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.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Authors: Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,930 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,943 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 | In this section we compare our model to previous methods on a rich set of experiments. (Section 4, first sentence) and Results. Results for the Sensorles Drive dataset are shown in Tab. 1. Tables for other datasets are in the appendix. (Section 4) |
| Researcher Affiliation | Academia | Bertrand Charpentier, Daniel Zügner, Stephan Günnemann Technical University of Munich, Germany EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and further supplementary material is available online (www.daml.in.tum.de/postnet). (Section 4) |
| Open Datasets | Yes | We evaluate on the following real-world datasets: Segment [6], Sensorless Drive [6], MNIST [22] and CIFAR10 [19]. (Section 4, Datasets) |
| Dataset Splits | Yes | Moreover, for all experiments, we split the data into train, validation and test set (60%, 20%, 20%) and train/evaluate all models on 5 different splits. (Section 4, Baselines) |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch via citation but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | The weight of the entropy regularization is a hyperparameter; experiments have shown Post Net to be fairly insensitive to it, so in our experiments we set it to 10 5. (Section 3, end) |