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-Implicit Variational Inference via Score Matching
Authors: Longlin Yu, Cheng Zhang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare SIVI-SM to ELBO-based methods including the original SIVI and UIVI on a range of inference tasks. We first show the effectiveness of our method and illustrate the role of the auxiliary network approximation fψ on several two-dimensional toy examples. The KL divergence from the target distributions to different variational approximations was also provided for direct comparison. We also compare the performance of SIVI-SM with both baseline methods on several Bayesian inference tasks, including a multidimensional Bayesian logistic regression problem and a high dimensional Bayesian multinomial logistic regression problem. |
| Researcher Affiliation | Academia | Longlin Yu School of Mathematical Sciences Peking University, Beijing, China EMAIL Cheng Zhang School of Mathematical Sciences and Center for Statistical Science Peking University, Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1 SIVI-SM with multivariate Gaussian conditional layer |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We consider the waveform3 dataset... 3https://archive.ics.uci.edu/ml/machine-learning-databases/waveform/. We used two data sets: MNIST4 and HAPT5. MNIST is a commonly used dataset in machine learning... 4http://yann.lecun.com/exdb/mnist/ 5http://archive.ics.uci.edu/ml/machine-learning-databases/00341/. Bayesian Neural Network on the UCI datasets. |
| Dataset Splits | Yes | The datasets are all randomly partitioned into 90% for training and 10% for testing. |
| Hardware Specification | Yes | The following table shows the run times of different methods per iteration on a RTX2080 GPU. |
| Software Dependencies | No | The paper states 'All experiments were implemented in Pytorch (Paszke et al., 2019).' but does not specify a version number for PyTorch or any other software. |
| Experiment Setup | Yes | If not otherwise specified, we use the Adam optimizer for training (Kingma & Ba, 2014). For SIVI-SM, we set the number of inner-loop gradient steps K = 1. For SIVI, we set L = 50 for the surrogate ELBO defined in Eq. 2. For UIVI, we used 10 iterations for every inner-loop HMC sampling. |