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..
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Authors: Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments We evaluate our framework on a range of UCI datasets (Dua & Graff, 2017), datasets of handwritten digits (Pedregosa et al., 2011; Deng, 2012), and real world images, such as CIFAR10 (Krizhevsky et al., 2009) and Celeb-A (Liu et al., 2015). |
| Researcher Affiliation | Academia | 1Computer and Information Science, Cornell University, Ithaca, NY, USA 2Machine Learning Department, Carnegie-Mellon University, Pittsburgh, PA, USA 3Tsinghua University, Beijing, China 4Department of Computer Science, Cornell Tech, NYC, NY, USA. |
| Pseudocode | Yes | F. Pseudocode for Semi-Autoregressive Flows In this section, we provide the algorithms for training (Algorithm 1) and sampling from (Algorithm 2) SAEFs. |
| Open Source Code | Yes | 1 Code is available at https://github.com/ps789/SAEF. |
| Open Datasets | Yes | We evaluate our framework on a range of UCI datasets (Dua & Graff, 2017), datasets of handwritten digits (Pedregosa et al., 2011; Deng, 2012), and real world images, such as CIFAR10 (Krizhevsky et al., 2009) and Celeb-A (Liu et al., 2015). |
| Dataset Splits | Yes | We split the original 10,000 test samples into 8,000 training data and 2,000 evaluation data for the logistic model. |
| Hardware Specification | No | Glow was trained with 3 levels and a depth of 1 on 8 GPUs for 250 epochs. No specific GPU model, CPU, or other hardware details are provided beyond the number of GPUs. |
| Software Dependencies | No | The paper mentions software like Keras and scikit-learn in the references and generally refers to optimizers like ADAM, but it does not specify version numbers for any software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | All models were trained with a batch size of 200 and learning rate of 1e 3 using the ADAM optimizer (Kingma & Ba, 2014) for 200 epochs for the smaller datasets (Miniboone, Hepmass) and 20 epochs for the larger ones (Gas, Power, BSDS 300). |