Mathematical Proofs

10 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

MIMBCD-UI/prototype-multi-modality 7 Apr 2020

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

anuwu/PSO-Grad 19 May 2020

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.

A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data

matt-sutton/bigsgPLS 23 Feb 2017

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

deepmind/open_spiel 4 Mar 2019

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

princeton-vl/CoqGym 21 May 2019

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

scottviteri/ManipulateProofTrees 31 Mar 2020

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 arXiv 2020

It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available.

IsarStep: a Benchmark for High-level Mathematical Reasoning

reactive-systems/ml2 ICLR 2021

In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.

Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method

YuntianChen/Hard_constrant_projection_HCP 11 Dec 2020

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

Formal Development of Safe Automated Driving using Differential Dynamic Logic

yuvrajselvam/safe-ad-dl 14 Apr 2022

The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment.