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Found 10 papers, 1 papers with code
Deep Learning with Parametric Lenses
We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories.
TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision
We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature.
Predicting article quality scores with machine learning: The UK Research Excellence Framework
National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics.
Can REF output quality scores be assigned by AI? Experimental evidence
This document describes strategies for using Artificial Intelligence (AI) to predict some journal article scores in future research assessment exercises.
Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound
We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation.
Categories of Differentiable Polynomial Circuits for Machine Learning
Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms.
Category Theory in Machine Learning
Over the past two decades machine learning has permeated almost every realm of technology.
Categorical Foundations of Gradient-Based Learning
We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories.
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories.
Best Practices for Scientific Computing
Scientists spend an increasing amount of time building and using software.
Mathematical Software Software Engineering
