Fetching the paper…
Reading the bibliography…
Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
Earlier work this paper cites.
Individual choice behavior
R Duncan Luce · 1959
Earlier work this paper cites.
Near-optimal machine teaching via explanatory teaching sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, and Yisong Yue · 1978
Earlier work this paper cites.
Improving human decision making through case-based decision aiding
Janet L Kolodneer · 1991
Earlier work this paper cites.
Case-based decision support system: Architecture for simulating military command and control
Shu-hsien Liao · 2000
Earlier work this paper cites.
Generalized non-metric multidimensional scaling
Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, David Kriegman, and Serge Belongie · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
A case-based decision support system for individual stress diagnosis using fuzzy similarity matching
Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, and Bo Von Schéele · 2009
Earlier work this paper cites.
Adaptively learning the crowd kernel
Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, and Adam Tauman Kalai · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Stochastic triplet embedding
Laurens Van Der Maaten and Kilian Weinberger · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Uniqueness of ordinal embedding
Matthäus Kleindessner and Ulrike Luxburg · 2014
Earlier work this paper cites.
Near-optimally teaching the crowd to classify
Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, and Andreas Krause · 2014
Earlier work this paper cites.
Local ordinal embedding
Yoshikazu Terada and Ulrike Luxburg · 2014
Earlier work this paper cites.
Preference completion: Large-scale collaborative ranking from pairwise comparisons
Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, and Inderjit Dhillon · 2015
Earlier work this paper cites.
Learning local feature descriptors with triplets and shallow convolutional neural networks
Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
Cited alongside, same era.
In two moves, alphago and lee sedol redefined the future. wired, 2016
C Metz, C Metz, N Tiku, I Lapowsky, K Finley, C Thompson, E Griffith, and M Spector · 2016
Cited alongside, same era.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
An overview of machine teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles, and Anna N Rafferty · 2018
Later among the works it cites.
Pytorch lightning, 2019
William Falcon et al · 2019
Later among the works it cites.
Landmark ordinal embedding
Nikhil Ghosh, Yuxin Chen, and Yisong Yue · 2019
Later among the works it cites.
The principles and limits of algorithm-in-the-loop decision making
Ben Green and Yiling Chen · 2019
Later among the works it cites.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Later among the works it cites.
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Vivian Lai and Chenhao Tan · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
Can ai become reliable source to support human decision making in a court scene?
Yugo Hayashi and Kosuke Wakabayashi · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Kernel functions based on triplet comparisons
Matthäus Kleindessner and Ulrike von Luxburg · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
“why is ‘chicago’ deceptive?” towards building model-driven tutorials for humans
Vivian Lai, Han Liu, and Chenhao Tan · 2020
Later among the works it cites.
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L. Ball, Marc Mendelson, Gary Maartens, Daniël J. van Hoving, Rulan Griesel, Andrew Y. Ng, Tom H. Boyles, and Matthew P. Lungren · 2020
Later among the works it cites.
Human–computer collaboration for skin cancer recognition
Philipp Tschandl, Christoph Rinner, Zoe Apalla, Giuseppe Argenziano, Noel Codella, Allan Halpern, Monika Janda, Aimilios Lallas, Caterina Longo, Josep Malvehy, John Paoli, Susana Puig, Cliff Rosendahl, H. Peter Soyer, Iris Zalaudek, and Harald Kittler · 2020
Later among the works it cites.
Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Towards a science of human-ai decision making: a survey of empirical studies
Vivian Lai, Chacha Chen, Q Vera Liao, Alison Smith-Renner, and Chenhao Tan · 2021
Later among the works it cites.
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen · 2021
Later among the works it cites.
Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making
Xinru Wang and Ming Yin · 2021
Later among the works it cites.
Visual correspondence-based explanations improve ai robustness and human-ai team accuracy
Mohammad Reza Taesiri, Giang Nguyen, and Anh Nguyen · 2022
Later among the works it cites.