Table of Contents

• Restructuring Batch Normalization to Accelerate CNN TrainingWonkyung Jung, Daejin Jung, Byeongho Kim, Sunjung Lee, Wonjong Rhee, Jung Ho Ahn. [doi]
• Data Validation for Machine LearningEric Breck, Neoklis Polyzotis, Sudip Roy 0002, Steven Whang, Martin Zinkevich. [doi]
• TicTac: Accelerating Distributed Deep Learning with Communication SchedulingSayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell. [doi]
• Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical TreatmentCédric Renggli, Bojan Karlas, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu 0001, Ce Zhang. [doi]
• Optimizing DNN Computation with Relaxed Graph SubstitutionsZhihao Jia, James J. Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken. [doi]
• Discrete Adversarial Attacks and Submodular Optimization with Applications to Text ClassificationQi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit S. Dhillon, Michael J. Witbrock. [doi]
• Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT ApplicationsDibakar Gope, Ganesh Dasika, Matthew Mattina. [doi]
• Priority-based Parameter Propagation for Distributed DNN TrainingAnand Jayarajan, Jinliang Wei, Garth Gibson, Alexandra Fedorova, Gennady Pekhimenko. [doi]
• RLgraph: Modular Computation Graphs for Deep Reinforcement LearningMichael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki. [doi]
• AdaScale: Towards Real-time Video Object Detection using Adaptive ScalingTing-Wu Chin, Ruizhou Ding, Diana Marculescu. [doi]
• CaTDet: Cascaded Tracked Detector for Efficient Object Detection from VideoHuizi Mao, Taeyoung Kong, Bill Dally. [doi]
• Scaling Video Analytics on Constrained Edge NodesChristopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky, Subramanya Dulloor. [doi]
• FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer LearningPaul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina. [doi]
• TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learningAkshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai. [doi]
• Pytorch-BigGraph: A Large Scale Graph Embedding SystemAdam Lerer, Ledell Wu, Jiajun Shen, Timothée Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich. [doi]
• TensorFlow.js: Machine Learning For The Web and BeyondDaniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viégas, Martin Wattenberg. [doi]
• YellowFin and the Art of Momentum TuningJian Zhang, Ioannis Mitliagkas. [doi]
• Beyond Data and Model Parallelism for Deep Neural NetworksZhihao Jia, Matei Zaharia, Alex Aiken. [doi]
• Full Deep Neural Network Training On A Pruned Weight BudgetMaximilian Golub, Guy Lemieux, Mieszko Lis. [doi]
• Kernel Machines That Adapt To Gpus For Effective Large Batch TrainingSiyuan Ma, Mikhail Belkin. [doi]
• Parmac: Distributed Optimisation Of Nested Functions, With Application To Learning Binary AutoencodersMiguel Á. Carreira-Perpiñán, Mehdi Alizadeh. [doi]
• Towards Federated Learning at Scale: System DesignKeith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander. [doi]
• Accurate and Efficient 2-bit Quantized Neural NetworksJungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang. [doi]
• Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDJianyu Wang, Gauri Joshi. [doi]
• AGGREGATHOR: Byzantine Machine Learning via Robust Gradient AggregationGeorgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis, Sébastien Rouault. [doi]
• Mini-batch Serialization: CNN Training with Inter-layer Data ReuseSangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez. [doi]
• Serving Recurrent Neural Networks Efficiently with a Spatial AcceleratorTian Zhao, Yaqi Zhang, Kunle Olukotun. [doi]
• AG: Imperative-style Coding with Graph-based PerformanceDan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko. [doi]
• Bandana: Using Non-Volatile Memory for Storing Deep Learning ModelsAssaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim M. Hazelwood, Asaf Cidon, Sachin Katti. [doi]
• To Compress Or Not To Compress: Understanding The Interactions Between Adversarial Attacks And Neural Network CompressionYiren Zhao, Ilia Shumailov, Robert D. Mullins, Ross Anderson 0001. [doi]
• BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network HierarchyMinsik Cho, Ulrich Finkler, David S. Kung 0001, Hillery C. Hunter. [doi]
• 3LC: Lightweight and Effective Traffic Compression for Distributed Machine LearningHyeontaek Lim, David G. Andersen, Michael Kaminsky. [doi]