Unargmaxable
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About
Unargmaxable is a research focus led by Andreas Grivas, a Postdoctoral Machine Learning Researcher at the University of Edinburgh. The core of this research aims to enhance AI reliability, interpretability, and efficiency by exploring geometric constraints within neural network models. Specifically, it investigates the output layers of deep neural networks, particularly Large Language Models (LLMs), which often produce a large number of outputs. The research identifies that when the output layer has more outputs than inputs, certain outputs become inherently impossible to predict—termed 'unargmaxable outputs.' This phenomenon has been identified in both LLMs and Clinical NLP models. The research not only identifies these unargmaxable outputs but also proposes innovative replacement layers designed to ensure that specific outputs of interest can always be predicted, thereby improving the robustness and predictability of AI systems. The work includes interactive visualizations of geometric representations and is supported by several selected publications and dissertations.
Platform
Task
Features
• research on ai interpretability
• research on ai reliability
• interactive visualizations of geometric representations
• proposal of replacement layers for predictable ai outputs
• identification of unargmaxable outputs in clinical nlp models
• identification of unargmaxable outputs in llms
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