Fetching the paper…
Reading the bibliography…
We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning.
Elementary applied statistics: for students in behavioral science
Linton C Freeman · 1965
Earlier work this paper cites.
An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
Earlier work this paper cites.
A new outlook on shannon’s information measures
Raymond W Yeung · 1991
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Active learning: theory and applications , volume 1
Simon Tong · 2001
Earlier work this paper cites.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
Gediminas Adomavicius and Alexander Tuzhilin · 2005
Earlier work this paper cites.
Batch mode active learning and its application to medical image classification
Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu · 2006
Earlier work this paper cites.
On learning, representing, and generalizing a task in a humanoid robot
Sylvain Calinon, Florent Guenter, and Aude Billard · 2007
Earlier work this paper cites.
Discriminative batch mode active learning
Yuhong Guo and Dale Schuurmans · 2008
Earlier work this paper cites.
Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies
Andreas Krause, Ajit Singh, and Carlos Guestrin · 2008
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.
Batch active learning via coordinated matching
Javad Azimi, Alan Fern, Xiaoli Zhang-Fern, Glencora Borradaile, and Brent Heeringa · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Cited alongside, same era.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
Cited alongside, same era.
Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Later among the works it cites.
Actively learning what makes a discrete sequence valid
David Janz, Jos van der Westhuizen, and José Miguel Hernández-Lobato · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2017
Later among the works it cites.
Cinic-10 is not imagenet or cifar-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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.
Batch Bayesian optimization via local penalization
Javier González, Zhenwen Dai, Philipp Hennig, and Neil Lawrence · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Predictive entropy search for multi-objective Bayesian optimization
Daniel Hernández-Lobato, Jose Hernandez-Lobato, Amar Shah, and Ryan Adams · 2016
Cited alongside, same era.
Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Later among the works it cites.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, and Animashree Anandkumar · 2018
Later among the works it cites.
Aditya Siddhant and Zachary C Lipton · 2018
Later among the works it cites.
Asynchronous batch Bayesian optimisation with improved local penalisation
Ahsan S Alvi, Binxin Ru, Jan Calliess, Stephen J Roberts, and Michael A Osborne · 2019
Closest in time.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Closest in time.
Variational adversarial active learning
Trevor Darrell Samarth Sinha, Sayna Ebrahimi · 2019
Closest in time.