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Tag: #program-synthesis8 results

SymLang:從噪聲數據發現支配方程

SymLang:從噪聲數據發現支配方程

SymLang 是一個新框架,整合對稱約束文法、語言模型引導的程式合成,以及貝氏模型選擇,從噪聲和部分觀測中發現支配方程。它在 10% 噪聲下,對 133 個動力系統達到 83.7% 精確結構恢復率,優於基準 22.4 個百分點。此開源工具將外推誤差降低 61%,並最小化守恆律違反。

Cadmus: Low-Cost Program Synthesis System

Cadmus: Low-Cost Program Synthesis System

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments bypassing issues with large LLMs like OOD challenges and high resource demands.

Apple Machine LearningOfficialFeb 13#research#apple-ml#cadmus
Cadmus Enables Cheap Program Synthesis Experiments

Cadmus Enables Cheap Program Synthesis Experiments

Apple Machine Learning introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup allows controlled experimentation without the complexities of large LLMs.

Apple Machine LearningOfficialFeb 13#research#apple#cadmus
Cadmus: Cheap Program Synthesis System

Cadmus: Cheap Program Synthesis System

Apple unveils Cadmus, a small-scale system for autoregressive program synthesis. It features an integer VM, diverse program dataset, and transformer model trained under $200 compute. Enables controlled experiments bypassing LLM challenges like OOD and tokenization.

Apple Machine LearningOfficialFeb 13#research#apple-ml#cadmus
Cadmus: Affordable Autoregressive Program Synthesis

Cadmus: Affordable Autoregressive Program Synthesis

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments avoiding LLM pitfalls like OOD issues and high compute demands.

Apple Machine LearningOfficialFeb 13#research#apple-ml#cadmus