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 | Conference PDF | Archive PDF | Plain Text | 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). |