SysML 2019: Jianyu Wang, Adaptive Communication Strategies in Local-Update SGD

SysML 19: Dan Moldovan, AutoGraph: Imperative-style Coding with Graph-based Performance

SysML 19: Daniel Smilkov, TensorFlow.js: Machine Learning for the Web and Beyond

SysML 19: Adam Lerer, Pytorch-BigGraph: A Large Scale Graph Embedding System

SysML 19: Assaf Eisenman, Bandana: Using Non-Volatile Memory for Storing Deep Learning Models

SysML 19: Akshay Agrawal, TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning

SysML 19 Keynote: Maya Gupta, How do we make AI fair?

SysML 19: Closing Remarks, Ameet Talwalkar

SysML 19: Sven Mika, RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

SysML 19: Paul Whatmough, FixyNN

SysML 19: Dibakar Gope, Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

SysML 19: Demo Previews

SysML 19: Anand Jayarajan, Priority-based Parameter Propagation for Distributed DNN Training

SysML 19: Christopher Canel, Scaling Video Analytics on Constrained Edge Nodes

SysML 19: Cedric Renggli, Continuous Integration of Machine Learning Models with ease.ml/ci

SysML 19: Georgios Damaskinos, AggregaThor

SysML 19: Huizi Mao, CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video

SysML 19: Jia Zhihao, Beyond Data and Model Parallelism for Deep Neural Networks

SysML 19: Jian Zhang, YellowFin and the Art of Momentum Tuning

SysML 19: Jungwook Choi, Accurate and Efficient 2-bit Quantized Neural Networks

SysML 19: Lei Qi, Discrete Adversarial Attacks and Submodular Optimization

SysML 19: Martin Zinkevich, Data Validation for Machine Learning

SysML 19: Matei Zaharia, Opening Remarks

SysML 19: Maximilian Golub, Full Deep Neural Network Training on a Pruned Weight Budget

SysML 19: Miguel Mehdi, ParMAC: Distributed Optimisation of Nested Functions

SysML 19: Minsik Cho, BlueConnect

SysML 19 Keynote: Ofer Dekel, Compiling AI for the Edge

SysML 19: Sangkug Lym, Mini-batch Serialization: CNN Training with Inter-layer Data Reuse

SysML 19: Sayed Hadi Hashemi, TicTac

SysML 19: Siyuan Ma, Kernel machines that adapt to GPUs for effective large batch training

SysML 19: Tian Zhao, Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator

SysML 19: Ting-Wu Chin, AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling

SysML 19: Wolfgang Grieskamp, Towards Federated Learning at Scale: System Design

SysML 19: Wonkyung Jung, Restructuring Batch Normalization to Accelerate CNN Training

SysML 19: Yiren Zhao, To compress or not to compress

SysML 19: Zhihao Jia, Optimizing DNN Computation with Relaxed Graph Substitutions

SysML 19: Ameet Talwalkar, Opening Remarks

SysML 18: Ameet Talwalkar, Opening Remarks

SysML 18: Jeff Dean, Systems and Machine Learning Symbiosis

SysML 18: Thibault Sellam, Inspecting the Internal Logic of Recurrent Neural Networks

SysML 18: Matteo Interlandi, Towards High-Performance Prediction Serving Systems

SysML 18: Vivienne Sze, Limitations of Energy-Efficient Design Approaches for Deep Neural Networks

SysML 18: Lise Getoor, Structured ML: Opportunities and Challenges for the SysML Community

SysML 18: Dawn Song, Security, Privacy, and Democratization

SysML 18: Ioannis Mitliagkas, YellowFin: Adaptive Optimization for (A)synchronous Systems

SysML 18: Bill Dally, Hardware for Deep Learning

SysML 18: Jonathan Binas, Analog electronic deep networks for fast and efficient inference

SysML 18: Arya Mazumdar, Robust Gradient Descent via Moment Encoding with LDPC Codes

SysML 18: Michael Jordan, Perspectives and Challenges

SysML 18: Tianqi Chen, TVM: End-to-End Compilation Stack for Deep Learning