ICLR 2025 Conference Submissions

$E^3$former: An Adaptive Energy-Aware Elastic Equivariant Transformer Model For Protein Representation Learning
Zhongyue Zhang, Runze Ma, Yanjie Huang, Shuangjia Zheng
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
LLaMA Decoder As Vision Transformer
Jiahao Wang, Wenqi Shao, Mengzhao Chen, Chengyue Wu, Yong Liu, Taiqiang Wu, Kaipeng Zhang, Songyang Zhang, Kai Chen, Ping Luo
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Motion-Catcher: Upholding Motion and Content Consistency in Multi-Sequence Video Generation
Zhicheng Gong, Fangzhou Yi, Qi Zhou, HuiZeng
- 26 Sept 2024 (modified: 19 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
DM-Codec: Distilling Multimodal Representations for Speech Tokenization
Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, AKMMAHBUBUR RAHMAN, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
- 26 Sept 2024 (modified: 16 Dec 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Variational Mirror Descent for Robust Learning in Schrödinger Bridge
Dong-Sig Han, Jaein Kim, HEE BIN YOO, Byoung-Tak Zhang
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Rethinking and Improving Autoformalization: Towards a Faithful Metric and a Dependency Retrieval-based Approach
Qi Liu, Xinhao Zheng, Xudong Lu, Qinxiang Cao, Junchi Yan
- Published: 22 Jan 2025, Last Modified: 14 Mar 2025
- ICLR 2025 Spotlight
- Readers: Everyone
On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning
Yunlu Yan, Chun-Mei Feng, Wangmeng Zuo, Salman Khan, Yong Liu, Lei Zhu
- Published: 22 Jan 2025, Last Modified: 27 Apr 2025
- ICLR 2025 Poster
- Readers: Everyone
Structured Diffusion Models with Mixture of Gaussians as Prior Distribution
Nanshan Jia, Tingyu Zhu, Haoyu Liu, Zeyu Zheng
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Vision Foundation Models Bridge the Geometric Knowledge Across Domains for Long-Tailed Recognition
Yanbiao Ma, Licheng Jiao, Lingling Li, Bowei Liu, Fang Liu, Jiahao Wang, Shuyuan Yang, Xu Liu, Wenping Ma
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel
- Published: 22 Jan 2025, Last Modified: 11 Feb 2025
- ICLR 2025 Spotlight
- Readers: Everyone
Loius (Look it up in the Structure): Benchmark and Techniques for Document structure aware LLM based Retrieval
Vineet Kumar, vishwajeet kumar, Jaydeep Sen, Riyaz Ahmad Bhat, Sachindra Joshi
- 26 Sept 2024 (modified: 14 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A Malik, Yarin Gal
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Evaluating Deep Unlearning in Large Language Models
Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
TwinsFormer: Revisiting Inherent Dependencies via Two Interactive Components for Time Series Forecasting
Yingbo Zhou, Yutong Ye, Pengyu Zhang, Xiao Du, Mingsong Chen
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Towards Reliable Backdoor Attacks on Vision Transformers
Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang
- 26 Sept 2024 (modified: 14 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Junfeng Fang, Hongcheng Gao, Shiyu Ni, Xueqi Cheng
- Published: 22 Jan 2025, Last Modified: 19 Mar 2025
- ICLR 2025 Poster
- Readers: Everyone
MAVIN: Multi-Action Video Generation with Diffusion Models via Transition Video Infilling
Bowen Zhang, Xiaofei Xie, Haotian Lu, NA MA, Tianlin Li, Qing Guo
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Differentiation of Multi-objective Data-driven Decision Pipeline
Peng Li, Lixia Wu, Chaoqun Feng, Jieping Ye, Lei Fu
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
TopoGaussian: Inferring Internal Topology Structures from Visual Clues
Xiaoyu Xiong, Changyu Hu, Chunru Lin, Pingchuan Ma, Chuang Gan, Tao Du
- Published: 22 Jan 2025, Last Modified: 28 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
Tree of Attributes Prompt Learning for Vision-Language Models
Tong Ding, Wanhua Li, Zhongqi Miao, Hanspeter Pfister
- Published: 22 Jan 2025, Last Modified: 28 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
RAEE: A Robust Retrieval-Augmented Early Exit Framework for Efficient Inference
LIANMING HUANG, Shangyu Wu, Yufei Cui, Ying Xiong, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
June Suk Choi, Kyungmin Lee, Jongheon Jeong, Saining Xie, Jinwoo Shin, Kimin Lee
- Published: 22 Jan 2025, Last Modified: 16 Mar 2025
- ICLR 2025 Poster
- Readers: Everyone
Training and Evaluating Causal Forecasting Models for Time-Series
Thomas Crasson, Yacine Nabet, Mathias Lécuyer
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Learning Spatiotemporal Dynamical Systems from Point Process Observations
Valerii Iakovlev, Harri Lähdesmäki
- Published: 22 Jan 2025, Last Modified: 11 Feb 2025
- ICLR 2025 Spotlight
- Readers: Everyone
PBCAT: Patch-Based Composite Adversarial Training against Physically Realizable Attacks on Object Detection
Xiao Li, Yiming Zhu, Yifan Huang, Wei Zhang, Yingzhe He, Jie Shi, Xiaolin Hu
- 26 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone