137 citations · 351 across the 41 of their papers we have counts for
44 papers
Authority-Preserving Evaluation of Medical Vision-Language Assistants
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Medical vision-language models can propose how urgently a skin lesion should be reviewed, but the local service retains authority to accept or replace that proposal under referral…
FedPref: Federated Preference Learning for Structured Radiology Report Extraction
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +1
Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labe…
EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams
Zixuan Chen, Jiakai Zhang, Junhao Dong +4
Event cameras, with high temporal resolution, high dynamic range, and asynchronous sensing characteristics, have shown great potential for dense 3D reconstruction. Traditional reco…
Unifying Adversarially Robust Model Experts in Vision-Language Models
Nguyen Duc Thai, Junhao Dong, Sua Qi Rong +2
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment. Adversarial fine-tuning emer…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Out-of-Distribution Generalization for Neural Physics Solvers
Zhao Wei, Chin Chun Ooi, Jian Cheng Wong +3
Neural physics solvers are increasingly used in scientific discovery, given their potential for rapid in silico insights into physical, materials, or biological systems and their l…
MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Models
Yuqi Li, Junhao Dong, Chuanguang Yang +5
Vision-Language Models (VLMs) are increasingly deployed in safety-critical applications, making their adversarial robustness a crucial concern. While adversarial knowledge distilla…
Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals
Li Rong Wang, Thomas C. Henderson, Yew Soon Ong +2
Vital signs, such as heart rate and blood pressure, are critical indicators of patient health and are widely used in clinical monitoring and decision-making. While deep learning mo…
A Continuous Encoding-Based Representation for Efficient Multi-Fidelity Multi-Objective Neural Architecture Search
Zhao Wei, Chin Chun Ooi, Yew-Soon Ong
Neural architecture search (NAS) is an attractive approach to automate the design of optimized architectures but is constrained by high computational budget, especially when optimi…
Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Meng Xu, Frank Neumann, Aneta Neumann +1
Real-world optimization often demands diverse, high-quality solutions. Quality-Diversity (QD) optimization is a multifaceted approach in evolutionary algorithms that aims to genera…
Evolvable Conditional Diffusion
Zhao Wei, Chin Chun Ooi, Abhishek Gupta +4
This paper presents an evolvable conditional diffusion method such that black-box, non-differentiable multi-physics models, as are common in domains like computational fluid dynami…
FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +2
In the era of increasing privacy concerns and demand for personalized experiences, traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face significant challen…
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Zhilong Li, Xiaohu Wu, Xiaoli Tang +6
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative t…
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Jiawen Zhu, Yew-Soon Ong, Chunhua Shen +1
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without usi…
DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction
Hua Yu, Yaqing Hou, Wenbin Pei +1
Diverse human motion prediction (HMP) aims to predict multiple plausible future motions given an observed human motion sequence. It is a challenging task due to the diversity of po…
Bridging the Gap Between Theory and Practice: Benchmarking Transfer Evolutionary Optimization
Yaqing Hou, Wenqiang Ma, Abhishek Gupta +5
In recent years, the field of Transfer Evolutionary Optimization (TrEO) has witnessed substantial growth, fueled by the realization of its profound impact on solving complex proble…
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs pri…
Virtual Co-Pilot: Multimodal Large Language Model-enabled Quick-access Procedures for Single Pilot Operations
Fan Li, Shanshan Feng, Yuqi Yan +2
Advancements in technology, pilot shortages, and cost pressures are driving a trend towards single-pilot and even remote operations in aviation. Considering the extensive workload…
Multi-Task Learning with Multi-Task Optimization
Lu Bai, Abhishek Gupta, Yew-Soon Ong
Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading…
Precise-Physics Driven Text-to-3D Generation
Qingshan Xu, Jiao Liu, Melvin Wong +2
Text-to-3D generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mostly focus on geometric or visual p…
Make Me Happier: Evoking Emotions Through Image Diffusion Models
Qing Lin, Jingfeng Zhang, Yew-Soon Ong +1
Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, mak…
PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
Jianyao Xu, Qingshan Xu, Xinyao Liao +4
Neural surfaces learning has shown impressive performance in multi-view surface reconstruction. However, most existing methods use large multilayer perceptrons (MLPs) to train thei…
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan, Hao Cheng, Xiaohu Wu +5
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to selec…
Adaptive In-Context Learning with Large Language Models for Bundle Generation
Zhu Sun, Kaidong Feng, Jie Yang +4
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the…