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Wei Li

4 papers hereh-index 5537 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.CV1
same name
  • Wei Li — 45 papers, h 18
  • Wei Li — 37 papers, h 24
  • Wei Li — 35 papers, h 30
  • Wei Li — 28 papers, h 31
  • Wei Li — 23 papers, h 18
  • Wei Li — 21 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172021
most citedAppearance-and-Relation Networks for Video Classification

44 citations · 74 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2021★ 27 cited

Tied & Reduced RNN-T Decoder

Rami Botros, Tara N. Sainath, Robert David +3

Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown that, under some conditions, it is possible to simplify its prediction network with little or no…

cs.CL2020★ 3 cited

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

Tara N. Sainath, Yanzhang He, Bo Li +26

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…

cs.CL2019

Two-Pass End-to-End Speech Recognition

Tara N. Sainath, Ruoming Pang, David Rybach +9

The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-ca…

cs.CV2017★ 44 cited

Appearance-and-Relation Networks for Video Classification

Limin Wang, Wei Li, Wen Li +1

Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), t…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.