Fetching the paper…
Reading the bibliography…
Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems.
Pattern recognition and machine learning (information science and statistics) springer-verlag new york
Christopher M Bishop · 2006
Earlier work this paper cites.
Human activity recognition and pattern discovery
Eunju Kim, Sumi Helal, and Diane Cook · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Gaussian mixture based hmm for human daily activity recognition using 3d skeleton features
Lasitha Piyathilaka and Sarath Kodagoda · 2013
Earlier work this paper cites.
Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David M Blei · 2013
Earlier work this paper cites.
A tutorial on human activity recognition using body-worn inertial sensors
Andreas Bulling, Ulf Blanke, and Bernt Schiele · 2014
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Cited alongside, same era.
Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
Cnn architectures for large-scale audio classification
Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al · 2017
Later among the works it cites.
The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Temporal relational reasoning in videos
Bolei Zhou, Alex Andonian, and Antonio Torralba · 2017
Later among the works it cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
Cited alongside, same era.
Uncertainty in deep learning
Yarin Gal · 2016
Cited alongside, same era.
Modelling uncertainty in deep learning for camera relocalization
Alex Kendall and Roberto Cipolla · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2017
Later among the works it cites.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Later among the works it cites.
Moments in time dataset: one million videos for event understanding
Mathew Monfort, Bolei Zhou, Sarah Adel Bargal, Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, et al · 2018
Closest in time.
Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
Closest in time.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
Closest in time.