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Yangqing Jia
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2020 – today
- 2024
- [c38]Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Kai Li, Song Han:
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models. CVPR 2024: 7183-7193 - [i14]Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Ming-Yu Liu, Kai Li, Song Han:
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models. CoRR abs/2402.19481 (2024) - 2021
- [j5]Yingda Chen, Jiamang Wang, Yifeng Lu, Ying Han, Zhiqiang Lv, Xuebin Min, Hua Cai, Wei Zhang, Haochuan Fan, Chao Li, Tao Guan, Wei Lin, Yangqing Jia, Jingren Zhou:
Fangorn: Adaptive Execution Framework for Heterogeneous Workloads on Shared Clusters. Proc. VLDB Endow. 14(12): 2972-2985 (2021) - 2020
- [c37]Wencong Xiao, Shiru Ren, Yong Li, Yang Zhang, Pengyang Hou, Zhi Li, Yihui Feng, Wei Lin, Yangqing Jia:
AntMan: Dynamic Scaling on GPU Clusters for Deep Learning. OSDI 2020: 533-548
2010 – 2019
- 2019
- [c36]Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer:
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search. CVPR 2019: 10734-10742 - [c35]Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha:
ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation. CVPR 2019: 11398-11407 - [c34]Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim M. Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang:
Machine Learning at Facebook: Understanding Inference at the Edge. HPCA 2019: 331-344 - [c33]Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu
, Jun Yang, Wei Lin, Yangqing Jia:
Characterizing Deep Learning Training Workloads on Alibaba-PAI. IISWC 2019: 189-202 - [e1]Xiang Bai, Yi Fang, Yangqing Jia, Meina Kan, Shiguang Shan, Chunhua Shen, Jingdong Wang, Gui-Song Xia, Shuicheng Yan, Zhaoxiang Zhang, Kamal Nasrollahi, Gang Hua, Thomas B. Moeslund, Qiang Ji:
Video Analytics. Face and Facial Expression Recognition - Third International Workshop, FFER 2018, and Second International Workshop, DLPR 2018, Beijing, China, August 20, 2018, Revised Selected Papers. Lecture Notes in Computer Science 11264, Springer 2019, ISBN 978-3-030-12176-1 [contents] - [i13]Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia:
Characterizing Deep Learning Training Workloads on Alibaba-PAI. CoRR abs/1910.05930 (2019) - 2018
- [c32]Kim M. Hazelwood, Sarah Bird, David M. Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang:
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. HPCA 2018: 620-629 - [i12]Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Shanker Khudia, James Law, Parth Malani, Andrey Malevich, Nadathur Satish, Juan Miguel Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim M. Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy:
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. CoRR abs/1811.09886 (2018) - [i11]Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer:
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search. CoRR abs/1812.03443 (2018) - [i10]Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha:
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation. CoRR abs/1812.08934 (2018) - 2017
- [c31]Yangqing Jia:
ML computation patterns in production systems. TIML@ISCA 2017: 11 - [i9]Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He:
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. CoRR abs/1706.02677 (2017) - [i8]Andrew Tulloch, Yangqing Jia:
High performance ultra-low-precision convolutions on mobile devices. CoRR abs/1712.02427 (2017) - 2016
- [i7]Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs/1603.04467 (2016) - [i6]Zhicheng Yan, Hao Zhang, Yangqing Jia, Thomas M. Breuel, Yizhou Yu:
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation. CoRR abs/1603.04871 (2016) - 2015
- [c30]Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan
Going deeper with convolutions. CVPR 2015: 1-9 - [c29]Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, Alexander C. Berg:
MatchNet: Unifying feature and metric learning for patch-based matching. CVPR 2015: 3279-3286 - 2014
- [b1]Yangqing Jia:
Learning Semantic Image Representations at a Large Scale. University of California, Berkeley, USA, 2014 - [j4]Jingdong Wang
Regularized Tree Partitioning and Its Application to Unsupervised Image Segmentation. IEEE Trans. Image Process. 23(4): 1909-1922 (2014) - [c28]Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, Hartwig Adam:
Large-Scale Object Classification Using Label Relation Graphs. ECCV (1) 2014: 48-64 - [c27]Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. ICML 2014: 647-655 - [c26]Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, Trevor Darrell:
Caffe: Convolutional Architecture for Fast Feature Embedding. ACM Multimedia 2014: 675-678 - [c25]Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, Sergey Ioffe:
