Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
Authors: Daniela de Albuquerque, John Pearson
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that inflationary flows indeed preserve local neighborhood structure, allowing for sampling-based uncertainty estimation, and that these models continue to provide high-quality generation under compression, even from latent spaces reduced to as little as 0.03% of the nominal data dimensionality. |
| Researcher Affiliation | Academia | Daniela de Albuquerque Department of Electrical & Computer Engineering School of Medicine Duke University Durham, NC 27708 daniela.de.albuquerque@duke.edu John Pearson Department of Neurobiology Department of Electrical & Computer Engineering Center for Cognitive Neuroscience Duke University Durham, NC 27708 john.pearson@duke.edu |
| Pseudocode | Yes | To integrate (83), we utilize either Euler s method for toy datasets and Heun s method (see Algorithm 1) for high-dimensional image datasets. |
| Open Source Code | Yes | Additionally, we provide entire code needed to reproduce results of paper in this repository [63]. |
| Open Datasets | Yes | We performed two sets of experiments on two benchmark image datasets (CIFAR-10 [61] and AFHQv2 [62]...) ... CIFAR-10 [61]: MIT license AFHQv2 [62]: Creative Commons BY-NC-SA 4.0 license Toys [93]: BSD License. The NeurIPS checklist also explicitly states: "All datasets utilized are publicly available and we provide details on how to download and pre-process these data in our repository and in the appendices." |
| Dataset Splits | Yes | To assess how these two factors affect model performance, we performed two sets of experiments on two benchmark image datasets (CIFAR-10 [61] and AFHQv2 [62]...)... In Tables 1 and 2 we showcase Frechet Inception Distance (FID) scores [65] (mean 2σ over 3 independently generated sets of images, each with 50,000 samples) and round-trip integration mean squared errors (mean MSE 2σ over 3 randomly sampled sets of images, each with 10,000 samples) for each (d, IG) combination explored (Appendices B.5.1, B.5.2, B.5.4). From NeurIPS checklist: "Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [Yes]" |
| Hardware Specification | Yes | training was performed in a distributed fashion using either 8 or 4 GPUs per each experiment (NVIDIA Ge Force GTX TITAN X, RTX 2080) in a compute cluster setting. Generation (FID) and round-trip (MSE) experiments were performed on single GPU (NVIDIA RTX 3090, 4090, A5000, A6000). |
| Software Dependencies | No | The paper mentions utilizing 'DDPM++ architecture, as implemented in [49]' and 'Toy datasets were obtained from scikit-learn [93]', but it does not provide explicit version numbers for general software dependencies like Python, PyTorch, or specific libraries used beyond these mentions. |
| Experiment Setup | Yes | For all cases, we used a learning rate of 10 5, batch size of 8192, and exponential moving average half-life of 50 104. ... Shown in Tables 8, 9 are our specific choices for the exponential inflation constant (ρ) and training duration (in 106 images Mimgs)... Table 7: CIFAR-10 & AFHQv2 Common Training Hyperparameters (Across All Schedules) Hyperparameter Name Hyperparameter Value Channel multiplier 128 Channels per resolution 2-2-2 Dataset x-flips No Augment Probability 12% Dropout Probability 13% Learning rate 10 4 LR Ramp-Up (Mimg) 10 EMA Half-Life (Mimg) 0.5 Batch-Size 512 |