README.md

paper note

archives

• Optimized Product Quantization[2]--2020-11-15
• Product Quantization for Nearest Neighbor Search[4]--2020-10-31
• papers to read--2020-10-27
• Video Google: A Text Retrieval Approach to Object Matching in Videos[3]--2020-10-04
• ModelHub: Lifecycle Management for Deep Learning[4]--2020-09-20

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• Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods--2020-08-16
• Automatically Tracking Metadata and Provenance of Machine Learning Experiments--2020-08-08
• Data Management Challenges in Production Machine Learning[3]--2020-07-14
• Goods: Organizing Google's Datasets[4]--2020-06-25
• Autopilot: workload autoscaling at Google[5]--2020-06-20
• A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments--2020-02-14
• DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING--2019-10-07
• Compressing Neural Networks with the Hashing Trick--2019-10-05
• Feature Hashing for Large Scale Multitask Learning--2019-10-05
• TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks[5]--2019-09-01
• Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft--2019-06-26
• Deep Learning Inference Service at Microsoft--2019-06-11
• Scaling Machine Learning as a Service--2019-04-24
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications--2019-01-13
• Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective--2018-11-17

area/RL

• Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods--2020-08-16

area/auto-scaling

• Autopilot: workload autoscaling at Google[5]--2020-06-20
• A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments--2020-02-14

area/data-management-in-ml

• Automatically Tracking Metadata and Provenance of Machine Learning Experiments--2020-08-08
• Data Management Challenges in Production Machine Learning[3]--2020-07-14

area/inference

• Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft--2019-06-26
• Deep Learning Inference Service at Microsoft--2019-06-11
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications--2019-01-13

area/large-scale-ml

• Automatically Tracking Metadata and Provenance of Machine Learning Experiments--2020-08-08
• Scaling Machine Learning as a Service--2019-04-24
• Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective--2018-11-17

area/metadata-management-in-ml

• ModelHub: Lifecycle Management for Deep Learning[4]--2020-09-20
• Automatically Tracking Metadata and Provenance of Machine Learning Experiments--2020-08-08
• Goods: Organizing Google's Datasets[4]--2020-06-25

area/model-compression

• DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING--2019-10-07
• Compressing Neural Networks with the Hashing Trick--2019-10-05
• Feature Hashing for Large Scale Multitask Learning--2019-10-05

area/similarity-search

• Optimized Product Quantization[2]--2020-11-15
• Product Quantization for Nearest Neighbor Search[4]--2020-10-31
• Video Google: A Text Retrieval Approach to Object Matching in Videos[3]--2020-10-04

area/training

• TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks[5]--2019-09-01

TODO

TODO list from papers to read--1 jobs to do--0 jobs done

• Helios: hyperscale indexing for the cloud & edge