Lotte Weerts
Lotte Weerts is a former member of the Neural Reckoning group. This page is no longer regularly updated.
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- Lab member: 2016-2021
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PhD thesis
Features of hearing: applications of machine learning to uncover the building blocks of hearing
Lotte Weerts was a PhD student in the Neurotechnology CDT, working on a combined information theory and machine learning approach to understanding the auditory system. She was jointly supervised by Claudia Clopath. After her PhD she moved to DeepMind.
Software
Videos
Publications
Note that only publications as part of the Neural Reckoning group are included here (see external publications below for full list).
2022
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Weerts L, Rosen S, Clopath C, Goodman DFM
The Psychometrics of Automatic Speech Recognition.
Preprint
2021
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Weerts L
(2021)
Features of hearing: applications of machine learning to uncover the building blocks of hearing.
PhD thesis, Imperial College London
2019
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Weerts L, Clopath C, Goodman DFM
(2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features.
Cognitive Computational Neuroscience
External publications
This is a short preview of the publications from other sources (ORCID, Semantic Scholar). Note that publications from work done outside the Neural Reckoning group are included in this list.
2025
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Gheorghe Comanici, et al. (2025)
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
arXiv.org
2023
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Caglar Gulcehre, et al. (2023)
Reinforced Self-Training (ReST) for Language Modeling
arXiv.org
2021
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L. Weerts, S. Rosen, C. Clopath, Dan F. M. Goodman (2021)
The Psychometrics of Automatic Speech Recognition
bioRxiv
2019
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L. Weerts, C. Clopath, Dan F. M. Goodman (2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features
2019 Conference on Cognitive Computational Neuroscience
2015
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L. Weerts (2015)
A computational approach towards the ontogeny of mirror neurons via Hebbian learning -
L. Weerts (2015)
A computational account for the ontogeny of mirror neurons via Hebbian learning