Kimberly T Mai

About Kimberly T Mai

Kimberly T Mai, With an exceptional h-index of 3 and a recent h-index of 3 (since 2020), a distinguished researcher at University College London, specializes in the field of Machine Learning, Anomaly Detection.

His recent articles reflect a diverse array of research interests and contributions to the field:

Understanding the limitations of self-supervised learning for tabular anomaly detection

Warning: humans cannot reliably detect speech deepfakes

Large language models respond to influence like humans

Susceptibility to influence of large language models

Explaining the decisions of anomalous sound detectors

Self-supervised losses for one-class textual anomaly detection

Brittle Features May Help Anomaly Detection

Transfer learning from audio deep learning models for micro-Doppler activity recognition

Kimberly T Mai Information

University

Position

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Citations(all)

23

Citations(since 2020)

23

Cited By

0

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

0

i10Index(since 2020)

0

Email

University Profile Page

Google Scholar

Kimberly T Mai Skills & Research Interests

Machine Learning

Anomaly Detection

Top articles of Kimberly T Mai

Understanding the limitations of self-supervised learning for tabular anomaly detection

Pattern Analysis and Applications

2024/3/12

Warning: humans cannot reliably detect speech deepfakes

Plos one

2023/8/2

Large language models respond to influence like humans

2023/7

Susceptibility to influence of large language models

arXiv preprint arXiv:2303.06074

2023/3/10

Explaining the decisions of anomalous sound detectors

2022/11/3

Self-supervised losses for one-class textual anomaly detection

arXiv preprint arXiv:2204.05695

2022/4/12

Brittle Features May Help Anomaly Detection

2021/4/21

Transfer learning from audio deep learning models for micro-Doppler activity recognition

2020/4/28

See List of Professors in Kimberly T Mai University(University College London)

Co-Authors

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