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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Authors: Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present experiments that verify that minimizing the proposed LRMF objective (3) with Gaussian, Real NVP, and FFJORD density estimators does indeed result in dataset alignment. |
| Researcher Affiliation | Collaboration | Ben Usman 1,2 EMAIL Avneesh Sud 2 EMAIL Nick Dufour 2 EMAIL Kate Saenko 1,3 EMAIL Boston University 1 Google AI 2 MIT-IBM Watson AI Lab 3 |
| Pseudocode | No | The paper presents mathematical definitions and derivations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide Jupyter notebooks with code in JAX [6] and Tensor Flow Probability (TFP) [7]. |
| Open Datasets | Yes | We also trained a Real NVP LRMF to map latent codes of USPS digits to latent codes of MNIST. |
| Dataset Splits | No | This enables automatic model validation and hyperparameter tuning on the held-out set. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or cloud instance specifications) used for running the experiments. |
| Software Dependencies | No | We provide Jupyter notebooks with code in JAX [6] and Tensor Flow Probability (TFP) [7]. |
| Experiment Setup | No | We used original hyperparameters and network architectures from Real NVP [8] and FFJORD [11], the exact values are given in the supplementary. |