Paper: Towards Automated Machine Learning Research
Authors: Shervin Ardeshir
Abstract: This paper explores a top-down approach to automating incremental advances inmachine learning research through component-level innovation, facilitated byLarge Language Models (LLMs). Our framework systematically generates novelcomponents, validates their feasibility, and evaluates their performanceagainst existing baselines. A key distinction of this approach lies in howthese novel components are generated. Unlike traditional AutoML and NASmethods, which often rely on a bottom-up combinatorial search over predefined,hardcoded base components, our method leverages the cross-domain knowledgeembedded in LLMs to propose new components that may not be confined to anyhard-coded predefined set. By incorporating a reward model to prioritizepromising hypotheses, we aim to improve the efficiency of the hypothesisgeneration and evaluation process. We hope this approach offers a new avenuefor exploration and contributes to the ongoing dialogue in the field.
Link: https://arxiv.org/abs/2409.05258
Reasoning: Reasoning: Let's think step by step in order to produce the answer. We start by examining the title and abstract for any mention of language models. The title "Towards Automated Machine Learning Research" does not explicitly mention language models. However, the abstract mentions "Large Language Models (LLMs)" and describes a framework that leverages these models to generate novel components for machine learning research. This indicates that the paper involves the use of language models as a significant part of its methodology.
Paper: Towards Automated Machine Learning Research
Authors: Shervin Ardeshir
Abstract: This paper explores a top-down approach to automating incremental advances inmachine learning research through component-level innovation, facilitated byLarge Language Models (LLMs). Our framework systematically generates novelcomponents, validates their feasibility, and evaluates their performanceagainst existing baselines. A key distinction of this approach lies in howthese novel components are generated. Unlike traditional AutoML and NASmethods, which often rely on a bottom-up combinatorial search over predefined,hardcoded base components, our method leverages the cross-domain knowledgeembedded in LLMs to propose new components that may not be confined to anyhard-coded predefined set. By incorporating a reward model to prioritizepromising hypotheses, we aim to improve the efficiency of the hypothesisgeneration and evaluation process. We hope this approach offers a new avenuefor exploration and contributes to the ongoing dialogue in the field.
Link: https://arxiv.org/abs/2409.05258
Reasoning: Reasoning: Let's think step by step in order to produce the answer. We start by examining the title and abstract for any mention of language models. The title "Towards Automated Machine Learning Research" does not explicitly mention language models. However, the abstract mentions "Large Language Models (LLMs)" and describes a framework that leverages these models to generate novel components for machine learning research. This indicates that the paper involves the use of language models as a significant part of its methodology.