Jaeho Lee

Affiliations

POSTECH

papers

262

total citations

papers (13)

Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity

ICML 2024arXiv

citations

Learning Large-scale Neural Fields via Context Pruned Meta-Learning

NEURIPS 2023arXiv

citations

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

ICLR 2025arXiv

citations

In Search of a Data Transformation That Accelerates Neural Field Training

CVPR 2024arXiv

citations

Hybrid Neural Representations for Spherical Data

ICML 2024arXiv

citations

Prompt-based Depth Pruning of Large Language Models

ICML 2025arXiv

citations

Learning from Failure: De-biasing Classifier from Biased Classifier

NEURIPS 2020

citations

Co2L: Contrastive Continual Learning

ICCV 2021

citations

Jaeho Lee

Affiliations

papers (13)

Learning Bounds for Risk-sensitive Learning

Meta-Learning Sparse Implicit Neural Representations

Discovering and Mitigating Visual Biases through Keyword Explanation

Scalable Neural Video Representations with Learnable Positional Features

Meta-Learning with Self-Improving Momentum Target

Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity

Learning Large-scale Neural Fields via Context Pruned Meta-Learning

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

In Search of a Data Transformation That Accelerates Neural Field Training

Hybrid Neural Representations for Spherical Data

Prompt-based Depth Pruning of Large Language Models

Learning from Failure: De-biasing Classifier from Biased Classifier

Co2L: Contrastive Continual Learning

papers (13)

Learning Bounds for Risk-sensitive Learning

Meta-Learning Sparse Implicit Neural Representations

Discovering and Mitigating Visual Biases through Keyword Explanation

Scalable Neural Video Representations with Learnable Positional Features

Meta-Learning with Self-Improving Momentum Target

Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity

Learning Large-scale Neural Fields via Context Pruned Meta-Learning

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

In Search of a Data Transformation That Accelerates Neural Field Training

Hybrid Neural Representations for Spherical Data

Prompt-based Depth Pruning of Large Language Models

Learning from Failure: De-biasing Classifier from Biased Classifier

Co2L: Contrastive Continual Learning