Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Liu, Hongfu"'
Autor:
Xiao, Wenxiao, Liu, Hongfu
Active learning strategically selects informative unlabeled data points and queries their ground truth labels for model training. The prevailing assumption underlying this machine learning paradigm is that acquiring these ground truth labels will opt
Externí odkaz:
http://arxiv.org/abs/2405.17627
Shapley value-based data valuation methods, originating from cooperative game theory, quantify the usefulness of each individual sample by considering its contribution to all possible training subsets. Despite their extensive applications, these meth
Externí odkaz:
http://arxiv.org/abs/2405.17489
Influence functions serve as crucial tools for assessing sample influence in model interpretation, subset training set selection, noisy label detection, and more. By employing the first-order Taylor extension, influence functions can estimate sample
Externí odkaz:
http://arxiv.org/abs/2405.17490
Influence functions offer a robust framework for assessing the impact of each training data sample on model predictions, serving as a prominent tool in data-centric learning. Despite their widespread use in various tasks, the strong convexity assumpt
Externí odkaz:
http://arxiv.org/abs/2405.03869
Test-Time Adaptation (TTA) is a critical paradigm for tackling distribution shifts during inference, especially in visual recognition tasks. However, while acoustic models face similar challenges due to distribution shifts in test-time speech, TTA te
Externí odkaz:
http://arxiv.org/abs/2310.09505
Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typically consider node balance, a notion pursuing a proportionally balance
Externí odkaz:
http://arxiv.org/abs/2306.10123
Automatic Pronunciation Assessment (APA) is vital for computer-assisted language learning. Prior methods rely on annotated speech-text data to train Automatic Speech Recognition (ASR) models or speech-score data to train regression models. In this wo
Externí odkaz:
http://arxiv.org/abs/2305.19563
Many research efforts have been committed to unsupervised domain adaptation (DA) problems that transfer knowledge learned from a labeled source domain to an unlabeled target domain. Various DA methods have achieved remarkable results recently in term
Externí odkaz:
http://arxiv.org/abs/2303.02302
Autor:
Li, Zhaonan, Liu, Hongfu
Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two mainstream settings that greatly extend conventional visual object recognition. However, the limitations of their problem settings are not negligible. The novel categories i
Externí odkaz:
http://arxiv.org/abs/2302.04427
Fairness is essential for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, deriving from a consensus that `similar individuals should be treated similarly,' is a vital notion to describe f
Externí odkaz:
http://arxiv.org/abs/2211.15897