Fetching the paper…
Reading the bibliography…
We are in the dawn of deep learning explosion for smartphones.
Embedding-based news recommendation for millions of users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1933–1942
Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017 · 1942
Earlier work this paper cites.
Towards service composition based on mashup. In 2007 IEEE Congress on Services (Services 2007) . IEEE, 332–339
Xuanzhe Liu, Yi Hui, Wei Sun, and Haiqi Liang. 2007 · 2007
Earlier work this paper cites.
The need for open source software in machine learning
SÃķren Sonnenburg, Mikio L Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert MÞller, Fernando Pereira, Carl Edward Rasmussen, et al · 2007
Earlier work this paper cites.
Diffusion of innovations
Everett M Rogers. 2010 · 2010
Earlier work this paper cites.
Improving the speed of neural networks on CPUs. In Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop , Vol. 1. 4
Vincent Vanhoucke, Andrew Senior, and Mark Z Mao. 2011 · 2011
Earlier work this paper cites.
An empirical study of learning rates in deep neural networks for speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on . IEEE, 6724–6728
Andrew Senior, Georg Heigold, Ke Yang, et al · 2013
Earlier work this paper cites.
Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel. 2014 · 2014
Earlier work this paper cites.
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems (NIPS’14) . 1269–1277
Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Generative adversarial nets. In Advances in neural information processing systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the ACM International Conference on Multimedia, MM ’14, Orlando, FL, USA, November 03 - 07, 2014 . 675–678
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, and Trevor Darrell. 2014 · 2014
Earlier work this paper cites.
Efficient mini-batch training for stochastic optimization. In Proceedings of the 20th international conference on Knowledge discovery and data mining (KDD’14) . 661–670
Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola. 2014 · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’14) . 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Deep Deural Detworks for Small Footprint Text-dependent Speaker Verification. In IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP’14) . 4052–4056
Ehsan Variani, Xin Lei, Erik McDermott, Ignacio Lopez-Moreno, and Javier Gonzalez-Dominguez. 2014 · 2014
Earlier work this paper cites.
Bayesian dark knowledge. In Advances in Neural Information Processing Systems . 3438–3446
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling. 2015 · 2015
Earlier work this paper cites.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015 · 2015
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems . 3123–3131
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. 2015 · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . 1322–1333
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision. In International Conference on Machine Learning . 1737–1746
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015 · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally. 2015 · 2015
Earlier work this paper cites.
DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers. In Proceedings of the 42nd Annual International Symposium on Computer Architecture, Portland, OR, USA, June 13-17, 2015 . 27–40
Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor N. Mudge, Ronald G. Dreslinski, Jason Mars, and Lingjia Tang. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices. In Proceedings of the 2015 International Workshop on Internet of Things towards Applications (IoT-App’15) . 7–12
Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, and Fahim Kawsar. 2015a · 2015
Earlier work this paper cites.
DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15) . 283–294
Nicholas D. Lane, Petko Georgiev, and Lorena Qendro. 2015b · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala. 2015 · 2015
Earlier work this paper cites.
MLaaS: Machine Learning as a Service. In 14th IEEE International Conference on Machine Learning and Applications, ICMLA 2015 . 896–902
Mauro Ribeiro, Katarina Grolinger, and Miriam A. M. Capretz. 2015 · 2015
Earlier work this paper cites.
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA’15) . 161–170
Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong. 2015 · 2015
Earlier work this paper cites.
Gaze-guided Object Classification Using Deep Neural Networks for Attention-based Computing. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16) . 253–256
Michael Barz and Daniel Sonntag. 2016 · 2016
Earlier work this paper cites.
Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks. In 43rd ACM/IEEE Annual International Symposium on Computer Architecture, (ISCA’16) . 367–379
Yu-Hsin Chen, Joel S. Emer, and Vivienne Sze. 2016 · 2016
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <1MB Model Size
Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. 2016 · 2016
Earlier work this paper cites.
DeepX: A Software Accelerator for Low-power Deep Learning Inference on Mobile Devices. In 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2016) . 23:1–23:12
Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar. 2016 · 2016
Earlier work this paper cites.
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android. In Proceedings of the 2016 ACM on Multimedia Conference . 1201–1205
Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, and Soheil Ghiasi. 2016 · 2016
Earlier work this paper cites.
Fast convnets using group-wise brain damage. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2554–2564
Vadim Lebedev and Victor Lempitsky. 2016 · 2016
Cited alongside, same era.
SpotGarbage: Smartphone App to Detect Garbage Using Deep Learning. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16) . 940–945
Gaurav Mittal, Kaushal B. Yagnik, Mohit Garg, and Narayanan C. Krishnan. 2016 · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings. In Security and Privacy (EuroS&P), 2016 IEEE European Symposium on . 372–387
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami. 2016 · 2016
Cited alongside, same era.
Towards Multimodal Deep Learning for Activity Recognition on Mobile Devices. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16) . 185–188
Valentin Radu, Nicholas D. Lane, Sourav Bhattacharya, Cecilia Mascolo, Mahesh K. Marina, and Fahim Kawsar. 2016 · 2016
Cited alongside, same era.
Mobile deep learning
2018 · 2018
Closest in time.
Open neural network exchange format
2018 · 2018
Closest in time.
Open Source Computer Vision Library
2018 · 2018
Closest in time.
Over Half of Smartphone Owners Use Voice Assistants
2018d · 2018
Closest in time.
Protocol Buffer
2018 · 2018
Closest in time.
