Space-Time Local Embeddings
Authors: Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on nonmetric datasets show that more information can be preserved in space-time. |
| Researcher Affiliation | Collaboration | 1 Viper Group, Computer Vision and Multimedia Laboratory, University of Geneva sunk.edu@gmail.com, Stephane.Marchand-Maillet@unige.ch, and 2 Expedia, Switzerland, jwang1@expedia.com, and 3 Business Informatics Department, University of Applied Sciences, Western Switzerland, Alexandros.Kalousis@hesge.ch |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | NIPS22 contains a 4197 3624 author-document matrix from NIPS 1988 to 2009 [2]. Gr Qc is an ar Xiv co-authorship graph [16]. W5000 is the semantic similarities among 5000 English words in WS5000 5000 [2, 17]. |
| Dataset Splits | No | The paper does not specify distinct training, validation, or test dataset splits. The evaluation is primarily based on KL divergence on the input similarity matrix, which represents the entire dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | During gradient descent, {ys i } are updated by the delta-bar-delta scheme as used in t-SNE [13], where each scalar parameter has its own adaptive learning rate initialized to γs > 0; {yt i} are updated based on one global adaptive learning rate initialized to γt > 0. The learning of time should be more cautious, because pij(Y ) is more sensitive to time variations by eq. (7). Therefore, the ratio γt/γs should be very small, e.g. 1/100. the minimal KL that we have achieved within 5000 epochs is shown. |