TNLearn: An Open Source Python Package for Task-based Neurons

📅 2026-09-23
📈 Citations: 0
✨ Influential: 0
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🤖 AI Summary
为解决特定任务需要定制化神经元的问题,TNLearn提供了一个开源Python包,用于自动构建基于任务的神经元和网络。
📝 Abstract
The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customized neurons, as task-based neurons capture useful prior knowledge from task-related data. To facilitate the use of task-based neurons in scientific research and industrial applications, we introduce TNLearn, an open-source Python package that provides automated construction of task-based neurons and networks, enabling smooth training of task-based networks. Comprehensive documentation, including technical exposition, API reference, and representative examples, is available online. TNLearn is open-sourced at https://github.com/NewT123-WM/tnlearn and has become a PyTorch ecosystem project.
Problem

Research questions and friction points this paper is trying to address.

task-based neurons
neural networks
customized neurons
Innovation

Methods, ideas, or system contributions that make the work stand out.

task-based neurons
automated construction
open-source Python package
M
Meng Wang
Shenzhen H&T Intelligent Control Co., Ltd., Shenzhen, China
T
Tieyun Li
School of Mathematics, Harbin Institute of Technology, Harbin, China
Juntong Fan
Juntong Fan
Chinese University of Hong Kong
Model Compression
Hanyu Pei
Hanyu Pei
University of Louisville
deep learning
J
Jing-Xiao Liao
Department of Data Science, The City University of Hong Kong, Kowloon, Hong Kong
Yaodong Yang
Yaodong Yang
Boya (博雅) Assistant Professor at Peking University
Reinforcement LearningAI AlignmentEmbodied AI
Jianwei Ma
Jianwei Ma
Professor, School of Earth and Space Sciences, Peking University; Harbin Institute of Technology
Seismic explorationArtificial IntelligenceCompressed sensingSparse transformsApplied Mathematics
F
Fenglei Fan
Department of Data Science, The City University of Hong Kong, Kowloon, Hong Kong