kaito Ariu, et al. ∙

research

∙ 05/23/2023

Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Respiratory sound contains crucial information for the early diagnosis o...

0 Sangmin Bae, et al. ∙

research

∙ 05/19/2023

Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation

Transformer-based speech self-supervised learning (SSL) models, such as ...

1 Kangwook Jang, et al. ∙

research

∙ 03/22/2023

Re-thinking Federated Active Learning based on Inter-class Diversity

Although federated learning has made awe-inspiring advances, most studie...

0 SangMook Kim, et al. ∙

research

∙ 03/20/2023

Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning

Deep learning in general domains has constantly been extended to domain-...

1 Sungnyun Kim, et al. ∙

research

∙ 03/09/2023

Communication-Efficient Collaborative Heterogeneous Bandits in Networks

The multi-agent multi-armed bandit problem has been studied extensively ...

0 Junghyun Lee, et al. ∙

research

∙ 03/03/2023

Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning

The multi-agent setting is intricate and unpredictable since the behavio...

0 Jihwan Oh, et al. ∙

research

∙ 02/10/2023

CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition

Class imbalance problems frequently occur in real-world tasks, and conve...

0 Sumyeong Ahn, et al. ∙

research

∙ 02/03/2023

Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective

Knowledge distillation (KD) is a highly promising method for mitigating ...

0 Jongwoo Ko, et al. ∙

research

∙ 12/20/2022

Large Language Models Are Reasoning Teachers

Language models (LMs) have demonstrated remarkable performance on downst...

3 Namgyu Ho, et al. ∙

research

∙ 12/07/2022

MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy

Cell segmentation is a fundamental task for computational biology analys...

0 Gihun Lee, et al. ∙

research

∙ 12/05/2022

Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition

The Weather4Cast competition (hosted by NeurIPS 2022) required competito...

0 Taehyeon Kim, et al. ∙

research

∙ 12/01/2022

Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels

Improperly constructed datasets can result in inaccurate inferences. For...

0 Sumyeong Ahn, et al. ∙

research

∙ 10/18/2022

Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks

Translation has played a crucial role in improving the performance on mu...

0 Jaehoon Oh, et al. ∙

research

∙ 08/17/2022

Nearly Optimal Latent State Decoding in Block MDPs

We investigate the problems of model estimation and reward-free learning...

5 Yassir Jedra, et al. ∙

research

∙ 07/05/2022

The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions

In this paper, we propose a novel benchmark called the StarCraft Multi-A...

0 Mingyu Kim, et al. ∙

research

∙ 06/30/2022

Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction

Precipitation forecasting is an important scientific challenge that has ...

16 Taehyeon Kim, et al. ∙

research

∙ 06/28/2022

Risk Perspective Exploration in Distributional Reinforcement Learning

Distributional reinforcement learning demonstrates state-of-the-art perf...

0 Jihwan Oh, et al. ∙

research

∙ 06/27/2022

Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search

Knowledge Distillation (KD) has recently emerged as a popular method for...

1 Taehyeon Kim, et al. ∙

research

∙ 06/18/2022

Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning

Few-shot class-incremental learning (FSCIL) has addressed challenging re...

0 Jaehoon Oh, et al. ∙

research

∙ 06/15/2022

ALASCA: Rethinking Label Smoothing for Deep Learning Under Label Noise

As label noise, one of the most popular distribution shifts, severely de...

0 Jongwoo Ko, et al. ∙

research

∙ 06/03/2022

Supernet Training for Federated Image Classification under System Heterogeneity

Efficient deployment of deep neural networks across many devices and res...

1 Taehyeon Kim, et al. ∙

research

∙ 05/31/2022

Mitigating Dataset Bias by Using Per-sample Gradient

The performance of deep neural networks is strongly influenced by the tr...

0 Sumyeong Ahn, et al. ∙

research

∙ 05/30/2022

Adversarial Bandits Robust to S-Switch Regret

We study the adversarial bandit problem under S number of switching best...

0 Jung-Hun Kim, et al. ∙

research

∙ 05/13/2022

Revisiting the Updates of a Pre-trained Model for Few-shot Learning

Most of the recent few-shot learning algorithms are based on transfer le...

