Proceedings of Machine Learning and Systems 1 (MLSys 2019)
Edited by:
A. Talwalkar and V. Smith and M. Zaharia
Kernel Machines That Adapt To Gpus For Effective Large Batch Training Siyuan Ma, Mikhail Belkin TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning Akshay Agrawal, Akshay Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video Huizi Mao, Taeyoung Kong, bill dally Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD Jianyu Wang, Gauri Joshi TensorFlow.js: Machine Learning For The Web and Beyond Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Charles Nicholson, Nick Kreeger, Ping Yu, Shanqing Cai, Eric Nielsen, David Soegel, Stan Bileschi, Michael Terry, Ann Yuan, Kangyi Zhang, Sandeep Gupta, Sarah Sirajuddin, D Sculley, Rajat Monga, Greg Corrado, Fernanda Viegas, Martin M Wattenberg Optimizing DNN Computation with Relaxed Graph Substitutions Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning Paul Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Venkataramanaiah, Jae-sun Seo, Matthew Mattina AG: Imperative-style Coding with Graph-based Performance Dan Moldovan, James Decker, Fei Wang, Andrew Johnson, Brian Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko Priority-based Parameter Propagation for Distributed DNN Training Anand Jayarajan, Jinliang Wei, Garth Gibson, Alexandra Fedorova, Gennady Pekhimenko AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation Georgios Damaskinos, El-Mahdi El-Mhamdi, Rachid Guerraoui, Arsany Guirguis, Sébastien Rouault Mini-batch Serialization: CNN Training with Inter-layer Data Reuse Sangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification Qi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit S. Dhillon, Michael J Witbrock Accurate and Efficient 2-bit Quantized Neural Networks Jungwook Choi, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang RLgraph: Modular Computation Graphs for Deep Reinforcement Learning Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki Restructuring Batch Normalization to Accelerate CNN Training Wonkyung Jung, Daejin Jung, Byeongho Kim, Sunjung Lee, Wonjong Rhee, Jung Ho Ahn Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator Tian Zhao, Yaqi Zhang, Kunle Olukotun Pytorch-BigGraph: A Large Scale Graph Embedding System Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich TicTac: Accelerating Distributed Deep Learning with Communication Scheduling Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy Campbell Full Deep Neural Network Training On A Pruned Weight Budget Mieszko Lis, Maximilian Golub, Guy Lemieux Beyond Data and Model Parallelism for Deep Neural Networks. Zhihao Jia, Matei Zaharia, Alex Aiken BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy Minsik Cho, Ulrich Finkler, David Kung, Hillery Hunter Data Validation for Machine Learning Neoklis Polyzotis, Martin Zinkevich, Sudip Roy, Eric Breck, Steven Whang AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling Ting-Wu Chin, Ruizhou Ding, Diana Marculescu YellowFin and the Art of Momentum Tuning Jian Zhang, Ioannis Mitliagkas Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications Dibakar Gope, Ganesh Dasika, Matthew Mattina Bandana: Using Non-Volatile Memory for Storing Deep Learning Models Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti To Compress Or Not To Compress: Understanding The Interactions Between Adversarial Attacks And Neural Network Compression Ilia Shumailov, Yiren Zhao, Robert Mullins, Ross Anderson Towards Federated Learning at Scale: System Design Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander Scaling Video Analytics on Constrained Edge Nodes Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G Andersen, Michael Kaminsky, Subramanya Dulloor Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang 3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning Hyeontaek Lim, David G Andersen, Michael Kaminsky Parmac: Distributed Optimisation Of Nested Functions, With Application To Learning Binary Autoencoders Miguel A Carreira-Perpinan, Mehdi Alizadeh