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].
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Authors: Yufan Zhou, Changyou Chen, Jinhui Xu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our framework can achieve state-of-the-art results compared to existing methods for the two tasks. |
| Researcher Affiliation | Academia | Yufan Zhou, Changyou Chen, Jinhui Xu Department of Computer Science and Engineering State University of New York at Buffalo EMAIL |
| Pseudocode | Yes | Algorithm 1 Heat Kernel Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | Yes | UCI datasets, CIFAR-10, STL-10, Image Net, Celeb A |
| Dataset Splits | No | The provided text does not explicitly detail the training/validation/test dataset splits, specific percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For all experiments, a 2-layer BNN with 50 hidden units, 10 weight particles, Re LU activation is used. We assign the isotropic Gaussian prior to the network weights. ... images are scaled to the resolution of 32 32, 48 48, 64 64 and 160 160 respectively. ... we test 2 architectures on CIFAR-10 and STL-10, 1 architecture on Celeb A and Image Net. |