ICLR 2024 Conference Submissions

Defying Multi-model Forgetting: Orthogonal Gradient Learning to One-shot Neural Architecture Search
Lianbo Ma, Ye Ma, Yuee Zhou, Guo Yu, Qing Li, Qiang He
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models
Nicholas Bai, Rahul Ajay Iyer, Tuomas Oikarinen, Tsui-Wei Weng
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Exploit Gradient Skew to Circumvent Byzantine Defenses for Federated Learning
Yuchen Liu, Chen Chen, Lingjuan Lyu, Yaochu Jin, Gang Chen
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
S$^6$-DAMON: Unlocking Structured Sparsity in Self-Supervised Speech Models via Data-Model Co-Compression
Yonggan Fu, Zhifan Ye, Zhongzhi Yu, Yingyan Celine Lin
- 23 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling
Tobias Katsch
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Quality Diversity through Human Feedback
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
What Algorithms can Transformers Learn? A Study in Length Generalization
Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Joshua M. Susskind, Samy Bengio, Preetum Nakkiran
- Published: 16 Jan 2024, Last Modified: 21 Apr 2024
- ICLR 2024 poster
- Readers: Everyone
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho
- Published: 16 Jan 2024, Last Modified: 10 May 2024
- ICLR 2024 poster
- Readers: Everyone
Training-free Multi-objective Diffusion Model for 3D Molecule Generation
Xu Han, Caihua Shan, Yifei Shen, Can Xu, Han Yang, Xiang Li, Dongsheng Li
- Published: 16 Jan 2024, Last Modified: 21 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Perceptual Context and Sensitivity in Image Quality Assessment: A Human-Centric Approach
Xudong Li, Jingyuan Zheng, Runze Hu, Xiawu Zheng, Ke Li, Yunhang Shen, Yutao Liu, Yan Zhang, Jian Zhang, Pingyang Dai, Rongrong Ji
- 23 Sept 2023 (modified: 14 Jan 2025)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Graph Neural Networks with Directional Encodings for Anisotropic Elasticity
Fabian Haller, Yue Li, Stelian Coros, Bernhard Thomaszewski
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Generative AI in healthcare: A trustworthy approach
parul berry, Vishwesh Milind Bharadiya, Sree Kumar
- 23 Sept 2023 (modified: 16 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, Xiang Ren
- Published: 16 Jan 2024, Last Modified: 18 Mar 2024
- ICLR 2024 oral
- Readers: Everyone
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning
Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao, Shixuan Li, Yaxing Li, Tianqing Fang, Mingxi Cheng, Paul Bogdan
- Published: 16 Jan 2024, Last Modified: 14 Apr 2024
- ICLR 2024 poster
- Readers: Everyone
The Power of Minimalism in Long Sequence Time-series Forecasting
Zhao Sun, Meng Fang, Dongjin Song, Defu Li, Tianjin Huang, Qinke Peng
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Hamidreza Almasi, Harsh Mishra, Balajee Vamanan, Sathya N. Ravi
- Published: 16 Jan 2024, Last Modified: 17 Mar 2024
- ICLR 2024 poster
- Readers: Everyone
Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach
Yancheng Wang, Ping Li, Yingzhen Yang
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Universally Amplifying Randomized Smoothing for Certified Robustness with Anisotropic Noise
Hanbin Hong, Ali Payani, Ashish Kundu, Binghui Wang, Yuan Hong
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Automatic Hallucination Assessment for Aligned Large Language Models via Transferable Adversarial Attacks
Xiaodong Yu, Hao Cheng, Xiaodong Liu, Dan Roth, Jianfeng Gao
- 23 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
PowerGPT: Foundation Model for Power Systems
Shihao Tu, Yupeng Zhang, Yan Huajiang, Fangbin Ye, An Wen, Zhendong Fu, Yang Yang
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
Put on your detective hat: What’s wrong in this video?
Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Qifan Zhang, Jikai Wang, Vasundhara Komaragiri, Nicholas Ruozzi, Eric Ragan, Yu Xiang, Vibhav Giridhar Gogate
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone
FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Wenchao Xu, Haozhao Wang, Ruixuan Li, Wei Liu, Jiaming Li, Xiaoquan Yi, Yuhua Li
- 23 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Low-Rank Robust Graph Contrastive Learning
Yancheng Wang, Yingzhen Yang
- 23 Sept 2023 (modified: 25 Mar 2024)
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone
Representation-space diffusion models for generating periodic materials
Anshuman Sinha, Shuyi Jia, Victor Fung
- 23 Sept 2023 (modified: 11 Feb 2024)
- Submitted to ICLR 2024
- Readers: Everyone