b'Jinwoo Shin'

research

∙ 09/19/2023

Guide Your Agent with Adaptive Multimodal Rewards

Developing an agent capable of adapting to unseen environments remains a...

0 Changyeon Kim, et al. ∙

research

∙ 08/15/2023

NeFL: Nested Federated Learning for Heterogeneous Clients

Federated learning (FL) is a promising approach in distributed learning ...

0 Honggu Kang, et al. ∙

research

∙ 07/12/2023

Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models

Large-scale image generation models, with impressive quality made possib...

0 Sanghyun Kim, et al. ∙

research

∙ 05/31/2023

Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration

A promising technique for exploration is to maximize the entropy of visi...

0 Dongyoung Kim, et al. ∙

research

∙ 03/25/2023

IFSeg: Image-free Semantic Segmentation via Vision-Language Model

Vision-language (VL) pre-training has recently gained much attention for...

0 Sukmin Yun, et al. ∙

research

∙ 03/24/2023

Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck

In practical scenarios where training data is limited, many predictive s...

0 Jongheon Jeong, et al. ∙

research

∙ 03/20/2023

Imitating Graph-Based Planning with Goal-Conditioned Policies

Recently, graph-based planning algorithms have gained much attention to ...

0 Junsu Kim, et al. ∙

research

∙ 03/06/2023

Guiding Energy-based Models via Contrastive Latent Variables

An energy-based model (EBM) is a popular generative framework that offer...

0 Hankook Lee, et al. ∙

research

∙ 03/02/2023

Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning

Unsupervised meta-learning aims to learn generalizable knowledge across ...

0 Huiwon Jang, et al. ∙

research

∙ 03/02/2023

Preference Transformer: Modeling Human Preferences using Transformers for RL

Preference-based reinforcement learning (RL) provides a framework to tra...

0 Changyeon Kim, et al. ∙

research

∙ 02/15/2023

Video Probabilistic Diffusion Models in Projected Latent Space

Despite the remarkable progress in deep generative models, synthesizing ...

0 Sihyun Yu, et al. ∙

research

∙ 02/05/2023

Multi-View Masked World Models for Visual Robotic Manipulation

Visual robotic manipulation research and applications often use multiple...

0 Younggyo Seo, et al. ∙

research

∙ 02/01/2023

Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations

We introduce an efficient optimization-based meta-learning technique for...

0 Jihoon Tack, et al. ∙

research

∙ 01/23/2023

Modality-Agnostic Variational Compression of Implicit Neural Representations

We introduce a modality-agnostic neural data compression algorithm based...

0 Jonathan Richard Schwarz, et al. ∙

research

∙ 12/18/2022

Confidence-aware Training of Smoothed Classifiers for Certified Robustness

Any classifier can be "smoothed out" under Gaussian noise to build a new...

0 Jongheon Jeong, et al. ∙

research

∙ 12/13/2022

OAMixer: Object-aware Mixing Layer for Vision Transformers

Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have sh...

0 Hyunwoo Kang, et al. ∙

research

∙ 12/05/2022

Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling

Trying to capture the sample-label relationship, conditional generative ...

0 Junhyun Nam, et al. ∙

research

∙ 10/13/2022

Scalable Neural Video Representations with Learnable Positional Features

Succinct representation of complex signals using coordinate-based neural...

0 Subin Kim, et al. ∙

research

∙ 10/11/2022

Meta-Learning with Self-Improving Momentum Target

The idea of using a separately trained target model (or teacher) to impr...

11 Jihoon Tack, et al. ∙

research

∙ 08/23/2022

String-based Molecule Generation via Multi-decoder VAE

In this paper, we investigate the problem of string-based molecular gene...

9 Kisoo Kwon, et al. ∙

research

∙ 08/10/2022

Robust Continual Test-time Adaptation: Instance-aware BN and Prediction-balanced Memory

Test-time adaptation (TTA) is an emerging paradigm that addresses distri...

0 Taesik Gong, et al. ∙

research

∙ 07/19/2022

Time Is MattEr: Temporal Self-supervision for Video Transformers

Understanding temporal dynamics of video is an essential aspect of learn...

26 Sukmin Yun, et al. ∙

research

∙ 06/16/2022

Patch-level Representation Learning for Self-supervised Vision Transformers

Recent self-supervised learning (SSL) methods have shown impressive resu...

0 Sukmin Yun, et al. ∙

research

∙ 04/05/2022

Zero-shot Blind Image Denoising via Implicit Neural Representations

Recent denoising algorithms based on the "blind-spot" strategy show impr...

