Fast search algorithm for product-quantized codes via hash-tables
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Updated
Jul 3, 2018 - C++
Fast search algorithm for product-quantized codes via hash-tables
[DEPRECATED] Baseline Project for Semantic Searching
Plugin to integrate approximate nearest neighbor(ANN) search with Elasticsearch
Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
Fast C++ implementation of https://github.com/yahoo/lopq: Locally Optimized Product Quantization (LOPQ) model and searcher for approximate nearest neighbor search of high dimensional data.
Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search.
basis embedding: a product quantization based model compression method for language models.
Product Quantization k-Nearest Neighbors
Converted version of yahoo LOPQ from python2.7 to python3.6
utils to use word embedding models like word2vec vectors in a PostgreSQL database
Product quantization using nearest neighbor search
Pytorch implementation of LISA (Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation. WWW 2021)
CIKM'21: JPQ substantially improves the efficiency of Dense Retrieval with 30x compression ratio, 10x CPU speedup and 2x GPU speedup.
Multiply-ADDitioN-lESS in Go.
WSDM'22 Best Paper: Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
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