Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Chong, Kai Fong Ernest"'
Autor:
Li, Jiaxing, Chen, Zihan, Chong, Kai Fong Ernest, Das, Bikramjit, Quek, Tony Q. S., Yang, Howard H.
Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superposition properties of multi-access channels, this approach facilitates a
Externí odkaz:
http://arxiv.org/abs/2409.15100
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously
Externí odkaz:
http://arxiv.org/abs/2401.17124
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-gener
Externí odkaz:
http://arxiv.org/abs/2310.04003
Autor:
Huang, Xia, Chong, Kai Fong Ernest
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) instances. In many recent approaches for mitigating the negative eff
Externí odkaz:
http://arxiv.org/abs/2307.09810
Autor:
Xu, Jingyi, Vaidya, Tushar, Wu, Yufei, Chandra, Saket, Lai, Zhangsheng, Chong, Kai Fong Ernest
We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fund
Externí odkaz:
http://arxiv.org/abs/2303.11730
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 could have label noise, and different clients could have vastly differen
Externí odkaz:
http://arxiv.org/abs/2204.04677
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 clients. However, FL suffers performance degradation when client data distri
Externí odkaz:
http://arxiv.org/abs/2108.05765
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 levels, they encounter significant performance reduction at high noise l
Externí odkaz:
http://arxiv.org/abs/2105.13892
Autor:
Chong, Kai Fong Ernest
The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions $\sigma$, then a standard feedforward neural network with one hidden layer is able to approximate any continuous m
Externí odkaz:
http://arxiv.org/abs/2002.06505
Autor:
Chong, Kai Fong Ernest, Nevo, Eran
We prove several relations on the $f$-vectors and Betti numbers of flag complexes. For every flag complex $\Delta$, we show that there exists a balanced complex with the same $f$-vector as $\Delta$, and whose top-dimensional Betti number is at least
Externí odkaz:
http://arxiv.org/abs/1908.08308