Summary of Lemo-nade: Multi-parameter Neural Architecture Discovery with Llms, by Md Hafizur Rahman and Prabuddha Chakraborty
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs
by Md Hafizur Rahman, Prabuddha Chakraborty
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel framework for automatically discovering efficient neural network architectures is introduced, capable of considering various edge device-specific parameters without requiring prior expertise in AI. LeMo-NADe leverages large language models (LLMs) and expert systems to search for optimal architectures based on user-defined criteria. The proposed framework is validated using popular datasets like CIFAR-10, CIFAR-100, and ImageNet16-120, achieving impressive performance across diverse application settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to design neural networks that uses artificial intelligence (AI) and expert knowledge. This method helps find the best network for specific devices, taking into account factors like how much power it uses and how fast it can process information. The system is designed to be used by people without extensive AI training, making it more accessible. They tested this approach on various datasets and found that it works well in different situations. |
Keywords
* Artificial intelligence * Neural network
