Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Collapsed Inference for Bayesian Deep Learning
Authors: Zhe Zeng, Guy Van den Broeck
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 26,451 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,239 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On various regression and classification tasks, our collapsed Bayesian deep learning approach demonstrates significant improvements over existing methods and sets a new state of the art in terms of uncertainty estimation as well as predictive performance. |
| Researcher Affiliation | Academia | Zhe Zeng Computer Science Department University of California, Los Angeles EMAIL Guy Van den Broeck Computer Science Department University of California, Los Angeles EMAIL |
| Pseudocode | Yes | We summarize our proposed algorithm CIBER in Algorithm 1 in the Appendix. |
| Open Source Code | Yes | Code and experiments are available at https://github.com/UCLA-Star AI/CIBER. |
| Open Datasets | Yes | We experiment on 5 small UCI datasets: boston, concrete, yacht, naval and energy. We further experiment on 6 large UCI datasets: elevators, keggdirected, keggundirected, pol, protein and skillcraft. CIFAR-10 and CIFAR-100 (Krizhevsky et al., 2009) |
| Dataset Splits | Yes | The hyperparameters including learning rates and weight decay are tuned by performing a grid search to maximize the Gaussian log likelihood using a validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'pywmi library' but does not specify version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | All the network models are trained for 300 epochs using SGD. We start the weight collection after epoch 160 with step size 5. We follow exactly the same hyperparameters as Maddox et al. (2019) including learning rates and weight decay parameters. |