Gradient Information for Representation and Modeling
Authors: Jie Ding, Robert Calderbank, Vahid Tarokh
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data. Table 2: Classification accuracy of three methods for data with different levels of correlation |
| Researcher Affiliation | Academia | Jian Ding School of Statistics University of Minnesota Minneapolis, MN 55455 dingj@umn.edu Robert Calderbank Department of Electrical and Computer Engineering Duke University Durham, NC 27708 robert.calderbank@duke.edu Vahid Tarokh Department of Electrical and Computer Engineering Duke University Durham, NC 27708 vahid.tarokh@duke.edu |
| Pseudocode | Yes | Algorithm 1 Generic tree approximation based on gradient information Algorithm 2 Community discovery based on mutual information |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code for its methodology is being made open-source or publicly available. |
| Open Datasets | Yes | We apply our algorithm to a protein signaling flow cytometry dataset. The dataset encodes the presence of p = 11 proteins in n = 7466 cells. It was first analyzed using Bayesian networks in [22] who fit a directed acyclic graph to the data, later studied in [23] using different methods. In a data study, we considered a dataset constructed in [28]. The data was also studied in [29] using an algorithm that recovers the communities using the eigenvectors of the sample covariance matrix. |
| Dataset Splits | No | The paper mentions 'cross validation accuracy (with 30% test data)' for synthetic data, which implies a training portion, but it does not specify a distinct validation dataset split with percentages or counts. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x', or specific library versions). |
| Experiment Setup | No | The paper describes aspects of the experiment such as data generation parameters and tree traversal logic, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings necessary for replication. |