SenseTime
2018 · 2018
Closest in time.
Snapdragon Neural Processing Engine
2018 · 2018
Closest in time.
Snapdragon performance
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xnor-net: Imagenet classification using binary convolutional neural networks. In European Conference on Computer Vision . 525–542
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Stealing Machine Learning Models via Prediction APIs.. In USENIX Security Symposium . 601–618
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016 · 2016
Cited alongside, same era.
Quantized Convolutional Neural Networks for Mobile Devices. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR’16) . 4820–4828
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. 2016 · 2016
Cited alongside, same era.
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
Qingchen Zhang, Laurence T. Yang, and Zhikui Chen. 2016 · 2016
Cited alongside, same era.
On the need of machine learning as a service for the internet of things. In Proceedings of the 1st International Conference on Internet of Things and Machine Learning, IML 2017 . 22:1–22:8
Davide Bacciu, Stefano Chessa, Claudio Gallicchio, and Alessio Micheli. 2017 · 2017
Cited alongside, same era.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017 · 2017
Cited alongside, same era.
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge. In Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’17) . 615–629
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor N. Mudge, Jason Mars, and Lingjia Tang. 2017 · 2017
Cited alongside, same era.
DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’17) . 82–95
Huynh Nguyen Loc, Youngki Lee, and Rajesh Krishna Balan. 2017 · 2017
Cited alongside, same era.
2018 · 2018
Closest in time.
SuperID Android SDK
2018 · 2018
Closest in time.
TensorFlow
2018 · 2018
Closest in time.
TensorFlow Lite
2018 · 2018
Closest in time.
The Machine Learning Behind Android Smart Linkify
2018e · 2018
Closest in time.
xNN deep learning framework
2018 · 2018
Closest in time.
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018 · 2018
Closest in time.
Empirical study of the topology and geometry of deep networks. In IEEE CVPR
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. 2018 · 2018
Closest in time.
Real-time Personalization using Embeddings for Search Ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 311–320
Mihajlo Grbovic and Haibin Cheng. 2018 · 2018
Closest in time.
Securing Input Data of Deep Learning Inference Systems via Partitioned Enclave Execution
Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Ankita Lamba, Dimitrios Pendarakis, and Ian Molloy. 2018 · 2018
Closest in time.
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Max Augustin, Michael Backes, and Mario Fritz. 2018 · 2018
Closest in time.
Privacy-preserving Machine Learning as a Service
Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi, and Rebecca N. Wright. 2018 · 2018
Closest in time.
On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’18) . 389–400
Sicong Liu, Yingyan Lin, Zimu Zhou, Kaiming Nan, Hui Liu, and Junzhao Du. 2018 · 2018
Closest in time.
Digital watermarking for deep neural networks
Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa, and Shin’ichi Satoh. 2018 · 2018
Closest in time.
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramer and Dan Boneh. 2018 · 2018
Closest in time.
DeepCache: Principled Cache for Mobile Deep Vision. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking . 129–144
Mengwei Xu, Mengze Zhu, Yunxin Liu, Felix Xiaozhu Lin, and Xuanzhe Liu. 2018 · 2018
Closest in time.
Protecting Intellectual Property of Deep Neural Networks with Watermarking. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security . 159–172
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy. 2018b · 2018
Closest in time.
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
Xingzhou Zhang, Yifan Wang, and Weisong Shi. 2018c · 2018
Closest in time.
An Empirical Study on TensorFlow Program Bugs
Yuhao Zhang, Yifan Chen, Shing-Chi Cheung, Yingfei Xiong, and Lu Zhang. 2018a · 2018
Closest in time.
Emoji-powered representation learning for cross-lingual sentiment classification. In The World Wide Web Conference . 251–262
Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, and Xuanzhe Liu. 2019 · 2019
Closest in time.
Moving deep learning into web browser: How far can we go?. In The World Wide Web Conference . 1234–1244
Yun Ma, Dongwei Xiang, Shuyu Zheng, Deyu Tian, and Xuanzhe Liu. 2019 · 2019
Closest in time.
A first look at deep learning apps on smartphones. In The World Wide Web Conference . 2125–2136
Mengwei Xu, Jiawei Liu, Yuanqiang Liu, Felix Xiaozhu Lin, Yunxin Liu, and Xuanzhe Liu. 2019 · 2019
Closest in time.
Artificial Intelligence Next Key Growth Area for Smartphones as Numbers Top Six Billion by 2020, IHS Markit Says
2018c · 2020
Closest in time.
A comprehensive study on challenges in deploying deep learning based software. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 750–762
Zhenpeng Chen, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, Tao Xie, and Xuanzhe Liu. 2020 · 2020
Closest in time.
Roaming Through the Castle Tunnels: An Empirical Analysis of Inter-app Navigation of Android Apps
Yun Ma, Ziniu Hu, Diandian Gu, Li Zhou, Qiaozhu Mei, Gang Huang, and Xuanzhe Liu. 2020 · 2020
Closest in time.
The Case for FPGA-based Edge Computing
Chenren Xu, Shuang Jiang, Guojie Luo, Guangyu Sun, Ning An, Gang Huang, and Xuanzhe Liu. 2020a · 2020
Closest in time.
Heterogeneity-aware federated learning
Chengxu Yang, QiPeng Wang, Mengwei Xu, Shangguang Wang, Kaigui Bian, and Xuanzhe Liu. 2020 · 2020
Closest in time.
Learning structured sparsity in deep neural networks. In Advances in Neural Information Processing Systems . 2074–2082
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016 · 2082
Closest in time.