ESPN: Memory-Efficient Multi-vector Information Retrieval

Author:

Shrestha Susav1ORCID,Reddy Narasimha1ORCID,Li Zongwang2ORCID

Affiliation:

1. Texas A&M University, College Station, USA

2. Samsung Semiconductor, San Jose, United States

Funder

Samsung

Publisher

ACM

Reference66 articles.

1. Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae Jun Ham, and Jae W Lee. 2021. $FlashNeuron$:$SSD-Enabled$$Large-Batch$ Training of Very Deep Neural Networks. In 19th USENIX Conference on File and Storage Technologies (FAST 21). 387–401.

2. Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2018. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arxiv:1611.09268.

3. Dmitry Baranchuk, Artem Babenko, and Yury Malkov. 2018. Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors. In Computer Vision – ECCV 2018, Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.). Springer International Publishing, Cham. 209–224. isbn:978-3-030-01258-8

4. Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search. In 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

5. David R. Cheriton. 2019. From doc2query to docTTTTTquery. https://api.semanticscholar.org/CorpusID:208612557

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