Generative Flows with Matrix Exponential
Authors: Changyi Xiao, Ligang Liu
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our model achieves great performance on density estimation amongst generative flows models. We evaluate our MEF model on CIFAR10 (Krizhevsky et al., 2009), Image Net32 and Image Net64 (Van Oord et al., 2016) datasets and compare log-likelihood with other generative flows models. |
| Researcher Affiliation | Academia | Changyi Xiao 1 Ligang Liu 1 University of Science and Technology of China. Correspondence to: Ligang Liu <lgliu@ustc.edu.cn> |
| Pseudocode | Yes | Algorithm 1 Algorithm for computing matrix exponential |
| Open Source Code | Yes | The code for our model is available at https://github.com/changyi7231/MEF. |
| Open Datasets | Yes | We evaluate our MEF model on CIFAR10 (Krizhevsky et al., 2009), Image Net32 and Image Net64 (Van Oord et al., 2016) datasets |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly describe a separate validation set or its split methodology. |
| Hardware Specification | Yes | All models are trained on one TITAN Xp GPU. Our CIFAR10 model takes 1.67 seconds to generate a batch of 64 samples on one NVIDIA 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions optimization methods like Adamax and activation functions like ELU, but does not provide specific version numbers for software dependencies such as Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | We use a level L = 3 and depth D1 = 8, D2 = 4, D3 = 2. Each coupling layer is composed of 8 residual blocks... All models are trained for 50 epochs with batch size 64. We run models on CIFAR10 dataset with learning rate 0.01 and 0.001. |