5 Yujin Kim, et al. ∙

research

∙ 05/11/2022

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning

Cross-domain few-shot learning (CD-FSL), where there are few target samp...

5 Jaehoon Oh, et al. ∙

research

∙ 05/03/2022

FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning

Robustness is becoming another important challenge of federated learning...

0 SangMook Kim, et al. ∙

research

∙ 04/11/2022

Neural Processes with Stochastic Attention: Paying more attention to the context dataset

Neural processes (NPs) aim to stochastically complete unseen data points...

0 Mingyu Kim, et al. ∙

research

∙ 04/08/2022

SuperNet in Neural Architecture Search: A Taxonomic Survey

Deep Neural Networks (DNN) have made significant progress in a wide rang...

0 Stephen Cha, et al. ∙

research

∙ 02/01/2022

Understanding Cross-Domain Few-Shot Learning: An Experimental Study

Cross-domain few-shot learning has drawn increasing attention for handli...

1 Jaehoon Oh, et al. ∙

research

∙ 01/31/2022

Rotting infinitely many-armed bandits

We consider the infinitely many-armed bandit problem with rotting reward...

3 Jung-Hun Kim, et al. ∙

research

∙ 06/29/2021

Self-Contrastive Learning

This paper proposes a novel contrastive learning framework, coined as Se...

10 Sangmin Bae, et al. ∙

research

∙ 06/06/2021

Preservation of the Global Knowledge by Not-True Self Knowledge Distillation in Federated Learning

In Federated Learning (FL), a strong global model is collaboratively lea...

0 Gihun Lee, et al. ∙

research

∙ 05/19/2021

Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation

Knowledge distillation (KD), transferring knowledge from a cumbersome te...

1 Taehyeon Kim, et al. ∙

research

∙ 02/23/2021

Winning Ticket in Noisy Image Classification

Modern deep neural networks (DNNs) become frail when the datasets contai...

1 Taehyeon Kim, et al. ∙

research

∙ 12/30/2020

Test Score Algorithms for Budgeted Stochastic Utility Maximization

Motivated by recent developments in designing algorithms based on indivi...

8 Dabeen Lee, et al. ∙

research

∙ 12/06/2020

TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture

Federated learning has emerged as a new paradigm of collaborative machin...

3 Jin-woo Lee, et al. ∙

research

∙ 10/23/2020

Regret in Online Recommendation Systems

This paper proposes a theoretical analysis of recommendation systems in ...

10 Kaito Ariu, et al. ∙

research

∙ 10/13/2020

MixCo: Mix-up Contrastive Learning for Visual Representation

Contrastive learning has shown remarkable results in recent self-supervi...

1 Sungnyun Kim, et al. ∙

research

∙ 08/20/2020

Does MAML really want feature reuse only?

Meta-learning, the effort to solve new tasks with only a few samples, ha...

0 Jaehoon Oh, et al. ∙

research

∙ 10/14/2019

Optimal Clustering from Noisy Binary Feedback

We study the problem of recovering clusters from binary user feedback. I...

4 Kaito Ariu, et al. ∙

research

∙ 07/29/2019

Reinforcement with Fading Memories

We study the effect of imperfect memory on decision making in the contex...

0 Kuang Xu, et al. ∙

research

∙ 05/14/2019

Spectral Approximate Inference

Given a graphical model (GM), computing its partition function is the mo...

3 Sejun Park, et al. ∙

research

∙ 10/26/2018

Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source

Nowadays, the task of sound source separation is an interesting task for...

0 Jaehoon Oh, et al. ∙

research

∙ 03/03/2017

Contextual Multi-armed Bandits under Feature Uncertainty

We study contextual multi-armed bandit problems under linear realizabili...

0 Se-Young Yun, et al. ∙

research

∙ 10/20/2015

Optimal Cluster Recovery in the Labeled Stochastic Block Model

We consider the problem of community detection or clustering in the labe...

0 Se-Young Yun, et al. ∙

research

∙ 04/13/2015

Streaming, Memory Limited Matrix Completion with Noise

In this paper, we consider the streaming memory-limited matrix completio...

0 Se-Young Yun, et al. ∙

Se-Young Yun

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