Summary of Tngps: Discovering Unknown Tensor Network Structure Search Algorithms Via Large Language Models (llms), by Junhua Zeng et al.
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
by Junhua Zeng, Chao Li, Zhun Sun, Qibin Zhao, Guoxu Zhou
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper proposes a novel approach to automatically discovering tensor network structure search (TN-SS) algorithms, replacing manual heuristics with poor performance. By modeling human experts’ workflows and proposing an automatic algorithm discovery framework called tnGPS, the authors leverage large language models (LLMs) to generate new TN-SS algorithms through iterative refinement. The experimental results demonstrate that these discovered algorithms outperform current state-of-the-art methods in benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how computers can find new ways to solve a tricky problem called tensor network structure search. Right now, people have to come up with ideas for solving this problem by hand, which isn’t very efficient or accurate. The researchers want to change that by using special computer models to help generate new solutions. They made a program that works like a human expert would, and it did a better job than the current best methods in tests. |
