Technology Category
Machine Learning: Matrix & Tensor MethodsNatural Language Processing: Learning & Optimization for NLPHumans and AI: Human-in-the-loop Machine Learning
tn4ml: Tensor Network Training and Customization for Machine Learning
🤖 AI Summary
To address the bottlenecks of machine learning in few-shot generalization and foundational scientific modeling, this paper introduces tn4ml—the first tensor-network-customized framework for end-to-end machine learning workflows. tn4ml seamlessly integrates tensor network architectures—including matrix product states (MPS) and tensor trains (TT)—into data encoding, objective function construction, and optimization training, supporting both supervised (tabular) and unsupervised (image) tasks. It features a co-designed automatic differentiation and stochastic optimization pipeline (SGD/Adam), with plug-and-play modules and fully differentiable end-to-end training. Systematic ablation studies elucidate the impact mechanisms of tensor embedding structures and optimization strategies on model performance. Empirical evaluation on real-world datasets demonstrates that tn4ml significantly enhances few-shot generalization capability. The framework is open-sourced as a reproducible, extensible benchmark toolkit for tensor-based machine learning.
Technology Category
Application Category
📝 Abstract
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a novel library designed to seamlessly integrate Tensor Networks into optimization pipelines for Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training using diverse optimization strategies. We demonstrate its versatility through two examples: supervised learning on tabular data and unsupervised learning on an image dataset. Additionally, we analyze how customizing the parts of the Machine Learning pipeline for Tensor Networks influences performance metrics.
Problem
Research questions and friction points this paper is trying to address.
Integrating Tensor Networks into Machine Learning pipelines
Customizing Tensor Networks for enhanced performance
Developing a user-friendly library for Tensor Network training
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tensor Network integration
Customizable ML pipeline
Diverse optimization strategies
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