ArticleJournal of scientific computing2025
Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks.
Article in Journal of scientific computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction-Diffusion Dynamics.Journal of computational physics · 2025Article
- Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations.Journal of scientific computing · 2025Article
- Learn Singularly Perturbed Solutions via Homotopy Dynamics.Proceedings of machine learning research · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we present a novel training approach called the Homotopy Relaxation Training Algorithm (HRTA), aimed at accelerating the training process in contrast to traditional methods. Our algorithm incorporates two key mechanisms: one involves building a homotopy activation function that seamlessly connects the linear activation function with the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.