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ORCIDSong Han 0003
Person information
- affiliation: Massachusetts Institute of Technology, Cambridge, MA, USA
- affiliation (former): Stanford University, Stanford, USA
Other persons with the same name
- Song Han — disambiguation page
- Song Han 0001
![[0000-0002-9072-420X]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-9072-420X")
— Yanshan University, Institute of Electrical Engineering, Qinhuangdao, China - Song Han 0002 — University of Connecticut, Department of Computer Science and Engineering, Storrs, CT, USA (and 2 more)
- Song Han 0004 — Curtin University, School of Information Systems, Perth, Australia
- Song Han 0005 — Kim Il Sung University, Pyongyang, North Korea
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2020 – today
- 2020
[j13]William J. Dally, Yatish Turakhia, Song Han:
Domain-specific hardware accelerators. Commun. ACM 63(7): 48-57 (2020)- [j12]Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han:
Hardware-Centric AutoML for Mixed-Precision Quantization. Int. J. Comput. Vis. 128(8): 2035-2048 (2020) - [j11]Han Cai, Ji Lin, Yujun Lin, Zhijian Liu, Kuan Wang, Tianzhe Wang, Ligeng Zhu, Song Han:
AutoML for Architecting Efficient and Specialized Neural Networks. IEEE Micro 40(1): 75-82 (2020) - [j10]H.-S. Philip Wong, Kerem Akarvardar, Dimitri A. Antoniadis, Jeffrey Bokor, Chenming Hu, Tsu-Jae King Liu, Subhasish Mitra, James D. Plummer, Sayeef S. Salahuddin, Lei Deng, Xin-Guo Li, Song Han, Luping Shi, Yuan Xie, Elias Yaacoub, Mohamed-Slim Alouini, Ahmed Douik, Hayssam Dahrouj, Tareq Y. Al-Naffouri:
Scanning the Issue. Proc. IEEE 108(4): 483-484 (2020) - [j9]Lei Deng, Guoqi Li, Song Han, Luping Shi, Yuan Xie:
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey. Proc. IEEE 108(4): 485-532 (2020) - [j8]Milad Mohammadi, Song Han, Ehsan Atoofian, Amirali Baniasadi, Tor M. Aamodt, William J. Dally:
Energy Efficient On-Demand Dynamic Branch Prediction Models. IEEE Trans. Computers 69(3): 453-465 (2020) - [j7]Yi Cai, Yujun Lin, Lixue Xia, Xiaoming Chen, Song Han, Yu Wang, Huazhong Yang:
Long Live TIME: Improving Lifetime and Security for NVM-Based Training-in-Memory Systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(12): 4707-4720 (2020) - [c44]Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, Song Han:
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. ACL 2020: 7675-7688 - [c43]Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, Song Han:
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy. CVPR 2020: 2075-2084 - [c42]Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han:
GAN Compression: Efficient Architectures for Interactive Conditional GANs. CVPR 2020: 5283-5293 - [c41]Hanrui Wang, Kuan Wang, Jiacheng Yang, Linxiao Shen, Nan Sun, Hae-Seung Lee, Song Han:
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning. DAC 2020: 1-6 - [c40]Zhijian Liu, Zhanghao Wu, Chuang Gan, Ligeng Zhu, Song Han:
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference. ECCV (11) 2020: 578-595 - [c39]Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, Song Han:
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. ECCV (28) 2020: 685-702 - [c38]Zhekai Zhang, Hanrui Wang, Song Han, William J. Dally:
SpArch: Efficient Architecture for Sparse Matrix Multiplication. HPCA 2020: 261-274 - [c37]Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han:
Once-for-All: Train One Network and Specialize it for Efficient Deployment. ICLR 2020 - [c36]Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han:
Lite Transformer with Long-Short Range Attention. ICLR 2020 - [c35]Han Cai, Chuang Gan, Ligeng Zhu, Song Han:
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning. NeurIPS 2020 - [c34]Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han:
MCUNet: Tiny Deep Learning on IoT Devices. NeurIPS 2020 - [c33]Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han:
