Fetching the paper…
Reading the bibliography…
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label.
Generalized benders decomposition
Arthur M Geoffrion · 1972
Earlier work this paper cites.
Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
Earlier work this paper cites.
Dynamic trees as search trees via euler tours, applied to the network simplex algorithm
Robert E Tarjan · 1997
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Scenario reduction algorithms in stochastic programming
Holger Heitsch and Werner Römisch · 2003
Earlier work this paper cites.
Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
Earlier work this paper cites.
CBC user guide
John Forrest and Robin Lougee-Heimer · 2005
Earlier work this paper cites.
Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
Earlier work this paper cites.
Active learning via transductive experimental design
Kai Yu, Jinbo Bi, and Volker Tresp · 2006
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Displacement interpolation using lagrangian mass transport
Nicolas Bonneel, Michiel Van De Panne, Sylvain Paris, and Wolfgang Heidrich · 2011
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Cited alongside, same era.
Facility location: concepts, models, algorithms and case studies. Series: Contributions to Management Science: edited by Zanjirani Farahani, Reza and Hekmatfar, Masoud, Heidelberg, Germany, Physica-Verlag, 2011
Gert W Wolf · 2011
Cited alongside, same era.
Active learning
Burr Settles · 2012
Cited alongside, same era.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Cited alongside, same era.
State of the art in global optimization: Computational methods and applications , volume 7
The benders decomposition algorithm: A literature review
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, and Walter Rei · 2017
Later among the works it cites.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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 · 2017
Later among the works it cites.
Mark Woodward and Chelsea Finn · 2017
Later among the works it cites.
Active learning and input space analysis for deep networks
Mélanie Ducoffe · 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…
Christodoulos A Floudas and Panos M Pardalos · 2013
Cited alongside, same era.
Adaptive active learning for image classification
Xin Li and Yuhong Guo · 2013
Cited alongside, same era.
Fast computation of wasserstein barycenters
Marco Cuturi and Arnaud Doucet · 2014
Cited alongside, same era.
A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning under distributed weak supervision
Martin Rajchl, Matthew CH Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, and Daniel Rueckert · 2016
Cited alongside, same era.
Inexact stabilized benders’ decomposition approaches with application to chance-constrained problems with finite support
Wim van Ackooij, Antonio Frangioni, and Welington de Oliveira · 2016
Cited alongside, same era.
Differential properties of sinkhorn approximation for learning with wasserstein distance
Giulia Luise, Alessandro Rudi, Massimiliano Pontil, and Carlo Ciliberto · 2018
Later among the works it cites.
Scenario reduction revisited: Fundamental limits and guarantees
Napat Rujeerapaiboon, Kilian Schindler, Daniel Kuhn, and Wolfram Wiesemann · 2018
Later among the works it cites.
Massively scalable sinkhorn distances via the nyström method
Jason Altschuler, Francis Bach, Alessandro Rudi, and Jonathan Niles-Weed · 2019
Later among the works it cites.
Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
Later among the works it cites.
Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, and Guillaume Gravier · 2019
Later among the works it cites.
Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
Later among the works it cites.
Deep active learning: Unified and principled method for query and training
Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang · 2020
Later among the works it cites.
f-domain adversarial learning: Theory and algorithms
David Acuna, Guojun Zhang, Marc T. Law, and Sanja Fidler · 2021
Closest in time.
Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
Closest in time.