Fetching the paper…
Reading the bibliography…
Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
Earlier work this paper cites.
Galaxy zoo: morphologies derived from visual inspection of galaxies from the sloan digital sky survey
Chris J. Lintott, Kevin Schawinski, Anže Slosar, Kate R. Land, Steven Bamford, Daniel I. Thomas, M. Jordan Raddick, Robert Nichol, Alexander S. Szalay, Daniel Andreescu, P. G. Murray, and Jan van den Berg · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Glove: global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
An empirical study into annotator agreement, ground truth estimation, and algorithm evaluation
Thomas A Lampert, André Stumpf, and Pierre Gançarski · 2016
Earlier work this paper cites.
Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael W. Macy, and Ingmar Weber · 2017
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin M. Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun · 2017
Earlier work this paper cites.
Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers · 2017
Earlier work this paper cites.
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
Alexandra Chouldechova, Diana Benavides Prado, Oleksandr Fialko, and Rhema Vaithianathan · 2018
Cited alongside, same era.
Predict responsibly: improving fairness and accuracy by learning to defer
David Madras, Toniann Pitassi, and Richard S. Zemel · 2018
Cited alongside, same era.
Umap: uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
Cited alongside, same era.
Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in india
Varun Gulshan, Renu P. Rajan, Kasumi Widner, Derek J. Wu, Peter Wubbels, Tyler Rhodes, Kira Whitehouse, Marc Coram, Greg S Corrado, Kim Ramasamy, Rajiv Raman, Lily H. Peng, and Dale R. Webster · 2019
Cited alongside, same era.
Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, et al · 2019
Consistent estimators for learning to defer to an expert
Hussein Mozannar and David A. Sontag · 2020
Later among the works it cites.
Learning to complement humans
Bryan Wilder, Eric Horvitz, and Ece Kamar · 2020
Later among the works it cites.
Is the most accurate ai the best teammate? optimizing ai for teamwork
Gagan Bansal, Besmira Nushi, Ece Kamar, Eric Horvitz, and Daniel S. Weld · 2021
Later among the works it cites.
Classification under human assistance
Abir De, Nastaran Okati, Ali Zarezade, and Manuel Gomez-Rodriguez · 2021
Later among the works it cites.
Human-ai collaboration with bandit feedback
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, and Matthew Lease · 2021
Later among the works it cites.
Human-ai complementarity in hybrid intelligence systems: a structured literature review
Patrick Hemmer, Max Schemmer, Michael Vössing, and Niklas Kühl · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Human uncertainty makes classification more robust
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, and Olga Russakovsky · 2019
Cited alongside, same era.
The algorithmic automation problem: prediction, triage, and human effort
Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan · 2019
Cited alongside, same era.
Augmenting the algorithm: emerging human-in-the-loop work configurations
Tor Grønsund and Margunn Aanestad · 2020
Cited alongside, same era.
Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
Anna Majkowska, Sid Mittal, David F Steiner, Joshua J Reicher, Scott Mayer McKinney, Gavin E Duggan, Krish Eswaran, Po-Hsuan Cameron Chen, Yun Liu, Kalidindi, et al · 2020
Cited alongside, same era.
Later among the works it cites.
Towards unbiased and accurate deferral to multiple experts
Vijay Keswani, Matthew Lease, and Krishnaram Kenthapadi · 2021
Later among the works it cites.
Differentiable learning under triage
Nastaran Okati, Abir De, and Manuel Rodriguez · 2021
Later among the works it cites.