Differentiable Augmentation for Data-Efficient GAN Training. NeurIPS 2020 - [p1]Ligeng Zhu, Song Han:
Deep Leakage from Gradients. Federated Learning 2020: 17-31 - [i38]Zhekai Zhang, Hanrui Wang, Song Han, William J. Dally:
SpArch: Efficient Architecture for Sparse Matrix Multiplication. CoRR abs/2002.08947 (2020) - [i37]Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han:
GAN Compression: Efficient Architectures for Interactive Conditional GANs. CoRR abs/2003.08936 (2020) - [i36]Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han:
Lite Transformer with Long-Short Range Attention. CoRR abs/2004.11886 (2020) - [i35]Hanrui Wang, Kuan Wang, Jiacheng Yang, Linxiao Shen, Nan Sun, Hae-Seung Lee, Song Han:
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning. CoRR abs/2005.00406 (2020) - [i34]Zhongxia Yan, Hanrui Wang, Demi Guo, Song Han:
MicroNet for Efficient Language Modeling. CoRR abs/2005.07877 (2020) - [i33]Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, Song Han:
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing. CoRR abs/2005.14187 (2020) - [i32]Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Song Han:
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy. CoRR abs/2006.08509 (2020) - [i31]Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han:
Differentiable Augmentation for Data-Efficient GAN Training. CoRR abs/2006.10738 (2020) - [i30]Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han:
MCUNet: Tiny Deep Learning on IoT Devices. CoRR abs/2007.10319 (2020) - [i29]Han Cai, Chuang Gan, Ligeng Zhu, Song Han:
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning. CoRR abs/2007.11622 (2020) - [i28]Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, Song Han:
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. CoRR abs/2007.16100 (2020) - [i27]Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han:
Hardware-Centric AutoML for Mixed-Precision Quantization. CoRR abs/2008.04878 (2020) - [i26]Yaoyao Ding, Ligeng Zhu, Zhihao Jia, Gennady Pekhimenko, Song Han:
IOS: Inter-Operator Scheduler for CNN Acceleration. CoRR abs/2011.01302 (2020) - [i25]Hanrui Wang, Zhekai Zhang, Song Han:
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning. CoRR abs/2012.09852 (2020)
2010 – 2019
- 2019
- [j6]Chen Pan, Mimi Xie, Song Han, Zhi-Hong Mao, Jingtong Hu:
Modeling and Optimization for Self-powered Non-volatile IoT Edge Devices with Ultra-low Harvesting Power. ACM Trans. Cyber Phys. Syst. 3(3): 32:1-32:26 (2019) - [c32]Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han:
HAQ: Hardware-Aware Automated Quantization With Mixed Precision. CVPR 2019: 8612-8620 - [c31]Zimeng Zhou, Chenchen Fu, Chun Jason Xue, Song Han:
Transmit or Discard: Optimizing Data Freshness in Networked Embedded Systems with Energy Harvesting Sources. DAC 2019: 35 - [c30]Zhenhua Zhu, Hanbo Sun, Yujun Lin, Guohao Dai, Lixue Xia, Song Han, Yu Wang, Huazhong Yang:
A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM. DAC 2019: 56 - [c29]Shulin Zeng, Yujun Lin, Shuang Liang, Junlong Kang, Dongliang Xie, Yi Shan, Song Han, Yu Wang, Huazhong Yang:
A Fine-Grained Sparse Accelerator for Multi-Precision DNN. FPGA 2019: 185 - [c28]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan Rankin, Ryan Rivera, Sioni Summers, Nhan Tran, Zhenbin Wu:
Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. FPGA 2019: 305 - [c27]Ji Lin, Chuang Gan, Song Han:
TSM: Temporal Shift Module for Efficient Video Understanding. ICCV 2019: 7082-7092 - [c26]Han Cai, Tianzhe Wang, Zhanghao Wu, Kuan Wang, Ji Lin, Song Han:
On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning. ICCV Workshops 2019: 2509-2513 - [c25]Han Cai, Ligeng Zhu, Song Han:
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. ICLR (Poster) 2019 - [c24]Ji Lin, Chuang Gan, Song Han:
Defensive Quantization: When Efficiency Meets Robustness. ICLR (Poster) 2019 - [c23]Zhongxia Yan, Hanrui Wang, Demi Guo, Song Han:
