Globally Convergent Variational Inference
Authors: Declan McNamara, Jackson Loper, Jeffrey Regier
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In ablation studies and practical problems, we demonstrate that our results explain the behavior of NPE in non-asymptotic finite-neuron settings, and show that NPE outperforms ELBO-based optimization, which often converges to shallow local optima. |
| Researcher Affiliation | Academia | Declan Mc Namara Jackson Loper Jeffrey Regier Department of Statistics University of Michigan {declan, jaloper, regier}@umich.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/declanmcnamara/gcvi_neurips. |
| Open Datasets | Yes | We use the MNIST dataset, freely available from torchvision under the BSD-3 License1 |
| Dataset Splits | No | The paper uses generated data for several experiments and mentions using N=1000 MNIST digits, but does not provide explicit training, validation, or test dataset splits (percentages or counts) or refer to standard splits with citations. |
| Hardware Specification | Yes | We used Py Torch (Paszke et al., 2019) for our experiments in accordance with its license, and NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | We used Py Torch (Paszke et al., 2019) for our experiments in accordance with its license... |
| Experiment Setup | Yes | SGD was performed using the Adam optimizer with a learning rate of ρ = 0.0001. We employ a learning rate scheduler that scales the learning rate as O(1/I), where I denotes the number of iterations. All models were fitted for 200,000 stochastic gradient steps... |