Fetching the paper…
Reading the bibliography…
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings.
An Improved Bonferroni Procedure for Multiple Tests of Significance
R John Simes · 1986
Earlier work this paper cites.
Multiple Significance Tests: The Bonferroni Method
J Martin Bland and Douglas G Altman · 1995
Earlier work this paper cites.
Columbia Object Image Library (COIL-100)
Sameer A Nene, Shree K Nayar, and Hiroshi Murase · 1996
Earlier work this paper cites.
Gradient-Based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
LOF: Identifying Density-Based Local Outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
Earlier work this paper cites.
Support Vector Method for Novelty Detection
Bernhard Schölkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt · 2000
Earlier work this paper cites.
Improving Predictive Inference Under Covariate Shift by Weighting the Log-Likelihood Function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
Earlier work this paper cites.
Truncated Product Method for Combining p p -Values
Dmitri V Zaykin, Lev A Zhivotovsky, Peter H Westfall, and Bruce S Weir · 2002
Earlier work this paper cites.
Database-Friendly Random Projections: Johnson-Lindenstrauss with Binary Coins
Dimitris Achlioptas · 2003
Earlier work this paper cites.
Novelty Detection: A Review: Part 1: Statistical Approaches
Markos Markou and Sameer Singh · 2003
Earlier work this paper cites.
A Systematic Comparison of Methods for Combining p p -Values from Independent Tests
Thomas M Loughin · 2004
Earlier work this paper cites.
Word Sense Disambiguation with Distribution Estimation
Yee Seng Chan and Hwee Tou Ng · 2005
Earlier work this paper cites.
Very Sparse Random Projections
Ping Li, Trevor J Hastie, and Kenneth W Church · 2006
Earlier work this paper cites.
Isolation Forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Autotagging Facebook: Social Network Context Improves Photo Annotation
Zak Stone, Todd Zickler, and Trevor Darrell · 2008
Earlier work this paper cites.
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe · 2008
Earlier work this paper cites.
Anomaly Detection: A Survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
Earlier work this paper cites.
Covariate Shift by Kernel Mean Matching
Arthur Gretton, Alexander J Smola, Jiayuan Huang, Marcel Schmittfull, Karsten M Borgwardt, and Bernhard Schölkopf · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky and Geoffrey Hinton · 2009
Cited alongside, same era.
When Training and Test Sets Are Different: Characterizing Learning Transfer
Amos Storkey · 2009
Cited alongside, same era.
Impossibility Theorems for Domain Adaptation
Shai Ben-David, Tyler Lu, Teresa Luu, and Dávid Pál · 2010
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.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
A Kernel Two-Sample Test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
A Baseline for Detecting Misclassified and Out-Of-Distribution Examples in Neural Networks
Dan Hendrycks and Kevin Gimpel · 2017
Later among the works it cites.
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani and Baskaran Sundaram · 2017
Later among the works it cites.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
Later among the works it cites.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al · 2012
Cited alongside, same era.
On Causal and Anticausal Learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Cited alongside, same era.
Speech Recognition with Deep Recurrent Neural Networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Cited alongside, same era.
Transfer Feature Learning with Joint Distribution Adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2013
Cited alongside, same era.
Domain Adaptation Under Target and Conditional Shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
Cited alongside, same era.
Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Alexander A Alemi, Ian Fischer, and Joshua V Dillon · 2018
Closest in time.
Generative Ensembles for Robust Anomaly Detection
Hyunsun Choi and Eric Jang · 2018
Closest in time.
Choosing Between Methods of Combining-Values
Nicholas A Heard and Patrick Rubin-Delanchy · 2018
Closest in time.
Uniform, Nonparametric, Non-Asymptotic Confidence Sequences
Steven R Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon · 2018
Closest in time.
Training Confidence-Calibrated Classifiers for Detecting Out-Of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
Closest in time.
Enhancing the Reliability of Out-Of-Distribution Image Detection in Neural Networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
Closest in time.
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary C Lipton, Yu-Xiang Wang, and Alex Smola · 2018
Closest in time.
Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of Outlier Detectors
Alireza Shafaei, Mark Schmidt, and James J Little · 2018
Closest in time.
Out-Of-Distribution Detection Using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, and Joseph Keshet · 2018
Closest in time.
A Review of Change Point Detection Methods
Charles Truong, Laurent Oudre, and Nicolas Vayatis · 2018
Closest in time.
Combining p p -Values via Averaging
Vladimir Vovk and Ruodu Wang · 2018
Closest in time.
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
Closest in time.
Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, and Thomas G Dietterich · 2019
Closest in time.