MicroNet for Efficient Language Modeling. Proceedings of Machine Learning Research 2019: 215-231 - [c22]Zhijian Liu, Haotian Tang, Yujun Lin, Song Han:
Point-Voxel CNN for Efficient 3D Deep Learning. NeurIPS 2019: 963-973 - [c21]Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh:
Park: An Open Platform for Learning-Augmented Computer Systems. NeurIPS 2019: 2490-2502 - [c20]Ligeng Zhu, Zhijian Liu, Song Han:
Deep Leakage from Gradients. NeurIPS 2019: 14747-14756 - [i24]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i23]Ji Lin, Chuang Gan, Song Han:
Defensive Quantization: When Efficiency Meets Robustness. CoRR abs/1904.08444 (2019) - [i22]Song Han, Han Cai, Ligeng Zhu, Ji Lin, Kuan Wang, Zhijian Liu, Yujun Lin:
Design Automation for Efficient Deep Learning Computing. CoRR abs/1904.10616 (2019) - [i21]Ligeng Zhu, Zhijian Liu, Song Han:
Deep Leakage from Gradients. CoRR abs/1906.08935 (2019) - [i20]Zhijian Liu, Haotian Tang, Yujun Lin, Song Han:
Point-Voxel CNN for Efficient 3D Deep Learning. CoRR abs/1907.03739 (2019) - [i19]Han Cai, Chuang Gan, Song Han:
Once for All: Train One Network and Specialize it for Efficient Deployment. CoRR abs/1908.09791 (2019) - [i18]Ji Lin, Chuang Gan, Song Han:
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos. CoRR abs/1910.00932 (2019) - 2018
- [j5]Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Jincheng Yu, Junbin Wang, Song Yao, Song Han, Yu Wang, Huazhong Yang:
Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(1): 35-47 (2018) - [c19]Yi Cai, Yujun Lin, Lixue Xia, Xiaoming Chen, Song Han, Yu Wang, Huazhong Yang:
Long live TIME: improving lifetime for training-in-memory engines by structured gradient sparsification. DAC 2018: 107:1-107:6 - [c18]Song Han, William J. Dally:
Bandwidth-efficient deep learning. DAC 2018: 147:1-147:6 - [c17]Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han:
AMC: AutoML for Model Compression and Acceleration on Mobile Devices. ECCV (7) 2018: 815-832 - [c16]Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally:
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. ICLR (Poster) 2018 - [c15]Xingyu Liu, Jeff Pool, Song Han, William J. Dally:
Efficient Sparse-Winograd Convolutional Neural Networks. ICLR (Poster) 2018 - [c14]Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu:
Path-Level Network Transformation for Efficient Architecture Search. ICML 2018: 677-686 - [i17]Xingyu Liu, Jeff Pool, Song Han, William J. Dally:
Efficient Sparse-Winograd Convolutional Neural Networks. CoRR abs/1802.06367 (2018) - [i16]Javier M. Duarte, Song Han, Philip C. Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, Zhenbin Wu:
Fast inference of deep neural networks in FPGAs for particle physics. CoRR abs/1804.06913 (2018) - [i15]Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu:
Path-Level Network Transformation for Efficient Architecture Search. CoRR abs/1806.02639 (2018) - [i14]Ji Lin, Chuang Gan, Song Han:
Temporal Shift Module for Efficient Video Understanding. CoRR abs/1811.08383 (2018) - [i13]Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han:
HAQ: Hardware-Aware Automated Quantization. CoRR abs/1811.08886 (2018) - [i12]Han Cai, Ligeng Zhu, Song Han:
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. CoRR abs/1812.00332 (2018) - [i11]Hanrui Wang, Jiacheng Yang, Hae-Seung Lee, Song Han:
Learning to Design Circuits. CoRR abs/1812.02734 (2018) - 2017
- [j4]Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han:
Research for practice: cryptocurrencies, blockchains, and smart contracts; hardware for deep learning. Commun. ACM 60(5): 48-51 (2017) - [j3]Kaiyuan Guo, Song Han, Song Yao, Yu Wang, Yuan Xie, Huazhong Yang:
Software-Hardware Codesign for Efficient Neural Network Acceleration. IEEE Micro 37(2): 18-25 (2017) - [c13]Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, William J. Dally:
