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Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems. [PDF]

Micromachines (Basel)
He J, Xu J, Wang Y.
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A KWS System for Edge-Computing Applications with Analog-Based Feature Extraction and Learned Step Size Quantized Classifier. [PDF]

Sensors (Basel)
Shen Y, Wu B, Straeussnigg D, Gutierrez E.   +3 more
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Product Quantized Collaborative Filtering

IEEE Transactions on Knowledge and Data Engineering, 2021
Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items.
Defu Lian, Xing Xie, Enhong Chen, Hui Xiong   +3 more
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Optimized Product Quantization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose the high-dimensional vector space into the Cartesian product of subspaces and then quantize these subspaces separately.
Tiezheng, Ge, Kaiming, He, Qifa, Ke, Jian, Sun   +3 more
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Kernelized product quantization

Neurocomputing, 2017
There has been increasing interest in learning compact binary codes for large-scale image data representation and retrieval. In most existing hashing-based methods, high-dimensional vectors are hashed into Hamming space, and the similarity between two vectors is approximated by the Hamming distance between their binary codes.
Jie Liu, Yichao Zhang, Jianshe Zhou, Jinsheng Shi, Yongdong Zhang   +4 more
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Online Optimized Product Quantization

2020 IEEE International Conference on Data Mining (ICDM), 2020
Recently, approximate nearest neighbor(ANN) search has achieved great success in quantization models due to its high search performance, strong expression ability, and small memory space. However, most existing quantization methods are batch-based models, such as product quantization and optimized product quantization, they are not suitable for ...
Chong Liu, Defu Lian, Min Nie, Xia Hu
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Distance Encoded Product Quantization

2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
Many binary code embedding techniques have been proposed for large-scale approximate nearest neighbor search in computer vision. Recently, product quantization that encodes the cluster index in each subspace has been shown to provide impressive accuracy for nearest neighbor search.
Jae-Pil Heo, Zhe Lin, Sung-Eui Yoon
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Uniform Variance Product Quantization

Applied Mechanics and Materials, 2014
Product quantization (PQ) is an efficient and effective vector quantization approach to fast approximate nearest neighbor (ANN) search especially for high-dimensional data. The basic idea of PQ is to decompose the original data space into the Cartesian product of some low-dimensional subspaces and then every subspace is quantized separately with the ...
Qin Zhen Guo, Zhi Zeng, Shu Wu Zhang
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