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Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior.
Clever Hans (the Horse of Mr. Von Osten): A contribution to experimental animal and human psychology , vol. 8 (Holt, Rinehart and Winston, 1911)
Pfungst, O · 1911
Earlier work this paper cites.
Intelligent machinery, a heretical theory
Turing, A. M · 1948
Earlier work this paper cites.
On the theory of dynamic programming
Bellman, R · 1952
Earlier work this paper cites.
The Psychology of Computer Vision (McGraw-Hill New York, 1975)
Winston, P. H. & Horn, B · 1975
Earlier work this paper cites.
Introduction to Modern Information Retrieval (McGraw-Hill Book Company, 1984)
Salton, G. & McGill, M · 1984
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M. & White, H · 1989
Earlier work this paper cites.
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I · 1989
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Learning from Delayed Rewards
Watkins, C. J. C. H · 1989
Earlier work this paper cites.
Principles of object perception
Spelke, E. S · 1990
Earlier work this paper cites.
Stochastic gradient learning in neural networks
Bottou, L · 1991
Earlier work this paper cites.
Cognitive development: Foundational theories of core domains
Wellman, H. M. & Gelman, S. A · 1992
Earlier work this paper cites.
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G. & Pallett, D. S · 1993
Earlier work this paper cites.
Neural networks for pattern recognition (Oxford University Press, 1995)
Bishop, C. M · 1995
Earlier work this paper cites.
Support-vector networks
Cortes, C. & Vapnik, V · 1995
Earlier work this paper cites.
Reinforcement learning: A survey
Kaelbling, L. P., Littman, M. L. & Moore, A. W · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. & Schmidhuber, J · 1997
Earlier work this paper cites.
Knowledge acquisition in foundational domains
Wellman, H. M. & Gelman, S. A · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P · 1998
Earlier work this paper cites.
Object recognition from local scale-invariant features
Lowe, D. G · 1999
Earlier work this paper cites.
A random walks view of spectral segmentation
Meila, M. & Shi, J · 2001
Earlier work this paper cites.
Object name learning provides on-the-job training for attention
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L. & Samuelson, L · 2002
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Stanley, K. O. & Miikkulainen, R · 2002
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I. & Weiss, Y · 2002
Earlier work this paper cites.
An introduction to variable and feature selection
Guyon, I. & Elisseeff, A · 2003
Earlier work this paper cites.
Review and comparison of methods to study the contribution of variables in artificial neural network models
Gevrey, M., Dimopoulos, I. & Lek, S · 2003
Earlier work this paper cites.
One-year-old infants use teleological representations of actions productively
Csibra, G., Bıró, S., Koós, O. & Gergely, G · 2003
Earlier work this paper cites.
Movielens unplugged: experiences with an occasionally connected recommender system
Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A. & Riedl, J · 2003
Earlier work this paper cites.
Infants’ physical world
Baillargeon, R · 2004
Earlier work this paper cites.
GPU implementation of neural networks
Oh, K.-S. & Jung, K · 2004
Earlier work this paper cites.
Visual explanation of evidence with additive classifiers
Poulin, B. et al · 2006
Earlier work this paper cites.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Lazebnik, S., Schmid, C. & Ponce, J · 2006
Earlier work this paper cites.
Supervised group lasso with applications to microarray data analysis
Ma, S., Song, X. & Huang, J · 2007
Earlier work this paper cites.
Universal intelligence: A definition of machine intelligence
Legg, S. & Hutter, M · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Von Luxburg, U · 2007
Earlier work this paper cites.
Core knowledge
Spelke, E. S. & Kinzler, K. D · 2007
Earlier work this paper cites.
Robust object recognition with cortex-like mechanisms
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M. & Poggio, T · 2007
Earlier work this paper cites.
The pascal visual object classes challenge results
Everingham, M., Gool, L., Williams, C., Winn, J. & Zisserman, A · 2007
Earlier work this paper cites.
Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A. & Munoz, X · 2007
Earlier work this paper cites.
Nonnegative matrix factorization: An analytical and interpretive tool in computational biology
Devarajan, K · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L. v. d. & Hinton, G · 2008
Earlier work this paper cites.
Computing machinery and intelligence
Turing, A. M · 2009
Earlier work this paper cites.
Machines who think: A personal inquiry into the history and prospects of artificial intelligence (AK Peters/CRC Press, 2009)
McCorduck, P · 2009
Earlier work this paper cites.
Explanation and categorization: How “why?” informs “what?”
Lombrozo, T · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. & Hinton, G · 2009
Earlier work this paper cites.
Learning deep architectures for ai
Bengio, Y. et al · 2009
Earlier work this paper cites.
How to explain individual classification decisions
Baehrens, D. et al · 2010
Earlier work this paper cites.
Improving the Fisher kernel for large-scale image classification
Perronnin, F., Sánchez, J. & Mensink, T · 2010
Earlier work this paper cites.
The Pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J. & Zisserman, A · 2010
Earlier work this paper cites.
The role of explanation in discovery and generalization: Evidence from category learning
Williams, J. J. & Lombrozo, T · 2010
Earlier work this paper cites.
Measuring universal intelligence: Towards an anytime intelligence test
Hernández-Orallo, J. & Dowe, D. L · 2010
Earlier work this paper cites.
