Mathematical Proofs
14 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Mathematical Proofs
HolStep
NaturalProofs
Most implemented papers
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.
IsarStep: a Benchmark for High-level Mathematical Reasoning
In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.
A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data
Partial Least Squares (PLS) methods have been heavily exploited to analyse the association between two blocs of data.
α-Rank: Multi-Agent Evaluation by Evolution
We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).
Learning to Prove Theorems via Interacting with Proof Assistants
Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.
Epistemic Phase Transitions in Mathematical Proofs
Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record.
Differential Machine Learning
differential-machine-learning/notebooks
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arXiv 2020
It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available.
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
YuntianChen/Hard_constrant_projection_HCP
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Machine learning models have been successfully used in many scientific and engineering fields.
