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 [1].
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Authors: Dilin Wang, Qiang Liu
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our method provides an effective mechanism for diversitypromoting learning, achieving substantial improvement over existing methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, UT Austin. Correspondence to: Dilin Wang <EMAIL>, Qiang Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Nonlinear SVGD for Learning Mixture Models |
| Open Source Code | Yes | Our code is avalable at https://github. com/dilinwang820/nonlinear_svgd. |
| Open Datasets | Yes | We evaluate our approach on the MNIST dataset (Le Cun et al., 1998), which consists of 70,000 handwritten digits of 28-by-28 pixel size. |
| Dataset Splits | Yes | We take 50% of the normal data for training and the rest for testing. |
| Hardware Specification | No | The paper mentions 'Google Cloud for their support' but does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used (e.g., 'Adam with a constant learning rate' is mentioned, but no framework or programming language versions like PyTorch 1.x or Python 3.x are specified). |
| Experiment Setup | Yes | In our experiments, all methods are optimized using Adagrad with a constant learning rate of 0.05. For each model, we train 50, 000 iterations with a mini-batch size of 256. We clip the logarithm values of the variance σi to [ 3, 3] to avoid singularities. |