ICLR 2024 Conference Submissions

When should we prefer Decision Transformers for Offline Reinforcement Learning?
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
- Published: 16 Jan 2024, Last Modified: 21 Apr 2024
- ICLR 2024 poster
- Readers: Everyone
Retentive Network: A Successor to Transformer for Large Language Models
Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, Zhiyuan Liu
- Published: 16 Jan 2024, Last Modified: 14 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
GSVA: Gradient-Based Sparse Voxel Attacks \\ on Point Cloud Object Detection
Junqi Wu, Wen Yao, Tingsong Jiang, Weien Zhou, Chao Ma, Xiao qian Chen
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Towards Environmental Robustness in Deep Reinforcement Learning
Chenxu Wang, Huaping Liu
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni, Benjamin Eysenbach, Erfan SeyedSalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon
- Published: 16 Jan 2024, Last Modified: 13 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, George Deligiannidis
- Published: 16 Jan 2024, Last Modified: 21 Apr 2024
- ICLR 2024 spotlight
- Readers: Everyone
GM-DDPM: Denoising diffusion probabilistic models with Gaussian Mixture Noise
Hanzhang Wang, Xianming Liu, Xiong Zhou, Junjun Jiang, Deming Zhai, Wen Gao
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
TapMo: Shape-aware Motion Generation of Skeleton-free Characters
Jiaxu Zhang, Shaoli Huang, Zhigang Tu, Xin Chen, Xiaohang Zhan, Gang YU, Ying Shan
- Published: 16 Jan 2024, Last Modified: 05 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Ronghao Dang, Jiangyan Feng, Haodong Zhang, Chongjian GE, Lin Song, Lijun GONG, Chengju Liu, Qijun Chen, Feng Zhu, Rui Zhao, Yibing Song
- Published: 16 Jan 2024, Last Modified: 11 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Xuan-Phi Nguyen, Mahani Aljunied, Shafiq Joty, Lidong Bing
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Jiaxi Gu, Shicong Wang, Haoyu Zhao, Tianyi Lu, Xing Zhang, Zuxuan Wu, Songcen Xu, Wei Zhang, Yu-Gang Jiang, Hang Xu
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Hybrid Representation Learning Via Epistemic Graph
Jin Yuan, Yang Zhang, Yangzhou Du, zhongchao shi, Xin Geng, Jianping Fan, Yong Rui
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang
- Published: 16 Jan 2024, Last Modified: 28 Mar 2024
- ICLR 2024 spotlight
- Readers: Everyone
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits
Chenguang Wang, Tianshu Yu
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Iterated Deep $Q$-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning
Théo Vincent, Boris Belousov, Jan Peters, Carlo D'Eramo
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
SelfEval: Leveraging the discriminative nature of generative models for evaluation
Sai Saketh Rambhatla, Ishan Misra
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Universal Metric Learning with Parameter-Efficient Transfer Learning
Sungyeon Kim, Donghyun Kim, Suha Kwak
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
RAIN: Your Language Models Can Align Themselves without Finetuning
Yuhui Li, Fangyun Wei, Jinjing Zhao, Chao Zhang, Hongyang Zhang
- Published: 16 Jan 2024, Last Modified: 13 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
Zhongpai Gao, Huayi Zhou, Abhishek Sharma, Meng Zheng, Benjamin Planche, Terrence Chen, Ziyan Wu
- Published: 16 Jan 2024, Last Modified: 05 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Prompt-based 3D Molecular Diffusion Models for Structure-based Drug Design
Ling Yang, Zhilin Huang, Xiangxin Zhou, Minkai Xu, Wentao Zhang, Yu Wang, Xiawu Zheng, Wenming Yang, Ron O. Dror, Shenda Hong, Bin CUI
- 18 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model
Hugo Lebeau, Mohamed El Amine Seddik, José Henrique De Morais Goulart
- Published: 16 Jan 2024, Last Modified: 05 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Adapting Cross-View Localization to New Areas without Ground Truth Positions
Zimin Xia, Yujiao Shi, Hongdong Li, Julian F. P. Kooij
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching
Xinghui Li, Jingyi Lu, Kai Han, Victor Adrian Prisacariu
- 18 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Uni3D: Exploring Unified 3D Representation at Scale
Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu, Tiejun Huang, Xinlong Wang
- Published: 16 Jan 2024, Last Modified: 14 Mar 2024
- ICLR 2024 spotlight
- Readers: Everyone