Self-attention with Functional Time Representation Learning

Authors: Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 {Da.Xu,Chuanwei.Ruan,EKorpeoglu,SKumar4,KAchan}@walmartlabs.com
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.