Amortised Learning by Wake-Sleep
Authors: Li Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ALWS on a wide range of generative models. Details for each experiment can be found in Appendix C. The results are shown in Figure 8. According to FID, ALWS-A is the best ML method for binarised MNIST, Fashion, and CIFAR-10. |
| Researcher Affiliation | Academia | 1Gatsby Computational Neuroscience Unit. Correspondence to: Li K. Wenliang <kevinli@gatsby.ucl.ac.uk>. |
| Pseudocode | Yes | Algorithm 1: Amortised learning by wake sleep |
| Open Source Code | Yes | Code is at github.com/kevin-w-li/al-ws |
| Open Datasets | Yes | We chose six benchmark datasets: the binarised and original MNIST (Le Cun et al., 1998) (B-MNIST and MNIST, respectively), fashion MNIST (Fashion) (Xiao et al., 2017), natural images (Natural) (Hateren & Schaaf, 1998), CIFAR10 (Krizhevsky et al., 2009) and Celeb A (Liu et al., 2015). |
| Dataset Splits | No | The paper uses standard benchmark datasets like MNIST, CIFAR-10, etc., but does not explicitly state the train/validation/test splits (e.g., percentages or sample counts) used for its experiments, nor does it cite a specific methodology for splitting. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions various algorithms and frameworks (e.g., DCGAN) but does not provide specific software version numbers for key dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For ALWS, we used a Gaussian kernel with a bandwidth equal to the median distance between samples generated for each b, and set λ = 0.01. Each algorithm is run for 50 epochs 10 times with different initialisations, except for SIVI where we trained for 1000 epochs with a lower learning rate for stability. |