Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber
Data / MLEngineering Uncertainty Estimation in Neural Networks for Time Series Prediction at UberSeptember 6, 2017 / Global Accurate time series forecasting during high variance segments (e.g., holidays and sporting events) is critical for anomaly detection, resource allocation, budget planning, and other related tasks necessary to facilitate optimal Uber user experiences at scale. Forecasting the
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Machine learning (ML) is widely used across the Uber platform to support intelligent decision making and forecasting for features such as ETA prediction and fraud detection. For optimal results, we invest a lot of resources in developing accurate predictive ML models. I
Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language
EngineeringUber AI Labs Open Sources Pyro, a Deep Probabilistic Programming LanguageNovember 3, 2017 / Global Achieving Uber’s goal of bringing reliable transportation to everyone requires effortless prediction and optimization at every turn. Opportunities range from matching riders to drivers, to suggesting optimal routes, finding sensible pool combinations, and even creating the next generation
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Introducing LCA: Loss Change Allocation for Neural Network Training
