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Enhancing Neural Theorem Proving through Data Augmentation and Dynamic
  Sampling Method

Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method

20 December 2023
Rahul Vishwakarma
Subhankar Mishra
    AIMat
ArXivPDFHTML

Papers citing "Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method"

5 / 5 papers shown
Title
A Survey on Deep Learning for Theorem Proving
A Survey on Deep Learning for Theorem Proving
Zhaoyu Li
Jialiang Sun
Logan Murphy
Qidong Su
Zenan Li
Xian Zhang
Kaiyu Yang
Xujie Si
LRM
42
21
0
15 Apr 2024
Baldur: Whole-Proof Generation and Repair with Large Language Models
Baldur: Whole-Proof Generation and Repair with Large Language Models
E. First
M. Rabe
Talia Ringer
Yuriy Brun
59
92
0
08 Mar 2023
Magnushammer: A Transformer-Based Approach to Premise Selection
Magnushammer: A Transformer-Based Approach to Premise Selection
Maciej Mikuła
Szymon Tworkowski
Szymon Antoniak
Bartosz Piotrowski
Albert Qiaochu Jiang
Jinyi Zhou
Christian Szegedy
Lukasz Kuciñski
Piotr Milo's
Yuhuai Wu
39
42
0
08 Mar 2023
Formal Mathematics Statement Curriculum Learning
Formal Mathematics Statement Curriculum Learning
Stanislas Polu
Jesse Michael Han
Kunhao Zheng
Mantas Baksys
Igor Babuschkin
Ilya Sutskever
AIMat
73
115
0
03 Feb 2022
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Yuhuai Wu
M. Rabe
Wenda Li
Jimmy Ba
Roger C. Grosse
Christian Szegedy
AIMat
LRM
61
51
0
15 Jan 2021
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