2 Chaewon Kim, et al. ∙

research

∙ 04/05/2022

Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation

The paradigm of worst-group loss minimization has shown its promise in a...

0 Junhyun Nam, et al. ∙

research

∙ 03/18/2022

SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

Preference-based reinforcement learning (RL) has shown potential for tea...

8 Jongjin Park, et al. ∙

research

∙ 02/21/2022

Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

In the deep learning era, long video generation of high-quality still re...

8 Sihyun Yu, et al. ∙

research

∙ 12/16/2021

Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

The Mixup scheme suggests mixing a pair of samples to create an augmente...

0 Joonhyung Park, et al. ∙

research

∙ 11/22/2021

DAPPER: Performance Estimation of Domain Adaptation in Mobile Sensing

Many applications that utilize sensors in mobile devices and apply machi...

0 Taesik Gong, et al. ∙

research

∙ 11/18/2021

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Recent unsupervised representation learning methods have shown to be eff...

9 Hankook Lee, et al. ∙

research

∙ 11/17/2021

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

Randomized smoothing is currently a state-of-the-art method to construct...

0 Jongheon Jeong, et al. ∙

research

∙ 10/27/2021

Meta-Learning Sparse Implicit Neural Representations

Implicit neural representations are a promising new avenue of representi...

16 Jaeho Lee, et al. ∙

research

∙ 10/27/2021

RoMA: Robust Model Adaptation for Offline Model-based Optimization

We consider the problem of searching an input maximizing a black-box obj...

0 Sihyun Yu, et al. ∙

research

∙ 10/27/2021

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Behavioral cloning has proven to be effective for learning sequential de...

0 Jongjin Park, et al. ∙

research

∙ 10/26/2021

Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning

Goal-conditioned hierarchical reinforcement learning (HRL) has shown pro...

0 Junsu Kim, et al. ∙

research

∙ 07/30/2021

Object-aware Contrastive Learning for Debiased Scene Representation

Contrastive self-supervised learning has shown impressive results in lea...

3 Sangwoo Mo, et al. ∙

research

∙ 07/22/2021

Abstract Reasoning via Logic-guided Generation

Abstract reasoning, i.e., inferring complicated patterns from given obse...

0 Sihyun Yu, et al. ∙

research

∙ 07/01/2021

Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble

Recent advance in deep offline reinforcement learning (RL) has made it p...

9 Seunghyun Lee, et al. ∙

research

∙ 06/28/2021

Co^2L: Contrastive Continual Learning

Recent breakthroughs in self-supervised learning show that such algorith...

0 Hyuntak Cha, et al. ∙

research

∙ 06/15/2021

Scaling Neural Tangent Kernels via Sketching and Random Features

The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely...

7 Amir Zandieh, et al. ∙

research

∙ 06/09/2021

Self-Improved Retrosynthetic Planning

Retrosynthetic planning is a fundamental problem in chemistry for findin...

0 Junsu Kim, et al. ∙

research

∙ 05/03/2021

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

Retrosynthesis, of which the goal is to find a set of reactants for synt...

0 Hankook Lee, et al. ∙

research

∙ 05/03/2021

Consistency and Monotonicity Regularization for Neural Knowledge Tracing

Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a c...

0 Seewoo Lee, et al. ∙

research

∙ 04/03/2021

Random Features for the Neural Tangent Kernel

The Neural Tangent Kernel (NTK) has discovered connections between deep ...

47 Insu Han, et al. ∙

research

∙ 03/17/2021

Training GANs with Stronger Augmentations via Contrastive Discriminator

Recent works in Generative Adversarial Networks (GANs) are actively revi...

19 Jongheon Jeong, et al. ∙

research

∙ 03/08/2021

Consistency Regularization for Adversarial Robustness

Adversarial training (AT) is currently one of the most successful method...

6 Jihoon Tack, et al. ∙

research

∙ 02/18/2021

State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...

13 Younggyo Seo, et al. ∙

research

∙ 02/07/2021

Model-Augmented Q-learning

In recent years, Q-learning has become indispensable for model-free rein...

0 Youngmin Oh, et al. ∙

research

∙ 12/17/2020

MASKER: Masked Keyword Regularization for Reliable Text Classification

Pre-trained language models have achieved state-of-the-art accuracies on...

0 Seung Jun Moon, et al. ∙

research

∙ 10/26/2020

Provable Memorization via Deep Neural Networks using Sub-linear Parameters

It is known that Θ(N) parameters are sufficient for neural networks to m...

0 Sejun Park, et al. ∙

Jinwoo Shin

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