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Yangqing Jia

Yangqing Jia has 3 papers in our library, in 2013–2018, most of them on GPU & parallel computing; often with Jeff Donahue, Trevor Jackson Darrell, Evan Shelhamer.

3 papers in our library · 2013–2018

OpenAlex · h-index 36 · 78 works · 83,574 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 2,608 times in our library · 27 more in our library without a page yet · ⭐ Paper Stars ranking: #376

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of California, BerkeleyBerkeley · United States2010–2014 · 16 papers🎓Tsinghua UniversityBeijing · China2008–2009 · 12 papers🏢GoogleMountain View · United States2013–2015 · 9 papers🔬State Key Laboratory of Intelligent Technology and SystemsBeijing · China2008–2009 · 8 papers🏢MetaMenlo Park · United States2017–2019 · 6 papers🔬International Computer Science InstituteBerkeley · United States2010–2013 · 6 papers🏢Alibaba GroupHangzhou · China2019–2021 · 3 papers🏢MetaLondon · United Kingdom2017–2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksHardware and Architecture · 1 work

🧭 Topics 1Research subjects and languages the work is about.GPU & parallel computing · 1 work

 Works by year

2010s

20181 paper20141 paper20131 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Caffe: Convolutional Architecture for Fast Feature Embedding (2014 · 4,209 citations)
    Deep convolutional networks had overtaken handcrafted features in visual recognition. Yet reproducing published results often took months, and few toolkits offered fast, ready-to-deploy models. The BVLC team at UC Berkeley presents Caffe, an open-source, BSD-licensed C++ library with CUDA support and Python and…
  2. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013 · 3,463 citations)
    The paper asks whether the internal activations of a large convolutional network, trained with full supervision on ImageNet object classes, can serve as a ready-made image descriptor for different vision problems that lack enough data to train deep models.
  3. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (2018 · 1,405 citations)
    Building ConvNets that are both accurate and fast on phones is hard. The space of possible architectures is enormous, earlier reinforcement-learning NAS methods cost thousands of GPU-hours, and the usual efficiency proxy (FLOPs) often fails to predict real latency. The authors propose DNAS, a differentiable search…

All works

30 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Going deeper with convolutions ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetChristian Szegedy et al.2015 · 46,691 citations
  2. Caffe ↗ACM International Conference on Multimedia (ACM MM) · no summary yetYangqing Jia et al.2014 · 10,940 citations
  3. Caffe: Convolutional Architecture for Fast Feature EmbeddingarXiv:1408.5093Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.2014 · 4,209 citations
  4. DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionarXiv:1310.1531Jeff Donahue, Yangqing Jia, Oriol Vinyals, et al.2013 · 3,463 citations
  5. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour ↗arXiv:1706.02677 · no summary yetPriya Goyal et al.2017 · 2,756 citations
  6. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchIEEE Computer Society Conference on Computer Vision and Pattern RecognitionBichen Wu, Xiaoliang Dai, Peizhao Zhang, et al.2018 · 1,405 citations
  7. MatchNet: Unifying feature and metric learning for patch-based matching ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetXufeng Han et al.2015 · 844 citations
  8. Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective ↗Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings · no summary yetKim Hazelwood et al.2018 · 623 citations
  9. ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation ↗arXiv:1812.08934 · no summary yetXiaoliang Dai et al.2018 · 297 citations
  10. A Category-Level 3D Object Dataset: Putting the Kinect to Work ↗Advances in computer vision and pattern recognition · no summary yetAllison Janoch et al.2012 · 258 citations
  11. A category-level 3-D object dataset: Putting the Kinect to work ↗IEEE International Conference on Computer Vision Workshops · no summary yetAllison Janoch et al.2011 · 253 citations
  12. Beyond spatial pyramids: Receptive field learning for pooled image features ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetYangqing Jia, Chang Huang, Trevor Jackson Darrell2012 · 236 citations
  13. Learning cross-modality similarity for multinomial data ↗International Conference on Computer Vision · no summary yetYangqing Jia, Mathieu Salzmann, Trevor Jackson Darrell2011 · 194 citations
  14. Factorized Latent Spaces with Structured Sparsity ↗no summary yetYangqing Jia, Mathieu Salzmann, Trevor Jackson Darrell2010 · 166 citations
  15. Grounding spatial relations for human-robot interaction ↗IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · no summary yetSergio Guadarrama et al.2013 · 149 citations
  16. One-Shot Adaptation of Supervised Deep Convolutional Models ↗arXiv (Cornell University) · no summary yetJudy Hoffman et al.2013 · 67 citations
  17. Learning with Recursive Perceptual Representations ↗no summary yetOriol Vinyals et al.2012 · 60 citations
  18. Factorized Multi-Modal Topic Model ↗arXiv (Cornell University) · no summary yetSeppo Virtanen et al.2012 · 38 citations
  19. Heavy-tailed Distances for Gradient Based Image Descriptors ↗no summary yetYangqing Jia, Trevor Jackson Darrell2011 · 36 citations
  20. Practical 3-D Object detection using category and instance-level appearance models ↗IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · no summary yetKate Saenko et al.2011 · 17 citations
  21. Pooling-Invariant Image Feature Learning ↗arXiv (Cornell University) · no summary yetYangqing Jia, Oriol Vinyals, Trevor Jackson Darrell2013 · 11 citations
  22. Mid-level Features Improve Recognition of Interactive Activities ↗no summary yetKate Saenko et al.2012 · 11 citations
  23. Latent Task Adaptation with Large-Scale Hierarchies ↗IEEE International Conference on Computer Vision (ICCV) · no summary yetYangqing Jia, Trevor Jackson Darrell2013 · 9 citations
  24. On Compact Codes for Spatially Pooled Features ↗no summary yetYangqing Jia, Oriol Vinyals, Trevor Jackson Darrell2013 · 3 citations
  25. Why Size Matters: Feature Coding as Nystrom Sampling ↗arXiv (Cornell University) · no summary yetOriol Vinyals, Yangqing Jia, Trevor Jackson Darrell2013 · 1 citation

See all 30 works by Yangqing Jia →

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 2,608 times (of the 149,439 papers whose bibliographies we read); 174 citing papers with a page here →.

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