ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2003.01753
  4. Cited By
Uncertainty Quantification for Deep Context-Aware Mobile Activity
  Recognition and Unknown Context Discovery

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

3 March 2020
Zepeng Huo
Arash Pakbin
Xiaohan Chen
N. Hurley
Ye Yuan
Xiaoning Qian
Zhangyang Wang
Shuai Huang
B. Mortazavi
    HAI
ArXivPDFHTML

Papers citing "Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery"

3 / 3 papers shown
Title
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and
  Temporal Relatedness
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
Zepeng Huo
Taowei Ji
Yi Liang
Shuai Huang
Zhangyang Wang
Xiaoning Qian
Bobak J. Mortazavi
AI4TS
41
1
0
26 Sep 2022
An Uncertainty-aware Loss Function for Training Neural Networks with
  Calibrated Predictions
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions
Afshar Shamsi
Hamzeh Asgharnezhad
AmirReza Tajally
Saeid Nahavandi
Henry Leung
UQCV
46
6
0
07 Oct 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
1