Feb 28, 2023 · Title:EvoPrompting: Language Models for Code-Level Neural Architecture Search. Authors:Angelica Chen, David M. Dohan, David R. So. View a PDF ...
We first demonstrate that EVOPROMPTING is effective on the computationally efficient MNIST-1D dataset, where EVOPROMPTING produces convolutional architecture ...
Feb 16, 2024 · The paper explores prompt tuning for neural architecture search. While language models can produce code snippets with prompting, it is often quite difficult for ...
This project is based on the paper: EvoPrompting: Language Models for Code-Level Neural Architecture Search by Angelica Chen, David M. Dohan, and David R ...
Nov 16, 2023 · There, evolution has long been used to search over discrete spaces to efficiently discover improved deep learning architectures (Yao, 1999; Real ...
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May 30, 2024 · We first demonstrate that EVOPROMPTING is effective on the computationally efficient MNIST-1D dataset, where EVOPROMPTING produces convolutional ...
EvoPrompting is successful at designing accurate and efficient neural network architectures across a variety of machine learning tasks, while also being ...
Mar 1, 2023 · This paper proposes a new method to train a LMs which can be used to design novel model architectures. EvoPrompting evolves few-shot prompts to ...
Feb 28, 2023 · EVOPROMPTING, which combines evolutionary search with soft-prompt tuning, discovers smaller and better performing architectures on MNIST1D than ...
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Mar 1, 2023 · EvoPrompting: Language Models for Code-Level Neural Architecture Search. EvoPrompting enables LMs to create novel and effective deep neural ...
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