ICLR 2025 Conference Submissions

Unsupervised Point Cloud Completion through Unbalanced Optimal Transport
Taekyung Lee, Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Sparse Training: Do All Tokens Matter for Long Sequence Generalization?
Kaitao Song, Zihao Li, Cen LU, Xu Tan, Dongsheng Li
- 25 Sept 2024 (modified: 20 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Interpreting Adversarial Attacks and Defenses using Architectures with Enhanced Interpretability
Akshay G Rao, Chandra Shekar Lakshminarayanan, Arun Rajkumar
- 25 Sept 2024 (modified: 10 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Does Example Selection for In-Context Learning Amplify the Biases of Large Language Models?
Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Xiangyu Zhao, Xin Yao, Xuetao Wei
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Structural Quantile Normalization: a general, differentiable feature scaling technique balancing gaussian approximation and structural preservation
Robert Wiebe, Aris Athanasios Panagopoulos
- 25 Sept 2024 (modified: 26 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Weibin Chen, Azhir Mahmood, Michel Tsamados, So Takao
- Published: 22 Jan 2025, Last Modified: 30 Mar 2025
- ICLR 2025 Poster
- Readers: Everyone
MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian
- Published: 22 Jan 2025, Last Modified: 15 Mar 2025
- ICLR 2025 Poster
- Readers: Everyone
Multi-modality Expansion and Retention for LLMs through Parameter Merging and Decoupling
Junlin Lee, Guodong DU, Jing Li, Sim Kuan Goh, Ho-Kin Tang, Yequan Wang, Fangming Liu, Saleh Alharbi, Daojing He, Min Zhang
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Temporal Test-Time Adaptation with State-Space Models
Mona Schirmer, Dan Zhang, Eric Nalisnick
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
EDSNN: Edge Detection with Spiking Neuron Network
Yimeng Fan, Changsong Liu, Mingyang Li, Wei Zhang
- 25 Sept 2024 (modified: 13 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Understand Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics
Binghui Li, Yuanzhi Li
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
Jiarui Jin, Haoyu Wang, Hongyan Li, Jun Li, Jiahui Pan, Shenda Hong
- Published: 22 Jan 2025, Last Modified: 14 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
A New Perspective on Shampoo's Preconditioner
Depen Morwani, Itai Shapira, Nikhil Vyas, eran malach, Sham M. Kakade, Lucas Janson
- Published: 22 Jan 2025, Last Modified: 26 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
EigenLoRA: Recycle trained Adapters for Resource Efficient Adaptation and Inference
Prakhar Kaushik, Aayush Mishra, Ankit Vaidya, Raghavendra Addanki, Ryan A. Rossi, Ani Nenkova, Anqi Liu, Alan Yuille, Jiuxiang Gu
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
TrackMamba: Mamba-Transformer Tracking
Jiaming Zhang, Cheng Liang, Yutao Cui, Xiangbo Shu, Gangshan Wu, Limin Wang
- 25 Sept 2024 (modified: 14 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Text2PDE: Latent Diffusion Models for Accessible Physics Simulation
Anthony Zhou, Zijie Li, Michael Schneier, John R Buchanan Jr, Amir Barati Farimani
- Published: 22 Jan 2025, Last Modified: 11 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Binghui Li, Yuanzhi Li
- Published: 22 Jan 2025, Last Modified: 11 Feb 2025
- ICLR 2025 Poster
- Readers: Everyone
DBGMS: A Dual-Branch Generative Adversarial Network with Multi-Task Self-Supervised Enhancement for Robust Auditory Attention Decoding
Shuai Huang, Yongxiong Wang, Chendong Qin
- 25 Sept 2024 (modified: 15 May 2025)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment
Yankai Jiang, Wenhui Lei, Xiaofan Zhang, Shaoting Zhang
- Published: 22 Jan 2025, Last Modified: 13 Mar 2025
- ICLR 2025 Poster
- Readers: Everyone
Interpretable Pre-Trained Transformers for Heart Time-Series Data
Harry J Davies, James Monsen, Danilo Mandic
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
Gueter Josmy Faure, Jia-Fong Yeh, Min-Hung Chen, Hung-Ting Su, Shang-Hong Lai, Winston H. Hsu
- 25 Sept 2024 (modified: 30 Nov 2024)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
OC-CLIP : Object-centric binding in Contrastive Language-Image Pretraining
Rim Assouel, Pietro Astolfi, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
Unsupervised 2D Molecule Drug-likeness Prediction based on Knowledge Distillation
Jia Song, Wanru Zhuang, Yujie Lin, Zhao jiale, Shuqi Lu, Jinsong Su, Song He, Xiaochen Bo
- 25 Sept 2024 (modified: 16 Jan 2025)
- ICLR 2025 Conference Withdrawn Submission
- Readers: Everyone
Rethinking Attentions in Zero-Shot Real Image Editing
Dinh-Khoi Vo, Thanh-Toan Do, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone
offline_rl_ope: A Python package for off-policy evaluation of offline RL models with real world data
Joshua William Spear, Matthieu Komorowski, REBECCA POPE, Neil J Sebire
- 25 Sept 2024 (modified: 05 Feb 2025)
- Submitted to ICLR 2025
- Readers: Everyone