activity
20162026
most citedParallelizing Word2Vec in Multi-Core and Many-Core Architectures

8 citations · 8 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs

Jin Bai, Huiyao Zhang, Qi Wen +4

The segmentation of thin linear structures is inherently topology allowbreak-critical, where minor local errors can sever long-range connectivity. While recent State-Space Models (…

cs.AR2022

Sustainable AI Processing at the Edge

Sébastien Ollivier, Sheng Li, Yue Tang +5

Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud.…

cs.CV2017

Enabling Sparse Winograd Convolution by Native Pruning

Sheng Li, Jongsoo Park, Ping Tak Peter Tang

Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs. Sparse Winograd…

cs.DC2016★ 8 cited

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Shihao Ji, Nadathur Satish, Sheng Li +1

Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words. State-of-the-art algorithms including those by Mikolov et al. have been parallel…

cs.CV2016

Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Jongsoo Park, Sheng Li, Wei Wen +4

Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The numbe…

cs.DC2016

Parallelizing Word2Vec in Shared and Distributed Memory

Shihao Ji, Nadathur Satish, Sheng Li +1

Word2Vec is a widely used algorithm for extracting low-dimensional vector representations of words. It generated considerable excitement in the machine learning and natural languag…