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..
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Authors: Louis Rouillard, Demian Wassermann
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the capability and scalability of our method on simulated data, as well as a challenging high-dimensional brain parcellation experiment. |
| Researcher Affiliation | Academia | Louis Rouillard Universit e Paris-Saclay, Inria, CEA Palaiseau, 91120, France EMAIL Demian Wassermann Universit e Paris-Saclay, Inria, CEA Palaiseau, 91120, France EMAIL |
| Pseudocode | No | The paper does not include a clearly labeled 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | Yes | As part of our submission we release the code associated to our experiments. |
| Open Datasets | Yes | Data is extracted from the Human Connectome Project dataset (Van Essen et al., 2012). |
| Dataset Splits | Yes | To measure the convergence, we compute analytically the KL divergence between the variational posterior and the analytical one (every distribution being a Gaussian), summed for every distribution, and averaged over a validation dataset of size 2000. |
| Hardware Specification | Yes | All experiments were performed on a computational cluster with 16 Intel(R) Xeon(R) CPU E5-2660 v2 @ 2.20GHz (256Mb RAM), 16 AMD EPYC 7742 64-Core Processor (512Mb RAM) CPUs and 1 NVIDIA Quadro RTX 6000 (22Gb), 1 Tesla V100 (32Gb) GPUs. |
| Software Dependencies | No | All methods were implemented in Python. We implemented most methods using Tensorflow Probability (Dillon et al., 2017), and SBI methods using the SBI Python library (Tejero-Cantero et al., 2020). The paper mentions software but lacks specific version numbers for the libraries. |
| Experiment Setup | Yes | ADAVI: NF with 1 Affine block with triangular scale, followed by 1 MAF with [32, 32, 32] units. HE with embedding size 8, 2 modules with 2 ISABs (2 heads, 8 inducing points), 1 PMA (seed size 1), 1 SAB and 1 linear unit each. Minibatch size 32, 32 theta draws per X point (see appendix C.5), Adam (10 3), 40 epochs using a reverse KL loss. |