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Learn about our engineering process, experiments, and discoveries.

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Insights Deep learning on dynamic graphs

By Emanuele Rossi and Michael Bronstein on

Insights What Twitter learned from the Recsys 2020 Challenge

By Wenzhe Shi and Luca Belli on

Insights Distributed training of sparse ML models — Part 3: Observed speedups

By Andrew Bean and Ricardo Cervera-Navarro on

Insights Distributed training of sparse ML models — Part 2: Optimized strategies

By Andrew Bean on

Insights Distributed training of sparse ML models — Part 1: Network bottlenecks

By Andrew Bean on

Insights Graph ML at Twitter

By Michael Bronstein on

Insights A new approach: Metric learning for SplitNet

By Hongjian Wang on

Insights Using machine learning to predict the value of ad requests

By Deepak Dilipkumar and Corbin Betheldo on

Insights One pattern to rule them all

By Matt Gross and ‎@drballstothewal‎ on

Insights Improving engagement on digital ads with delayed feedback

By Justina Chen and Ira Ktena on

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Cortex, machine learning, optimization, diversity, ads, events, analytics, research, scale, experiments, women in tech, data

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