# Mathematical Proofs

14 papers with code • 0 benchmarks • 2 datasets

## Benchmarks

These leaderboards are used to track progress in Mathematical Proofs
## 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

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

Machine learning models have been successfully used in many scientific and engineering fields.