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 | Conference PDF | Archive PDF | Plain Text | 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 {yufengzhang, jialupan, lkl}@hnu.edu.cn Wanwei Liu, Zhenbang Chen, Xinwang Liu College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology, Changsha, China {wwliu, zbchen, xinwangliu, wj}@nudt.edu.cn 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 wj@nudt.edu.cn |
| 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. |