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
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
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective--2018-11-17
TODO list from papers to read--1 jobs to do--0 jobs done