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..
Scalable Adaptive Computation for Iterative Generation
Authors: Allan Jabri, David J. Fleet, Ting Chen
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with diffusion models show that RINs outperform U-Net architectures for image and video generation, as shown in Figure 1. |
| Researcher Affiliation | Collaboration | 1Google Brain, Toronto. 2Department of EECS, UC Berkeley. Correspondence to: Ting Chen <EMAIL>, Allan Jabri <EMAIL>. |
| Pseudocode | Yes | Algorithm 3 RINs Implementation Pseudo-code. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | For image generation, we mainly use the Image Net dataset (Russakovsky et al., 2015). [...] We also use CIFAR-10 (Krizhevsky et al.) to show the model can be trained with small datasets. [...] For video prediction, we use the Kinetics-600 dataset (Carreira et al., 2018) at 16 × 64 × 64 resolution. |
| Dataset Splits | No | The paper does not explicitly state the training, validation, and test dataset splits, only mentioning evaluation on 50K samples for some metrics. |
| Hardware Specification | Yes | We train most models on 32 TPUv3 chips with a batch size of 1024. Models for 512 × 512 and 1024 × 1024 are trained on 64 TPUv3 chips and 256 TPUv4 chips, respectively. |
| Software Dependencies | No | The paper mentions using 'major deep learning frameworks, such as Tensorflow (Abadi et al., 2016) and Py Torch (Paszke et al., 2019)' but does not specify their version numbers. |
| Experiment Setup | Yes | Table C.2. Training Hyper-parameters. (Updates, Batch Size, LR, LR-decay, Optim β2, Weight Dec., Self-cond. Rate, EMA β) |