Teaching Old DB Neu(ral) Tricks: Learning Embeddings on Multi-tabular Databases

Author:

Gaur Garima1ORCID,Singh Rajat1ORCID,Arora Siddhant2ORCID,Gupta Vinayak1ORCID,Bedathur Srikanta1ORCID

Affiliation:

1. Indian Institute of Technology Delhi, India

2. Carnegie Mellon University, USA

Publisher

ACM

Reference39 articles.

1. Sercan Ö Arik and Tomas Pfister . 2021 . Tabnet: Attentive interpretable tabular learning. In AAAI. Sercan Ö Arik and Tomas Pfister. 2021. Tabnet: Attentive interpretable tabular learning. In AAAI.

2. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR.

3. Jinze Bai Jialin Wang Zhao Li Donghui Ding Ji Zhang and Jun Gao. 2021. ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases. In WWW. Jinze Bai Jialin Wang Zhao Li Donghui Ding Ji Zhang and Jun Gao. 2021. ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases. In WWW.

4. Ivan Bilan and Benjamin Roth. 2018. Position-aware Self-attention with Relative Positional Encodings for Slot Filling. arXiv preprint arXiv:1807.03052(2018). Ivan Bilan and Benjamin Roth. 2018. Position-aware Self-attention with Relative Positional Encodings for Slot Filling. arXiv preprint arXiv:1807.03052(2018).

5. Rajesh Bordawekar and Oded Shmueli. 2017. Using Word Embedding to Enable Semantic Queries in Relational Databases. In DEEM. Rajesh Bordawekar and Oded Shmueli. 2017. Using Word Embedding to Enable Semantic Queries in Relational Databases. In DEEM.

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