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].
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
Authors: Yufeng Zhang, Jialu Pan, Li Ken Li, Wanwei Liu, Zhenbang Chen, Xinwang Liu, J Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we discuss several applications of our theories in deep learning, reinforcement learning, and sample complexity research... We conduct extensive experiments to compare our method with 13 baseline methods... Table 2: Group-wise anomaly detection Results (AUROC and AUPR in percentage) of our method KLODS and the SOTA method GOD2KS on Glow with batch sizes 5 and 10. We run our method for 5 times. Results of GOD2KS are referred from [28]. |
| Researcher Affiliation | Academia | Yufeng Zhang, Jialu Pan , Kenli Li College of Computer Science and Electronic Engineering Hunan University, Changsha, China EMAIL Wanwei Liu, Zhenbang Chen, Xinwang Liu College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology, Changsha, China EMAIL Ji Wang College of Computer State Key Laboratory for High Performance Computing Key laboratory of Software Engineering for Complex Systems National University of Defense Technology, Changsha, China EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses applications of its theoretical findings in a related work [57] but does not explicitly state that source code for the theoretical work or the specific KLODS algorithm mentioned is made publicly available within this paper or via a direct link. |
| Open Datasets | Yes | For example, Glow [29] assigns higher likelihoods for SVHN when trained on CIFAR-10... Table 2: Group-wise anomaly detection Results (AUROC and AUPR in percentage) of our method KLODS and the SOTA method GOD2KS on Glow with batch sizes 5 and 10. We run our method for 5 times. Results of GOD2KS are referred from [28]. ID OOD Fashion MNIST MNIST... CIFAR-10 Celeb A... CIFAR-10 CIFAR-100 SVHN LSUN |
| Dataset Splits | No | The paper mentions using In-Distribution (ID) and Out-of-Distribution (OOD) datasets for evaluation but does not provide specific train/validation/test dataset splits, percentages, or methodology for partitioning the data in this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running the experiments mentioned in the application section. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | Table 2: Group-wise anomaly detection Results (AUROC and AUPR in percentage) of our method KLODS and the SOTA method GOD2KS on Glow with batch sizes 5 and 10. We run our method for 5 times. |