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..
Sample as you Infer: Predictive Coding with Langevin Dynamics
Authors: Umais Zahid, Qinghai Guo, Zafeirios Fountas
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare LPC against VAEs by training generative models on benchmark datasets; our experiments demonstrate superior sample quality and faster convergence for LPC in a fraction of SGD training iterations, while matching or exceeding VAE performance across key metrics like FID, diversity and coverage. |
| Researcher Affiliation | Industry | 1Huawei Technologies R&D, London, UK 2Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China. |
| Pseudocode | Yes | Our final preconditioned algorithm with amortised warm-starts is described in Algorithm 1. Algorithm 1 Preconditioned Langevin PC with Amortized Warm-Starts trained with Jeffrey s Divergence. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | We begin by investigating the performance of our three approximate inference objectives, the forward KL, reverse KL and Jeffrey s divergence on the quality of our samples when trained with CIFAR-10 (Krizhevsky, 2009), SVHN (Netzer et al., 2011) and Celeb A (64x64) (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions using benchmark datasets (CIFAR-10, SVHN, Celeb A) and training for a certain number of epochs, but it does not explicitly state the specific training, validation, and test dataset splits (e.g., percentages or sample counts) used for reproducibility. |
| Hardware Specification | Yes | Batch times and end-to-end slowdowns for LPC algorithms as recorded on a single GPU, equipped with 24GB of GDDR6X memory, providing approximately 83 tera FLOPS. |
| Software Dependencies | No | The paper mentions 'Optimizer Adam' in Table 3 but does not provide specific version numbers for Adam or any other software components (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | Default hyperparameters used in experiments unless explicitly stated. Note: some of these are varied as part of ablation tests, see main text for more details. Optimizer Adam, Learning Rate (α) 1e-3, Batch size 64, Output Likelihood Discretised Gaussian, Max Sampling Steps (T) 300, Preconditioning Decay Rate (β) 0.99. Optimal learning rates for VAE were found to be 1e-3, 8e-4 and 1e-3 for CIFAR10, Celeb A and SVHN respectively. For LPC, optimal inference learning rates were found to be 1e-1, 1e-1, and 1e-3 with β equal to 0.25, 0.25 and 0 (No preconditioning), for CIFAR10, Celeb A and SVHN respectively. |