137 citations · 351 across the 41 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao, Weixin Bu, Shengjun Huang +4
Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the g…
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta +1
A physics-informed neural network (PINN) uses physics-augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensur…
Multi-Party Dual Learning
Maoguo Gong, Yuan Gao, Yu Xie +3
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed partie…
An Improved Transfer Model: Randomized Transferable Machine
Pengfei Wei, Xinghua Qu, Yew Soon Ong +1
Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is \emph{domain…
A Survey on Multi-output Learning
Donna Xu, Yaxin Shi, Ivor W. Tsang +3
Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making…
Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer
Alvin Chan, Lei Ma, Felix Juefei-Xu +3
Deep neural networks (DNN), while becoming the driving force of many novel technology and achieving tremendous success in many cutting-edge applications, are still vulnerable to ad…