Exploring the Granularity of Sparsity in Convolutional Neural Networks. CVPR Workshops 2017: 1927-1934 - [c12]Sicheng Li, Wei Wen, Yu Wang, Song Han, Yiran Chen, Hai Li:
An FPGA Design Framework for CNN Sparsification and Acceleration. FCCM 2017: 28 - [c11]Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William (Bill) J. Dally:
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA. FPGA 2017: 75-84 - [c10]Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally:
DSD: Dense-Sparse-Dense Training for Deep Neural Networks. ICLR (Poster) 2017 - [c9]Xingyu Liu, Song Han, Huizi Mao, William J. Dally:
Efficient Sparse-Winograd Convolutional Neural Networks. ICLR (Workshop) 2017 - [c8]Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally:
Trained Ternary Quantization. ICLR (Poster) 2017 - [i10]Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, William J. Dally:
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks. CoRR abs/1705.08922 (2017) - [i9]Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus T. Alley, Neil Thakur, Song Han, William J. Dally, John M. Pauly, Lei Xing:
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI. CoRR abs/1706.00051 (2017) - [i8]Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally:
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. CoRR abs/1712.01887 (2017) - 2016
- [j2]Peter Bailis, Arvind Narayanan, Andrew Miller, Song Han:
Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning. ACM Queue 14(6): 43-55 (2016) - [c7]Junbin Wang, Ke Yan, Kaiyuan Guo, Jincheng Yu, Lingzhi Sui, Song Yao, Song Han, Yu Wang:
Real-Time Pedestrian Detection and Tracking on Customized Hardware. ESTImedia 2016: 1 - [c6]Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Song Yao, Song Han, Yu Wang, Huazhong Yang:
From model to FPGA: Software-hardware co-design for efficient neural network acceleration. Hot Chips Symposium 2016: 1-27 - [c5]Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark Horowitz, Bill Dally:
Deep compression and EIE: Efficient inference engine on compressed deep neural network. Hot Chips Symposium 2016: 1-6 - [c4]Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally:
EIE: Efficient Inference Engine on Compressed Deep Neural Network. ISCA 2016: 243-254 - [c3]Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Song Yao, Song Han, Yu Wang, Huazhong Yang:
Angel-Eye: A Complete Design Flow for Mapping CNN onto Customized Hardware. ISVLSI 2016: 24-29 - [c2]Song Han, Huizi Mao, William J. Dally:
Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. ICLR 2016 - [i7]Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally:
EIE: Efficient Inference Engine on Compressed Deep Neural Network. CoRR abs/1602.01528 (2016) - [i6]Shijian Tang, Song Han:
Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features. CoRR abs/1602.01895 (2016) - [i5]Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, Kurt Keutzer:
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR abs/1602.07360 (2016) - [i4]Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Shijian Tang, Erich Elsen, Bryan Catanzaro, John Tran, William J. Dally:
DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow. CoRR abs/1607.04381 (2016) - [i3]Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William J. Dally:
ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA. CoRR abs/1612.00694 (2016) - [i2]Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally:
Trained Ternary Quantization. CoRR abs/1612.01064 (2016) - 2015
- [j1]Milad Mohammadi, Song Han, Tor M. Aamodt, William J. Dally:
On-Demand Dynamic Branch Prediction. IEEE Comput. Archit. Lett. 14(1): 50-53 (2015) - [c1]Song Han, Jeff Pool, John Tran, William J. Dally:
Learning both Weights and Connections for Efficient Neural Network. NIPS 2015: 1135-1143 - [i1]Song Han, Jeff Pool, John Tran, William J. Dally:
Learning both Weights and Connections for Efficient Neural Networks. CoRR abs/1506.02626 (2015)
Coauthor Index
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