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MLSys 2020 Tentative Schedule Overview 

Monday March 2nd

  • 7:00 - 7:45 am Breakfast & Registration
  • 7:45 - 8:00 am Opening Remarks
  • 8:00 - 10:05 am Session 1 (5 papers): Distributed and parallel learning algorithms
    • A System for Massively Parallel Hyperparameter Tuning
    • PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud
    • Federated Optimization in Heterogeneous Networks
    • BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
    • Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
  • 10:05 - 10:30 am Coffee Break
  • 10:30 - 12:10 pm Session 2 (4 papers): Efficient model training
    • Resource Elasticity in Distributed Deep Learning
    • SLIDE: Training Deep Neural Networks with Large Outputs on a CPU faster than a V100-GPU
    • FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks
    • Breaking the Memory Wall with Optimal Tensor Rematerialization
  • 12:10 - 1:30 pm Lunch on your own
  • 1:30 - 2:30 pm Keynote: Chris Ré: Theory and Systems for Weak Supervision
  • 2:30 - 4:10 pm Session 3 (4 papers): Efficient inference and model serving
    • What is the State of Neural Network Pruning?
    • SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
    • MNN: A Universal and Efficient Inference Engine
    • OptX: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
  • 4:10 - 4:30 pm Coffee Break
  • 4:30 - 6:10 pm Session 4 (4 papers): Model/Data Quality and Privacy
    • AimNet: Attention-based Learning for Missing Data Imputation
    • Privacy-Preserving Bandits
    • Understanding the Downstream Instability of Word Embeddings
    • Model Assertions for Monitoring and Improving ML Models
  • 6:10 - 6:15 pm Demo Previews
  • 6:30 - 9:00 pm Posters, Demos, & Reception (dinner + drinks)

Tuesday March 3rd

  • 7:00 - 8:00 am Breakfast & Registration
  • 8:00 - 10:05 am Session 5 (5 papers): ML programming models and abstractions & ML applied to systems
    • Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
    • Automatically batching control-intensive programs for modern accelerators
    • Predictive Precompute with Recurrent Neural Networks
    • Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation
    • Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
  • 10:05 - 10:30 am Coffee Break
  • 10:00 - 12:10 pm Session 6: Efficient inference and model serving
    • Fine-Grained GPU Sharing Primitives for Deep Learning Applications
    • Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
    • OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator
    • PoET-BiN: Power Efficient Tiny Binary Neurons
  • 12:10 - 1:30 pm Lunch on your own
  • 1:30 - 2:30 pm Keynote: Shafi Goldwasser: The Emerging Role of Cryptography in Trustworthy AI
  • 2:30 - 4:10 pm Session 7: Quantization of deep neural networks
    • Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers
    • Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
    • Riptide: Fast End-to-End Binarized Neural Networks
    • Searching for Winograd-aware Quantized Networks
  • 4:10 - 4:30 pm Coffee Break
  • 4:30 - 6:00 pm Session 8: Efficient model training
    • Blink: Fast and Generic Collectives for Distributed ML
    • A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms
    • MotherNets: Rapid Deep Ensemble Learning
    • MLPerf Training Benchmark
  • 6:10 - 6:15 pm Closing Remarks & MLSys 2021