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..
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
Authors: Henry Li, Ronen Basri, Yuval Kluger
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our multi-scale likelihood model on a selection of datasets and tasks including density estimation, lossless compression, and out-of-distribution detection and observe significant improvements to the existing state-of-the-art, demonstrating the power behind a multi-scale prior for likelihood modeling. ... We evaluate both the the Laplacian pyramid-based and wavelet-based variants of our proposed probabilistic cascading diffusion model (LP-PCDM and W-PCDM, respectively) in several settings. |
| Researcher Affiliation | Collaboration | 1Yale University, 2Meta AI, 3Weizmann Institute of Science EMAIL EMAIL |
| Pseudocode | No | The paper includes mathematical formulations and derivations but does not present any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code can be found at this https url. |
| Open Datasets | Yes | First, we begin on a general density estimation task on the CIFAR10 (Krizhevsky et al., 2009) and Image Net 32, 64, and 128 (Van Den Oord et al., 2016) datasets. |
| Dataset Splits | No | The paper mentions training and testing on datasets like CIFAR10 and ImageNet, and refers to a 'test set', but it does not explicitly specify the training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit references to predefined splits for reproducibility). |
| Hardware Specification | Yes | All training is performed on 8x NVIDIA RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions specific software components like 'Adam W' and refers to prior work for architectural details ('VDM U-Net implementation in (Kingma et al., 2021)'), but it does not provide specific version numbers for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | We construct our cascaded diffusion models with antithetic time sampling and a learnable noise schedule as in (Kingma et al., 2021). ... For CIFAR10, we use two scales... We use a U-Net of depth 32, consisting of 32 residual blocks in the forward and reverse directions, respectively. ... We train with Adam W for 2 million updates. |