Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy
Authors: Lei Xu, Lei Chen, Rong Wang, Feiping Nie, Xuelong Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Lei Xu School of Computer Science & School of Artificial Intelligence, OPtics and Electro Nics (i OPEN) Northwestern Polytechnical University Xi an 710072, P.R. China solerxl1998@gmail.com Lei Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003, P.R. China chenlei@njupt.edu.cn Rong Wang School of Artificial Intelligence, OPtics and Electro Nics (i OPEN) Northwestern Polytechnical University Xi an 710072, P.R. China wangrong07@tsinghua.org.cn Feiping Nie School of Artificial Intelligence, OPtics and Electro Nics (i OPEN) & School of Computer Science Northwestern Polytechnical University Xi an 710072, P.R. China feipingnie@gmail.com Xuelong Li School of Artificial Intelligence, OPtics and Electro Nics (i OPEN) Northwestern Polytechnical University Xi an 710072, P.R. China li@nwpu.edu.cn |
| Pseudocode | Yes | Algorithm 1 UFS |
| Open Source Code | No | The paper uses or references code for competing methods and a differentiable top-k selector component, but does not provide an explicit statement or link to the source code for their own proposed methodology. For example: "the implementation of differentiable top-k selector is based on the code provided by [26] in https://papers.nips.cc/paper_files/paper/2020/hash/ ec24a54d62ce57ba93a531b460fa8d18-Abstract.html" |
| Open Datasets | Yes | Table 1 exhibits the details of these datasets, which include many high-dimensional datasets to test the performance of our method. |
| Dataset Splits | Yes | We partition each dataset into training data and testing data using an 8:2 ratio and identify useful features using training data. |
| Hardware Specification | Yes | All experiments are conducted on a server equipped with an RTX 3090 GPU and an Intel Xeon Gold 6240 (18C36T) @ 2.6GHz x 2 (36 cores in total) CPU. |
| Software Dependencies | No | The paper mentions software frameworks like PyTorch and scikit-learn but does not provide specific version numbers for these dependencies, which are necessary for reproducible setup. For example: "Our method is implemented using the Py Torch framework [48]." and "scikit-learn library [47]". |
| Experiment Setup | Yes | We train our method using the Adam optimizer for 1000 epochs on all datasets, with the learning rate searched from {10 4, 10 3, 10 2, 10 1, 100, 101}. We search the parameter γ in {10 3, 10 2, 10 1} and the parameter k in {1, 2, 3, 4, 5}. |