output
20142026
most citedDeep and Confident Prediction for Time Series at Uber

353 citations

Showing 2017Show all

5 papers · 1 filter

cs.PL201764 cited

Denotational validation of higher-order Bayesian inference

Adam Ścibior, Ohad Kammar, Matthijs Vákár +7

We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inferenc…

stat.ML2017353 cited

Deep and Confident Prediction for Time Series at Uber

Lingxue Zhu, Nikolay Laptev

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting…

stat.ML2017226 cited

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

Maithra Raghu, Justin Gilmer, Jason Yosinski +1

We propose a new technique, Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine trans…

cs.CV201793 cited

Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI

Saifeng Liu, Huaixiu Zheng, Yesu Feng +1

A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MR…

cs.AI20177 cited

Space-Time Graph Modeling of Ride Requests Based on Real-World Data

Abhinav Jauhri, Brian Foo, Jerome Berclaz +4

This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a g…