머신러닝, 학회
ICLR 논문을 낸 국내 연구실
탑티어International Conference on Learning Representations. 2023년 이후 논문 537편, 연구실 205곳
이 학회에 논문을 낸 연구실
고려대 진경환 교수모집 안내
탑티어 23전체 28구성원 –
생성 모델·디퓨전경량화·효율 AI고려대 인호 교수
탑티어 2전체 20구성원 13
AI 안전·정렬연합·분산 학습고려대 강성구 교수신생 랩모집 안내
탑티어 31전체 34구성원 17
추천·검색대규모 언어 모델
최근 논문
- Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
2024, 비전 및 학습 연구실, 머신러닝 & 마인드 랩
- Learning to Embed Time Series Patches Independently
2024, Machine Learning Lab
- Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
2024, 김승룡 연구실
- Linear attention is (maybe) all you need (to understand Transformer optimization)
2024, 윤철희 연구실
- Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
2024, 이주호 연구실
- LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
2024, 예종철 연구실
- Local Search GFlowNets
2024, System Intelligence Lab, 안성수 연구실
- Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
2024, 시각인공지능
- Minimum width for universal approximation using ReLU networks on compact domain
2024, Efficient Inference and Learning Lab
- Neural Language of Thought Models
2024, 머신러닝 & 마인드 랩
- Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
2024, 기계 지능 연구실
- Online Continual Learning for Interactive Instruction Following Agents
2024, 기계 인지 및 추론 연구실
- On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
2024, PDE&AI Lab
- PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
2024, 빅데이터 분석 및 학습 연구실, 기계 인지 및 추론 연구실
- PAC Prediction Sets Under Label Shift
2024, Machine Learning Lab, Machine Learning Lab, Machine Learning Lab
- Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
2024, 디스플레이소자 연구실
- Progressive Fourier Neural Representation for Sequential Video Compilation
2024, 황성주 연구실, 인공지능 및 머신러닝 연구실
- Prometheus: Inducing Fine-Grained Evaluation Capability in Language Models
2024, 서민준 연구실
- Querying Easily Flip-flopped Samples for Deep Active Learning
2024, AGI-LAB, KAIST, 인공지능 및 머신러닝 연구실
- Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
2024, MIPA Laboratory




























