Search Results for author: Hojoon Lee

Found 12 papers, 8 papers with code

Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning

1 code implementation10 Jun 2024 Donghu Kim, Hojoon Lee, Kyungmin Lee, Dongyoon Hwang, Jaegul Choo

In contrast, objectives focused on learning task-specific knowledge (e. g., identifying agents and fitting reward functions) improve performance in environments similar to the pre-training dataset but not in varied ones.

Atari Games Reinforcement Learning (RL)

Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control

1 code implementation10 Jun 2024 Dongyoon Hwang, Byungkun Lee, Hojoon Lee, Hyunseung Kim, Jaegul Choo

To this end, we introduce Convolution Injector (CoIn), an add-on module that injects convolutions which are rich in locality and equivariance biases into a pretrained ViT for effective adaptation in visuo-motor control.

Inductive Bias

Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks

1 code implementation1 Jun 2024 Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle

By periodically reinitializing the Hare network to the Tortoise's weights, our method preserves plasticity while retaining general knowledge.

General Knowledge Hippocampus

Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems

no code implementations22 Aug 2023 Hojoon Lee, Dongyoon Hwang, Kyushik Min, Jaegul Choo

In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward.

Recommendation Systems Reinforcement Learning (RL)

ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal

1 code implementation21 Aug 2023 Hojoon Lee, Hawon Jeong, Byungkun Lee, Kyungyup Lee, Jaegul Choo

In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal.

Graph Neural Network

On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning

1 code implementation9 Jun 2023 Hojoon Lee, Koanho Lee, Dongyoon Hwang, Hyunho Lee, Byungkun Lee, Jaegul Choo

To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space.

Reinforcement Learning (RL) Representation Learning

Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

1 code implementation12 Oct 2022 Junwoo Park, Youngwoo Cho, Gyuhyeon Sim, Hojoon Lee, Jaegul Choo

By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks.

Classification Sound Classification

DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games

1 code implementation27 Apr 2022 Hojoon Lee, Dongyoon Hwang, Hyunseung Kim, Byungkun Lee, Jaegul Choo

To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players.

Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective

no code implementations5 Nov 2021 Kha Dinh Duy, Taehyun Noh, Siwon Huh, Hojoon Lee

Hence, researchers have leveraged the Trusted Execution Environments (TEEs) to build confidential ML computation systems.

BIG-bench Machine Learning

Learning Representations by Contrasting Clusters While Bootstrapping Instances

no code implementations1 Jan 2021 Junsoo Lee, Hojoon Lee, Inkyu Shin, Jaekyoung Bae, In So Kweon, Jaegul Choo

Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.

Clustering Contrastive Learning +5

Cannot find the paper you are looking for? You can Submit a new open access paper.