Learning Binary Codes for Maximum Inner Product Search

By Fumin Shen, Wei David Liu, Shaoting Zhang, Yang Yang, Heng Tao Shen. 2015

154 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Information Retrieval & Search, Representation & Self-Supervised Learning

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

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In Representation & Self-Supervised Learning

Citations in the corpus

REALM: Retrieval-Augmented Language Model Pre-Training (2020, methodology): “Thus, we can employ Maximum Inner Product Search (MIPS) algorithms to find the approximate top k documents, using running time and storage space that scale sub-linearly with the number of documents (Ram & Gray, 2012; Shrivastava & Li, 2014; Shen et al., 2015).”
Entities as Experts: Sparse Memory Access with Entity Supervision (2020, background): “, 2019) and, while our current implementation does not yet include an efficient implementation of top-k routing (Section 2), we note that it is possible with fast maximum inner product search (Ram and Gray, 2012; Shen et al., 2015; Shrivastava and Li, 2014; Johnson et al., 2017).”
Efficient Natural Language Response Suggestion for Smart Reply (2017, background): “For more background, we refer readers to the relevant works of [3, 5, 21, 22].”
Optimizing Dense Retrieval Model Training with Hard Negatives (2021, methodology): “1We focus on the training efficiency because the efficiency in the inference process is guaranteed by the maximum inner product search algorithms[14, 27].”
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval (2018, background): “Owing to the explosive increase in multimedia data from a great variety of search engines and social media, crossmodal retrieval has become a compelling topic in recent years [20, 21, 22, 23, 24, 25, 29, 35, 36, 41, 42, 45].”
Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval (2017, unknown): “Similar regularization terms are also used in [53, 37] for effective hash code learning.”
Central Similarity Quantization for Efficient Image and Video Retrieval (2019, methodology): “Different from previous works [41, 25, 23, 26, 38, 31, 32, 19, 34, 16, 17, 45], this work proposes a new central similarity metric and use it to model the relationships between similar and dissimilar pairs for improving the discriminability of generated hash codes.”
RepBERT: Contextualized Text Embeddings for First-Stage Retrieval (2020, methodology): “Note that in our current retrieval implementation, we have not adopted optimized MIPS algorithms [6, 7, 8] and simply compute relevance scores between the given query and each document.”
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models (2024, unknown): “Komeili et al. [61] propose retrieving information from the internet search to further augment conversation performance.”
Learning and Inference via Maximum Inner Product Search (2016, background): “Maximum Inner Product Search (MIPS) is a standard computational task where we are given a set of vectors V and a query vector q, and the goal is to find the vector in V that has the maximum inner product with the query vector q (Shrivastava & Li, 2014; Ram & Gray, 2012; Shen et al., 2015).”
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, methodology): “The Sublinear Maximum Inner Product Search (MIPS) algorithm [68], [69], [70] is used to improve the retrieval efficiency given a question, especially when the document repository is large-scale.”