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Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence.
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Clever Hans: (the horse of Mr. Von Osten.) a contribution to experimental animal and human psychology (Holt, Rinehart and Winston, 1911)
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The role of context in object recognition
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Scene completion using millions of photographs
Hays, J. & Efros, A. A · 2007
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IM2GPS: estimating geographic information from a single image
Hays, J. & Efros, A. A · 2008
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Discriminative learning under covariate shift
Bickel, S., Brückner, M. & Scheffer, T · 2009
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The unreasonable effectiveness of data
Halevy, A., Norvig, P. & Pereira, F · 2009
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Animal detection in natural scenes: Critical features revisited
Wichmann, F. A., Drewes, J., Rosas, P. & Gegenfurtner, K. R · 2010
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Unbiased look at dataset bias
Torralba, A. & Efros, A. A · 2011
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The neural net tank urban legend
Branwen, G · 2011
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Scene context influences without scene gist: Eye movements guided by spatial associations in visual search
Castelhano, M. S. & Heaven, C · 2011
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On causal and anticausal learning
Schölkopf, B. et al · 2012
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The Winograd Schema Challenge
Levesque, H., Davis, E. & Morgenstern, L · 2012
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O. & Zemel, R · 2012
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Intriguing properties of neural networks
Szegedy, C. et al · 2013
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The first level of Super Mario Bros. is easy with lexicographic orderings and time travel
Murphy VII, T · 2013
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T. & Dwork, C · 2013
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
He, K., Zhang, X., Ren, S. & Sun, J · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J. & Clune, J · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O. et al · 2015
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Mastering the game of Go with deep neural networks and tree search
Silver, D. et al · 2016
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Generating captions without looking beyond objects
Heuer, H., Monz, C. & Smeulders, A. W · 2016
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Why should I trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S. & Guestrin, C · 2016
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Object recognition with and without objects
Zhu, Z., Xie, L. & Yuille, A. L · 2016
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Analyzing the behavior of visual question answering models
Agrawal, A., Batra, D. & Parikh, D · 2016
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Concrete problems in AI safety
Amodei, D. et al · 2016
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J · 2016
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Group equivariant convolutional networks
Cohen, T. & Welling, M · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P. & Meinshausen, N · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E. & Srebro, N · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D. & Lillicrap, T · 2016
Cited alongside, same era.
Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Moravčík, M. et al · 2017
Cited alongside, same era.
Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning
Rajpurkar, P. et al · 2017
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Visual concepts and compositional voting
Wang, J. et al · 2017
Cited alongside, same era.
Measuring the tendency of CNNs to learn surface statistical regularities
Probing neural network comprehension of natural language arguments
Niven, T. & Kao, H.-Y · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A. et al · 2019
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Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet
Brendel, W. & Bethge, M · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R. et al · 2019
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What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
Malhotra, G. & Bowers, J · 2019
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Jo, J. & Bengio, Y · 2017
Cited alongside, same era.
Conditional variance penalties and domain shift robustness
Heinze-Deml, C. & Meinshausen, N · 2017
Cited alongside, same era.
Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D. & Parikh, D · 2017
Cited alongside, same era.
Synthetic and natural noise both break neural machine translation
Belinkov, Y. & Bisk, Y · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Jia, R. & Liang, P · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J. et al · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K.-W · 2017
Cited alongside, same era.
Excessive invariance causes adversarial vulnerability
Jacobsen, J.-H., Behrmann, J., Zemel, R. & Bethge, M · 2019
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Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J. & Song, D · 2019
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Alcorn, M. A. et al · 2019
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Human and DNN classification performance on images with quality distortions: A comparative study
Dodge, S. & Karam, L · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. & Dietterich, T · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Michaelis, C. et al · 2019
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Geva, M., Goldberg, Y. & Berant, J · 2019
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When choosing plausible alternatives, Clever Hans can be clever
Kavumba, P. et al · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in Natural Language Inference
McCoy, R. T., Pavlick, E. & Linzen, T · 2019
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Language models are unsupervised multitask learners
Radford, A. et al · 2019
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Solving Rubik’s Cube with a robot hand
Akkaya, I. et al · 2019
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Lessons for artificial intelligence from the study of natural stupidity
Rich, A. S. & Gureckis, T. M · 2019
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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A. & Choi, Y · 2019
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The notorious difficulty of comparing human and machine perception
Borowski, J. et al · 2019
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The Comparative Psychology of Artificial Intelligences (2019)
Buckner, C · 2019
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Are we asking the right questions in MovieQA?
Jasani, B., Girdhar, R. & Ramanan, D · 2019
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Deep neural networks as scientific models
Cichy, R. M. & Kaiser, D · 2019
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Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S. et al · 2019
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Chollet, F · 2019
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The Animal-AI Olympics
Crosby, M., Beyret, B. & Halina, M · 2019
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Obstacle tower: A generalization challenge in vision, control, and planning
Juliani, A. et al · 2019
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A discussion of ‘adversarial examples are not bugs, they are features’
Engstrom, L. et al · 2019
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ObjectNet: a large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A. et al · 2019
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Flexibly fair representation learning by disentanglement
Creager, E. et al · 2019
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Do ImageNet classifiers generalize to ImageNet?
Recht, B., Roelofs, R., Schmidt, L. & Shankar, V · 2019
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How reasonable are common-sense reasoning tasks: A case-study on the Winograd Schema Challenge and SWAG
Trichelair, P., Emami, A., Trischler, A., Suleman, K. & Cheung, J. C. K · 2019
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Finding the needle in the haystack with convolutions: On the benefits of architectural bias
d’Ascoli, S., Sagun, L., Bruna, J. & Biroli, G · 2019
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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Hein, M., Andriushchenko, M. & Bitterwolf, J · 2019
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Arjovsky, M., Bottou, L., Gulrajani, I. & Lopez-Paz, D · 2019
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Valle-Perez, G., Camargo, C. Q. & Louis, A. A · 2019
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Lightlike neuromanifolds, Occam’s Razor and deep learning
Sun, K. & Nielsen, F · 2019
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Li, Y., Wei, C. & Ma, T · 2019
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Benign overfitting in linear regression
Bartlett, P. L., Long, P. M., Lugosi, G. & Tsigler, A · 2019
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Engineering a less artificial intelligence
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S · 2019
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V. & Le, Q. V · 2019
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D. et al · 2019
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Causality for machine learning
Schölkopf, B · 2019
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Towards the first adversarially robust neural network model on MNIST
Schott, L., Rauber, J., Bethge, M. & Brendel, W · 2019
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Learning perceptually-aligned representations via adversarial robustness
Engstrom, L. et al · 2019
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A meta-transfer objective for learning to disentangle causal mechanisms
Bengio, Y. et al · 2019
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Automatic shortcut removal for self-supervised representation learning
Minderer, M., Bachem, O., Houlsby, N. & Tschannen, M · 2020
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Understanding the limitations of conditional generative models
Fetaya, E., Jacobsen, J.-H., Grathwohl, W. & Zemel, R · 2020
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