Deep Convolutional Ranking for Multilabel Image Annotation. ICLR (Poster) 2014 - [c24]Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell:
One-Shot Adaptation of Supervised Deep Convolutional Models. ICLR (Workshop Poster) 2014 - [i5]Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, Trevor Darrell:
Caffe: Convolutional Architecture for Fast Feature Embedding. CoRR abs/1408.5093 (2014) - [i4]Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich:
Going Deeper with Convolutions. CoRR abs/1409.4842 (2014) - 2013
- [c23]Yangqing Jia, Mei Han:
Category-Independent Object-Level Saliency Detection. ICCV 2013: 1761-1768 - [c22]Yangqing Jia, Trevor Darrell:
Latent Task Adaptation with Large-Scale Hierarchies. ICCV 2013: 2080-2087 - [c21]Yangqing Jia, Oriol Vinyals, Trevor Darrell:
On Compact Codes for Spatially Pooled Features. ICML (3) 2013: 549-557 - [c20]Sergio Guadarrama, Lorenzo Riano, Dave Golland, Daniel Göhring, Yangqing Jia, Dan Klein, Pieter Abbeel, Trevor Darrell:
Grounding spatial relations for human-robot interaction. IROS 2013: 1640-1647 - [c19]Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths, Trevor Darrell:
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies. NIPS 2013: 1842-1850 - [c18]Oriol Vinyals, Yangqing Jia, Trevor Darrell:
Why Size Matters: Feature Coding as Nystrom Sampling. ICLR (Workshop) 2013 - [p1]Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, Trevor Darrell:
A Category-Level 3D Object Dataset: Putting the Kinect to Work. Consumer Depth Cameras for Computer Vision 2013: 141-165 - [i3]Yangqing Jia, Oriol Vinyals, Trevor Darrell:
Pooling-Invariant Image Feature Learning. CoRR abs/1302.5056 (2013) - [i2]Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell:
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. CoRR abs/1310.1531 (2013) - 2012
- [c17]Yangqing Jia, Chang Huang, Trevor Darrell:
Beyond spatial pyramids: Receptive field learning for pooled image features. CVPR 2012: 3370-3377 - [c16]Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell:
Learning with Recursive Perceptual Representations. NIPS 2012: 2834-2842 - [c15]Seppo Virtanen, Yangqing Jia, Arto Klami, Trevor Darrell:
Factorized Multi-Modal Topic Model. UAI 2012: 843-851 - [i1]Seppo Virtanen, Yangqing Jia, Arto Klami, Trevor Darrell:
Factorized Multi-Modal Topic Model. CoRR abs/1210.4920 (2012) - 2011
- [c14]Yangqing Jia, Mathieu Salzmann, Trevor Darrell:
Learning cross-modality similarity for multinomial data. ICCV 2011: 2407-2414 - [c13]Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, Trevor Darrell:
A category-level 3-D object dataset: Putting the Kinect to work. ICCV Workshops 2011: 1168-1174 - [c12]Kate Saenko, Sergey Karayev, Yangqing Jia, Alex Shyr, Allison Janoch, Jonathan Long, Mario Fritz, Trevor Darrell:
Practical 3-D object detection using category and instance-level appearance models. IROS 2011: 793-800 - [c11]Yangqing Jia, Trevor Darrell:
Heavy-tailed Distances for Gradient Based Image Descriptors. NIPS 2011: 397-405 - 2010
- [c10]Yangqing Jia, Mathieu Salzmann, Trevor Darrell:
Factorized Latent Spaces with Structured Sparsity. NIPS 2010: 982-990
2000 – 2009
- 2009
- [j3]Yangqing Jia, Changshui Zhang:
Front-view vehicle detection by Markov chain Monte Carlo method. Pattern Recognit. 42(3): 313-321 (2009) - [j2]Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang:
Semi-supervised orthogonal discriminant analysis via label propagation. Pattern Recognit. 42(11): 2615-2627 (2009) - [j1]Yangqing Jia, Feiping Nie, Changshui Zhang:
Trace Ratio Problem Revisited. IEEE Trans. Neural Networks 20(4): 729-735 (2009) - [c9]Yangqing Jia, Shuicheng Yan, Changshui Zhang:
Semi-Supervised Classification on Evolutionary Data. IJCAI 2009: 1083-1088 - 2008
- [c8]Yangqing Jia, Changshui Zhang:
Instance-level Semisupervised Multiple Instance Learning. AAAI 2008: 640-645 - [c7]Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang, Shuicheng Yan:
Trace Ratio Criterion for Feature Selection. AAAI 2008: 671-676 - [c6]Jingdong Wang
Normalized tree partitioning for image segmentation. CVPR 2008 - [c5]Yangqing Jia, Jingdong Wang
Augmented tree partitioning for interactive image segmentation. ICIP 2008: 2292-2295 - [c4]Yangqing Jia, Changshui Zhang:
Learning distance metric for semi-supervised image segmentation. ICIP 2008: 3204-3207 - [c3]Yangqing Jia, Changshui Zhang:
Local Regularized Least-Square Dimensionality Reduction. ICPR 2008: 1-4 - [c2]Yangqing Jia, Jingdong Wang, Changshui Zhang, Xian-Sheng Hua:
Finding image exemplars using fast sparse affinity propagation. ACM Multimedia 2008: 639-642 - [c1]Yangqing Jia, Zheng Wang, Changshui Zhang:
Distortion-Free Nonlinear Dimensionality Reduction. ECML/PKDD (1) 2008: 564-579
Coauthor Index
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