13 Papers Accepted at ICLR 2024

Congratulations to the following scientists from A*STAR’s Centre for Frontier AI Research (CFAR) whose papers have been accepted at the International Conference on Learning Representations (ICLR) 2024:

Prof Ivor Tsang, Director
Dr Cheston Tan, Senior Principal Scientist
Dr Atsushi Nitanda, Principal Scientist
Dr Joey Zhou, Principal Scientist
Dr Foo Chuan Sheng, Principal Scientist
Dr Guo Qing, Senior Scientist
Dr He Yang, Scientist
Dr Lyu Yueming, Scientist

Held from 7 – 11 May 2024 at Vienna, Austria, ICLR 2024 is a globally renowned conference in the field of deep learning. The annual conference features cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

List of accepted papers:

IRAD: Implicit Representation-driven Image Resampling Against Adversarial Attacks
Yue Cao, Tianlin Li, Xiaofeng Cao, Ivor Tsang, Yang Liu, Qing Guo
LRR: Language-driven Resamplable Continuous Representation Against Adversarial Tracking Attacks
Jianlang Chen, Xuhong Ren, Qing Guo, Felix Juefei-Xu, Di Lin, Wei Feng, Lei Ma, Jianjun Zhao
Koopman-Based Bound for Generalisation: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, and Taiji Suzuki
Anisotropy Helps: Improved Statistical and Computational Complexity of the Mean-Field Langevin Dynamics Under Structured Data
Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, and Denny Wu
Multisize Dataset Condensation (Oral presentation)
Yang He, Lingao Xiao, Joey Tianyi Zhou, Ivor Tsang
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Yang He, Joey Tianyi Zhou
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu
A Unified Framework for Bayesian Optimisation under Contextual Uncertainty
Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low
Self-Teaching Prompting for Multi-Intent Learning with Limited Supervision (Tiny Paper)
Chen Cheng, Ivor Tsang
Decentralised Riemannian Conjugate Gradient Method on the Stiefel Manifold
Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor Tsang, Yong Liu
On Harmonising Implicit Subpopulations
Feng Hong, Jiangchao Yao, Yueming Lyu, Zhihan Zhou, Ivor Tsang, Ya Zhang, Yanfeng Wang
Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning
Feiyang Ye, Yueming Lyu, Xuehao Wang, Yu Zhang, Ivor Tsang
Dissecting Zero-Shot Visual Reasoning Capabilities in Vision and Language Models (Tiny Paper)
Aishik Nagar, Shantanu Jaiswal, Cheston Tan

Find out more about ICLR 2024.