reproducibilityindex.ai

Theoretical guarantees in KL for Diffusion Flow Matching

Authors: Marta Gentiloni Silveri, Alain Durmus, Giovanni Conforti

Reproducibility Variable	Result	LLM Response
Research Type	Theoretical	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Researcher Affiliation	Academia	Marta Gentiloni Silveri École polytechnique Route de Saclay, 91120 Palaiseau, France marta.gentiloni-silveri@polytechnique.edu Giovanni Conforti Università degli Studi di Padova Via Trieste, 63, 35131 Padova, Italia giovanni.conforti@math.unipd.it Alain Durmus École polytechnique Route de Saclay, 91120 Palaiseau, France alain.durmus@polytechnique.edu
Pseudocode	No	The paper describes mathematical models and equations but does not include structured pseudocode or an algorithm block.
Open Source Code	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Open Datasets	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Dataset Splits	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Hardware Specification	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Software Dependencies	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.
Experiment Setup	No	Since this work is of theoretical nature, we do not provide experimental data, hence our answer.