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GAMMA DISTRIBUTION APPLICATION OF PARAMETER ESTIMATION METHOD BASED ON WEIBULL AUXILIARY MODEL (MT)

Autor: XIAO ShiHai, JIANG RenYan

Publikováno v: Jixie qiangdu, Pp 620-625 (2023)

Since there are more systems to be observed on the sites and the observation time is shorter than the average life of the product, the life data actually collected is often heavily censored data. For such data, the classical parameter estimation meth

Externí odkaz: https://doaj.org/article/61b2182136364ead993e80ca92105779

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FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search

Autor: Tian, Bing, Liu, Haikun, Tang, Yuhang, Xiao, Shihai, Duan, Zhuohui, Liao, Xiaofei, Zhang, Xuecang, Zhu, Junhua, Zhang, Yu

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern

Externí odkaz: http://arxiv.org/abs/2409.16576

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DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex

Autor: Ni, Jiongkang, Xu, Xiaoliang, Wang, Yuxiang, Li, Can, Yao, Jiajie, Xiao, Shihai, Zhang, Xuecang

Given a vector dataset $\mathcal{X}$ and a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a graph index $G$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over $G$.

Externí odkaz: http://arxiv.org/abs/2310.00402

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Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks

Autor: Jiang, Lei, Liu, Yongqing, Xiao, Shihai, Chua, Yansong

Lateral inhibitory connections have been observed in the cortex of the biological brain, and has been extensively studied in terms of its role in cognitive functions. However, in the vanilla version of backpropagation in deep learning, all gradients

Externí odkaz: http://arxiv.org/abs/2208.06918

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Vertical Writes: Closing the Throughput Gap between Deeply Scaled STT-MRAM and DRAM

Autor: Engin Ipek, Florian Longnos, Xiao Shihai, Yang Wei

Publikováno v: IEEE Computer Architecture Letters. 17:151-154

STT-MRAM is a second generation MRAM technology that addresses many of the scaling problems of earlier generation magnetic RAMs, and is a promising candidate to replace DRAM due to its high operational speed, scalable energy characteristics, and high

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c87639721b4eb755cc22fd9c81063604
https://doi.org/10.1109/lca.2018.2820027

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Bit-Level Load Balancing: A New Technique for Improving the Write Throughput of Deeply Scaled STT-MRAM

Autor: Engin Ipek, Yang Wei, Xiao Shihai, Florian Longnos

Publikováno v: IEEE Computer Architecture Letters. 17:139-142

Emerging non-volatile memories (NVMs) have drawn significant attention as potential DRAM replacements. STT-MRAM is one of the most promising NVMs due to its relatively low write energy, high speed, and high endurance. However, STT-MRAM suffers from i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::25c8de7fc938514f521488e27f45dfba
https://doi.org/10.1109/lca.2018.2819979

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Akademický článek

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Akademický článek

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