Sheng R. Li

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EDBT 2026 Demo / reviewers in the wild / expert

dblp:185/0679 · DBLP profile ↗

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1ranked-venue papers

0as first author

0since 2021 · last 2017

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Efficient and distributed learning · 70% 3D vision · 23% Deep learning architectures and training · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Efficient and distributed learning inference efficiency	0.3	1	2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning · ICLR (Poster) 2017
Machine learning › Efficient and distributed learning model compression	0.3	1	2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning · ICLR (Poster) 2017
Machine learning › Efficient and distributed learning › model compression pruning	0.3	1	2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning · ICLR (Poster) 2017
Computer vision › 3D vision › point cloud processing sparse convolution	0.3	1	2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning · ICLR (Poster) 2017
Machine learning › Deep learning architectures and training convolutional neural network	0.1	1	2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning · ICLR (Poster) 2017

Methods — techniques the papers use, named apart from their topics

guided pruning · 0.3direct sparse convolution · 0.3

Year	Publication	Venue	Position
2017	Faster CNNs with Direct Sparse Convolutions and Guided Pruning Jongsoo Park, Sheng R. Li, Wei Wen 0003, Ping Tak Peter Tang, Hai Li 0001, Yiran Chen 0001, Pradeep Dubey	ICLR (Poster)	2