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..
Self-Adversarially Learned Bayesian Sampling
Authors: Yang Zhao, Jianyi Zhang, Changyou Chen5893-5900
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real datasets verify advantages of our framework, outperforming related methods in terms of both sampling efficiency and sample quality. |
| Researcher Affiliation | Academia | Yang Zhao State University of New York at Buffalo EMAIL Jianyi Zhang Fudan University EMAIL Changyou Chen State University of New York at Buffalo EMAIL |
| Pseudocode | Yes | Algorithm 1: SAL-MC training and sampling |
| Open Source Code | No | The paper states "detailed algorithm given in the Supplementary Material (SM) on our homepage" but does not provide a concrete link to source code for the described methodology. |
| Open Datasets | Yes | Experiments on both synthetic and real datasets... for image synthesis on MNIST and Celeb A datasets... We further compare SAL-MC with A-NICE-MC on several BLR tasks... Three datasets, Heart (532-13), Australian (690-14) and German (1000-24), are used... |
| Dataset Splits | No | The paper states "The models are trained on a random 80% of the datasets and tested on the remaining 20% in each run" but does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | As suggested by (Welling and Teh 2011), a polynomially-decayed step size ϵt = a/(t + 1)0.55 is used in SGLD for a fair comparison... The mini-batch size for training is 64; and the injected noise ξ is drawn from N(0, I) for all tasks. |