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Indicates the beginning of a session

 Mon Mar 02, 2020

Time Ballroom A Ballroom B Ballroom B + C
08:00 AM (Orals)
Distributed and Parallel Learning Algorithms
A System for Massively Parallel Hyperparameter Tuning
08:25 AM (Orals)
PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud
08:50 AM (Orals)
Federated Optimization in Heterogeneous Networks
09:00 AM (Breaks)
Breakfast and Registration
09:15 AM (Orals)
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
09:40 AM (Orals)
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
09:45 AM (Breaks)
Opening Remarks
10:30 AM (Orals)
Efficient Model Training
Resource Elasticity in Distributed Deep Learning
10:55 AM (Orals)
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems
11:20 AM (Orals)
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks
11:45 AM (Orals)
Breaking the Memory Wall with Optimal Tensor Rematerialization
01:30 PM (Invited Talks)
Theory and Systems for Weak Supervision
02:30 PM (Orals)
Efficient Inference and Model Serving
What is the State of Neural Network Pruning?
02:55 PM (Orals)
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
03:20 PM (Orals)
MNN: A Universal and Efficient Inference Engine
03:45 PM (Orals)
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
04:30 PM (Orals)
Model / Data Quality and Privacy
Attention-based Learning for Missing Data Imputation in HoloClean
04:55 PM (Orals)
Privacy-Preserving Bandits
05:20 PM (Orals)
Understanding the Downstream Instability of Word Embeddings
05:45 PM (Orals)
Model Assertions for Monitoring and Improving ML Models
06:00 PM (Demonstrations)
Demonstrations
06:10 PM (Demonstrations)
Demonstration Previews
06:30 PM (Posters)
Posters and Reception

 Tue Mar 03, 2020

Time Ballroom A Ballroom B
08:00 AM (Orals)
ML programming models and abstractions & ML applied to systems
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
08:25 AM (Orals)
Automatically batching control-intensive programs for modern accelerators
08:50 AM (Orals)
Predictive Precompute with Recurrent Neural Networks
09:00 AM (Breaks)
Breakfast and Registration
09:15 AM (Orals)
Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation
09:40 AM (Orals)
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
10:30 AM (Orals)
Efficient inference and model serving
Fine-Grained GPU Sharing Primitives for Deep Learning Applications
10:55 AM (Orals)
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
11:20 AM (Orals)
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator
11:45 AM (Orals)
PoET-BiN: Power Efficient Tiny Binary Neurons
01:30 PM (Invited Talks)
The Emerging Role of Cryptography in Trustworthy AI
02:30 PM (Orals)
Quantization of deep neural networks
Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers
02:55 PM (Orals)
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
03:20 PM (Orals)
Riptide: Fast End-to-End Binarized Neural Networks
03:45 PM (Orals)
Searching for Winograd-aware Quantized Networks
04:30 PM (Orals)
Efficient Model Training 2
Blink: Fast and Generic Collectives for Distributed ML
04:55 PM (Orals)
A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms
05:20 PM (Orals)
MotherNets: Rapid Deep Ensemble Learning
05:45 PM (Orals)
MLPerf Training Benchmark
08:10 PM (Breaks)
Closing Remarks