10 citations · 27 across the 6 of their papers we have counts for
8 papers
GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence
Xia Huang, Kai Fong Ernest Chong
Web image datasets curated online inherently contain ambiguous in-distribution (ID) instances and out-of-distribution (OOD) instances, which we collectively call non-conforming (NC…
Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices
Jingyi Xu, Tushar Vaidya, Yufei Wu +3
We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult proce…
FedCorr: Multi-Stage Federated Learning for Label Noise Correction
Jingyi Xu, Zihan Chen, Tony Q. S. Quek +1
Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data co…
Dynamic Attention-based Communication-Efficient Federated Learning
Zihan Chen, Kai Fong Ernest Chong, Tony Q. S. Quek
Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the client…
Training Classifiers that are Universally Robust to All Label Noise Levels
Jingyi Xu, Tony Q. S. Quek, Kai Fong Ernest Chong
For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise le…
A closer look at the approximation capabilities of neural networks
Kai Fong Ernest Chong
The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions , then a standard feedforward neural netw…