I am a Postdoctoral Machine Learning Researcher at the University of Edinburgh, co-supervised by Antonio Vergari and Edoardo Ponti. My research focusses on making AI more reliable, interpretable and efficient by understanding and exploiting geometric constraints that arise in neural network models.
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More specifically, I study the output layer of deep neural networks that have a large number of outputs, e.g. Large Language Models (LLMs). Any such output layer that has more outputs than inputs unavoidably has outputs that are impossible to predict. I call such outputs unargmaxable. My research identifies unargmaxable outputs in LLMs and Clinical NLP models and proposes replacement layers which guarantee that outputs of interest can be predicted.
More generally, I have a soft spot for language and structure and I enjoy creating interactive visualisations of geometric representations I am learning about; here are some examples. I completed my PhD in 2024, supervised by Adam Lopez and Antonio Vergari. During my PhD, I was also a part-time Research Assistant on Information Extraction from news articles and clinical text under the supervision of Beatrice Alex.Selected publications
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Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. Andreas Grivas, Antonio Vergari and Adam Lopez. Accepted at AAAI 2024.
See also: , Poster, Interactive visualisation (i), Interactive visualisation (ii)
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice. Andreas Grivas, Nikolay Bogoychev and Adam Lopez. ACL 2022 (Oral Presentation)
See also: , Poster, Interactive visualisation
Not a cute stroke: Analysis of Rule-and Neural Network-based Information Extraction Systems for Brain Radiology Reports. Andreas Grivas, Beatrice Alex, Claire Grover, Richard Tobin and William Whiteley. LOUHI 2020
See also:
What do Character-level Models Learn About Morphology? The Case of Dependency Parsing. Clara Vania, Andreas Grivas, and Adam Lopez. EMNLP 2018
Dissertations
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PhD Thesis, University of Edinburgh (2024)
- My vimrc and other dotfiles can be found here.
- My public key is 24A721BB42D9A790