Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Theoretical guarantees in KL for Diffusion Flow Matching
Authors: Marta Gentiloni Silveri, Alain Durmus, Giovanni Conforti
NeurIPS 2024 | Venue PDF | LLM Run Details
| 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 EMAIL Giovanni Conforti Università degli Studi di Padova Via Trieste, 63, 35131 Padova, Italia EMAIL Alain Durmus École polytechnique Route de Saclay, 91120 Palaiseau, France EMAIL |
| 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. |