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..
Unbiased learning of deep generative models with structured discrete representations
Authors: Henry C Bendekgey, Gabe Hope, Erik Sudderth
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare models via their test likelihoods, the quality of generated data, and the quality of interpolations. We consider joint positions from human motion capture data (MOCAP [10, 26]) and audio spectrograms from recordings of people reading Wall Street Journal headlines (WSJ0 [19]); see Table 2. |
| Researcher Affiliation | Academia | Harry Bendekgey, Gabriel Hope, Erik B. Sudderth EMAIL Department of Computer Science, University of California, Irvine |
| Pseudocode | Yes | Algorithm 1 Structured Mean Field Variational Inference Require: graphical model potentials η, network potentials λ = encode(x; ϕ). (µ1, . . . , µM) init_expected_stats(η, λ) while not converged do for m 1 to M do ωm MF(µ m; η) µm BP(ωm; η, λ) return ω = concat(ω1, . . . , ωM) |
| Open Source Code | Yes | Code can be found at https://github.com/hbendekgey/SVAE-Implicit. All methods were implemented with the JAX library [8]. |
| Open Datasets | Yes | We consider joint positions from human motion capture data (MOCAP [10, 26]) and audio spectrograms from recordings of people reading Wall Street Journal headlines (WSJ0 [19]); see Table 2. |
| Dataset Splits | Yes | In total, our training dataset consisted of 53,443 sequences, our validation set contained 2,752 sequences, and our test set contained 25,893 sequences. |
| Hardware Specification | Yes | The total amount of computation across both datasets amounts to 6 GPU days for A10G GPUs on an EC2 instance. |
| Software Dependencies | No | All methods were implemented with the JAX library [8]. While JAX is mentioned, no specific version number for JAX or other software dependencies (e.g., Python, PyTorch/TensorFlow, CUDA) is provided. |
| Experiment Setup | Yes | All experiments use latent space with dimension D = 16. We train using the Adam [31] optimizer for neural network parameters, and stochastic natural gradient descent for graphical model parameters, with a batch size of B = 128 and learning rate 10 3 (the transformer DVAE uses learning rate 10 4, which improved performance). We train all methods for 200 epochs on MOCAP and 100 epochs on WSJ0 (including VAE-pre-training for 10 epochs for SVAE/SIN methods), which we found was sufficient for convergence. |