A committee of neural networks for traffic sign classification
Cireşan, D., Meier, U., Masci, J. & Schmidhuber, J · 2011
Cited alongside, same era.
Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K. & Farabet, C · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Netzer, Y. et al · 2011
Cited alongside, same era.
Deep sparse rectifier neural networks
Glorot, X., Bordes, A. & Bengio, Y · 2011
Cited alongside, same era.
Kernel analysis of deep networks
Montavon, G., Braun, M. L. & Müller, K.-R · 2011
Cited alongside, same era.
The devil is in the details: an evaluation of recent feature encoding methods
Chatfield, K., Lempitsky, V. S., Vedaldi, A. & Zisserman, A · 2011
Cited alongside, same era.
Deep learning (MIT Press, 2016)
Goodfellow, I., Bengio, Y. & Courville, A · 2016
Later among the works it cites.
Salient deconvolutional networks
Mahendran, A. & Vedaldi, A · 2016
Later among the works it cites.
Identifying individual facial expressions by deconstructing a neural network
Arbabzadah, F., Montavon, G., Müller, K.-R. & Samek, W · 2016
Later among the works it cites.
Classifying and segmenting microscopy images with deep multiple instance learning
Kraus, O. Z., Ba, J. L. & Frey, B. J · 2016
Later among the works it cites.
Visualizing and understanding neural models in NLP
Li, J., Chen, X., Hovy, E. H. & Jurafsky, D · 2016
Later among the works it cites.
Explaining predictions of non-linear classifiers in nlp
Arras, L., Horn, F., Montavon, G., Müller, K.-R. & Samek, W · 2016
Later among the works it cites.
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Allen, J. D., Xie, Y., Chen, M., Girard, L. & Xiao, G · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G. et al · 2012
Cited alongside, same era.
Learning invariant feature hierarchies
LeCun, Y · 2012
Cited alongside, same era.
Playing Atari with deep reinforcement learning
Mnih, V. et al · 2013
Cited alongside, same era.
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Simonyan, K., Vedaldi, A. & Zisserman, A · 2013
Cited alongside, same era.
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Das, A., Agrawal, H., Zitnick, L., Parikh, D. & Batra, D · 2016
Later among the works it cites.
Top-down neural attention by excitation backprop
Zhang, J., Lin, Z. L., Brandt, J., Shen, X. & Sclaroff, S · 2016
Later among the works it cites.
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Later among the works it cites.
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Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R. & Samek, W · 2016
Later among the works it cites.
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Bach, S., Binder, A., Müller, K.-R. & Samek, W · 2016
Later among the works it cites.
Faulty reward functions in the wild (2016)
Amodei, D. & Clark, J · 2016
Later among the works it cites.
Mastering the game of Go without human knowledge
Silver, D. et al · 2017
Later among the works it cites.
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Arras, L., Horn, F., Montavon, G., Müller, K.-R. & Samek, W · 2017
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Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement
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Building machines that learn and think like people
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Human learning in Atari
Tsividis, P. A., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J · 2017
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Goodman, B. & Flaxman, S. R · 2017
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Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R. & Tkatchenko, A · 2017
Later among the works it cites.
Bypassing the kohn-sham equations with machine learning
Brockherde, F. et al · 2017
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Beating the world’s best at super smash bros. with deep reinforcement learning
Firoiu, V., Whitney, W. F. & Tenenbaum, J. B · 2017
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Superintelligence (Dunod, 2017)
Bostrom, N · 2017
Later among the works it cites.
Weller, A · 2017
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A roadmap for a rigorous science of interpretability
Doshi-Velez, F. & Kim, B · 2017
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Accountability of ai under the law: The role of explanation
Doshi-Velez, F. et al · 2017
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
Later among the works it cites.
Opening the black box of deep neural networks via information
Shwartz-Ziv, R. & Tishby, N · 2017
Later among the works it cites.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. & Vedaldi, A · 2017
Later among the works it cites.
Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S. & Müller, K.-R · 2017
Later among the works it cites.
Explaining recurrent neural network predictions in sentiment analysis
Arras, L., Montavon, G., Müller, K.-R. & Samek, W · 2017
Later among the works it cites.
Leike, J. et al · 2017
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Clustering with t-sne, provably
Linderman, G. C. & Steinerberger, S · 2017
Later among the works it cites.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D. et al · 2018
Later among the works it cites.
Visualizing and understanding Atari agents
Greydanus, S., Koul, A., Dodge, J. & Fern, A · 2018
Later among the works it cites.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rajalingham, R. et al · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
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Explaining therapy predictions with layer-wise relevance propagation in neural networks
Yang, Y., Tresp, V., Wunderle, M. & Fasching, P. A · 2018
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Thomas, A. W., Heekeren, H. R., Müller, K.-R. & Samek, W · 2018
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Methods for interpreting and understanding deep neural networks
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Deep neural networks for no-reference and full-reference image quality assessment
Bosse, S., Maniry, D., Müller, K.-R., Wiegand, T. & Samek, W · 2018
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Seibold, C., Samek, W., Hilsmann, A. & Eisert, P · 2018
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Alber, M. et al · 2018
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Fair play: The ethics of sport (Routledge, 2018)
Simon, R. L · 2018
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