16 citations · 68 across the 10 of their papers we have counts for
10 papers
Pretraining ECG Data with Adversarial Masking Improves Model Generalizability for Data-Scarce Tasks
Jessica Y. Bo, Hen-Wei Huang, Alvin Chan +1
Medical datasets often face the problem of data scarcity, as ground truth labels must be generated by medical professionals. One mitigation strategy is to pretrain deep learning mo…
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?
Alvin Chan, Yew-Soon Ong, Clement Tan
Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model ov…
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
Zhenghua Chen, Min Wu, Alvin Chan +2
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for…
ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning
Bill Tuck Weng Pung, Alvin Chan
The ability to reason with multiple hierarchical structures is an attractive and desirable property of sequential inductive biases for natural language processing. Do the state-of-…
Deep Extrapolation for Attribute-Enhanced Generation
Alvin Chan, Ali Madani, Ben Krause +1
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequen…
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
Alvin Chan, Yi Tay, Yew-Soon Ong +1
This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a 'backdoor poisoning' attack on NLP…
Jacobian Adversarially Regularized Networks for Robustness
Alvin Chan, Yi Tay, Yew Soon Ong +1
Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the…
What it Thinks is Important is Important: Robustness Transfers through Input Gradients
Alvin Chan, Yi Tay, Yew-Soon Ong
Adversarial perturbations are imperceptible changes to input pixels that can change the prediction of deep learning models. Learned weights of models robust to such perturbations a…
Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks
Alvin Chan, Yew-Soon Ong
Deep learning models have recently shown to be vulnerable to backdoor poisoning, an insidious attack where the victim model predicts clean images correctly but classifies the same…
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…