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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bridging OOD Detection and Generalization: A Graph-Theoretic View
Authors: Han Wang, Sharon Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results showcase competitive performance in comparison to existing methods, thereby validating our theoretical underpinnings. |
| Researcher Affiliation | Academia | Han Wang Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign EMAIL Yixuan Li Department of Computer Sciences University of Wisconsin-Madison EMAIL |
| Pseudocode | No | The paper describes algorithms and methods through text and mathematical equations but does not present a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code is publicly available at https://github.com/deeplearning-wisc/graph-spectral-ood. |
| Open Datasets | Yes | Following the setup of [5], we employ CIFAR-10 [14] as Pin and CIFAR-10-C [15] with Gaussian additive noise as the Pcovariate out . For Psemantic out , we leverage SVHN [16], LSUN [17], Places365 [18], Textures [19]. |
| Dataset Splits | Yes | For splitting training/validation, we use 30% for validation and the remaining for training. |
| Hardware Specification | Yes | We conduct all the experiments in Pytorch, using NVIDIA GeForce RTX 2080Ti. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number for it or any other software dependencies, which is required for a reproducible description. |
| Experiment Setup | Yes | We use stochastic gradient descent with Nesterov momentum [22], with weight decay 0.0005 and momentum 0.09. We train the network with the loss function in Eq. 6 for 1000 epochs. The learning rate is 0.03 and the batch size is 512. We fine-tune for 20 epochs with a learning rate of 0.005 and batch size of 512. |