paper-with-me

홈 › Papers

Can Neural Networks Learn Symbolic Rewriting?

2019-11-07 · Bartosz Piotrowski, Josef Urban, Chad E. Brown, Cezary Kaliszyk

This work investigates if the current neural architectures are adequate for learning symbolic rewriting. Two kinds of data sets are proposed for this research -- one based on automated proofs and the other being a synthetic set of polynomial terms. The experiments with use of the current neural machine translation models are performed and its results are discussed. Ideas for extending this line of research are proposed, and its relevance is motivated.

📄 PDF Abstract BibTeX arXiv:1911.04873

Code (0)

등록된 구현이 없습니다.

Tasks

Machine TranslationTranslation

Similar Papers 제목 키워드 기반

Learning neuro-symbolic convergent term rewriting systems

2025-07-25 · Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti arxiv

Building neural systems that can learn to execute symbolic algorithms is a challenging open problem in artificial intelligence, especially when aiming for strong generalization and out-of-distribution performance. In thi…

High-performance symbolic-numerics via multiple dispatch

2021-05-09 · Shashi Gowda, Yingbo Ma, Alessandro Cheli, Maja Gwozdz 외

As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the best performance out of their machine wi…

CPUVocal Bursts Intensity Prediction

Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks

2017-04-25 · Cheng-Hao Cai, Dengfeng Ke, Yanyan Xu, Kaile Su

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neur…

SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning

2025-04-14 · Yiting Wang, Wanghao Ye, Ping Guo, Ziyao Wang 외

Optimizing Register Transfer Level (RTL) code is crucial for improving the power, performance, and area (PPA) of digital circuits in the early stages of synthesis. Manual rewriting, guided by synthesis feedback, can yiel…

Large Language ModelRAGRetrieval-augmented Generation

Algebraic Dynamical Systems in Machine Learning

2023-11-06 · Iolo Jones, Jerry Swan, Jeffrey Giansiracusa

We introduce an algebraic analogue of dynamical systems, based on term rewriting. We show that a recursive function applied to the output of an iterated rewriting system defines a formal class of models into which all th…