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eess.SY2025
Revisiting Deep AC-OPF
Oluwatomisin I. Dada, Neil D. Lawrence
Recent work has proposed machine learning (ML) approaches as fast surrogates for solving AC optimal power flow (AC-OPF), with claims of significant speed-ups and high accuracy. In…
cs.LG2025
Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements
Aditya Ravuri, Neil D. Lawrence
We propose a probabilistic interpretation of transformers as unrolled inference steps assuming a probabilistic Laplacian Eigenmaps model from the ProbDR framework. Our derivation s…
cs.LG2025
Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout
Aditya Ravuri, Neil D. Lawrence
Protein Language Models (PLMs) such as ESM2 have been shown to be capable of zero-shot prediction of critical scalar properties of proteins (fitness). In this work, we show that in…