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..
Self-attention with Functional Time Representation Learning
Authors: Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the proposed time embedding methods with self-attention on several real-world datasets from various domains. The experiemnts aim at quantitatively evaluating the performance of the four time embedding methods, and comparing them with baseline models. |
| Researcher Affiliation | Industry | Walmart Labs California, CA 94086 EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Stack Overflow2 dataset records user s history awarded badges in a question-answering website. The task is to predict the next badge the user receives, as a classification task. 2https://archive.org/details/stackexchange |
| Dataset Splits | Yes | On the stack overflow dataset we use the same filtering procedures described in [12] and randomly split the dataset on users into training (80%), validation (10%) and test (10%). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We use d = 100 for both Bochner and Mercer time embedding, with the sensitivity analysis on time embedding dimensions provided in appendix. We treat the dimension of Fourier basis k for Mercer time embedding as hyper-parameter, and select from {1, 5, 10, 15, 20, 25, 30} according to the validation Hit@10 as well. |