-
1
-
-
84930630277
-
Deep learning
-
Bengio, Yoshua, Goodfellow, Ian J, and Courville, Aaron. Deep learning. Nature, 521:436-444,2015
-
(2015)
Nature
, vol.521
, pp. 436-444
-
-
Yoshua, B.1
Goodfellow, J.2
Aaron, C.3
-
2
-
-
85010075270
-
-
arXiv preprint arXiv: 1604.07316
-
Bojarski, Mariusz, Del Testa, Davide, Dworakowski, Daniel, Firner, Bernhard, Flepp, Beat, Goyal, Prasoon, Jackel, Lawrence D, Monfort, Mathew, Muller, Urs, Zhang, Jiakai, et al. End to end learning for self-driving cars. ArXiv preprint arXiv:1604.07316, 2016
-
(2016)
End to End Learning for Self-driving Cars
-
-
Mariusz, B.1
Testa Davide, D.2
Daniel, D.3
Bernhard, F.4
Beat, F.5
Prasoon, G.6
Jackel Lawrence, D.7
Mathew, M.8
Urs, M.9
Jiakai, Z.10
-
3
-
-
84954180053
-
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day rcadmission
-
Caruana, Rich, Lou, Yin, Gehrke, Johannes, Koch, Paul, Sturm, Marc, and Elhadad, Noemie. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day rcadmission. In KDD, 2015
-
(2015)
KDD
-
-
Rich, C.1
Yin, L.2
Johannes, G.3
Paul, K.4
Marc, S.5
Noemie, E.6
-
4
-
-
84989815411
-
Torch7: A matlab-like environment for machine learning
-
Collobert, Ronan, Kavukcuoglu, Koray, and Farabet, Clément. Torch7: A matlab-like environment for machine learning. In BigLearn Workshop, NIPS, 2011
-
(2011)
BigLearn Workshop, NIPS
-
-
Ronan, C.1
Koray, K.2
Clément, F.3
-
5
-
-
0042123195
-
Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty
-
Cosmides, Leda and Tooby, John. Are humans good intuitive statisticians after all? rethinking some conclusions from the literature on judgment under uncertainty, cog-w7vi,58(l):l-73, 1996
-
(1996)
COG-w7vi
, vol.58
, Issue.1
, pp. 1-73
-
-
Leda, C.1
John, T.2
-
7
-
-
72249100259
-
Imagenet: A large-scale hierarchical image database
-
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li. Imagenet: A large-scale hierarchical image database. In CVPR, pp. 248-255, 2009
-
(2009)
CVPR
, pp. 248-255
-
-
Jia, D.1
Wei, D.2
Richard, S.3
Li-Jia, L.4
Kai, L.5
Li, F.6
-
8
-
-
0011847141
-
Transforming neural-net output levels to probability distributions
-
Denker, John S and Lecun, Yann. Transforming neural-net output levels to probability distributions. In NIPS, pp. 853-859, 1990
-
(1990)
NIPS
, pp. 853-859
-
-
Denker John, S.1
Yann, L.2
-
9
-
-
0003684449
-
-
Springer series in statistics Springer, Berlin
-
Friedman, Jerome, Hastie, Trevor, and Tibshirani, Robert. The elements of statistical learning, volume 1. Springer series in statistics Springer, Berlin, 2001
-
(2001)
The Elements of Statistical Learning, Volume 1
-
-
Jerome, F.1
Trevor, H.2
Robert, T.3
-
10
-
-
84998879817
-
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
-
Gal, Yarin and Ghahramani, Zoubin. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In ICML, 2016
-
(2016)
ICML
-
-
Yarin, G.1
Zoubin, G.2
-
11
-
-
84964588182
-
Fastr-cnn
-
Girshick, Ross. Fastr-cnn. In ICCV, pp. 1440-1448, 2015
-
(2015)
ICCV
, pp. 1440-1448
-
-
Ross, G.1
-
12
-
-
84983519403
-
-
arXiv preprint arXiv:I4I2.5567, 2014
-
Hannun, Awni, Case, Carl, Casper, Jared, Catanzaro, Bryan, Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, et al. Deep speech: Scaling up end-to-end speech recognition. ArXiv preprint arXiv:I4I2.5567, 2014
-
Deep Speech: Scaling Up End-to-end Speech Recognition
-
-
Awni, H.1
Carl, C.2
Jared, C.3
Bryan, C.4
Greg, D.5
Erich, E.6
Ryan, P.7
Sanjeev, S.8
Shubho, S.9
Adam, C.10
-
13
-
-
84986274465
-
Deep residual learning for image recognition
-
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Deep residual learning for image recognition. In CVPR, pp. 770-778, 2016
-
(2016)
CVPR
, pp. 770-778
-
-
Kaiming, H.1
Xiangyu, Z.2
Shaoqing, R.3
Jian, S.4
-
14
-
-
85048447329
-
A baseline for detecting misclassified and out-of-distribution examples in neural networks
-
Hendrycks, Dan and Gimpel, Kevin. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In ICLR, 2017
-
(2017)
ICLR
-
-
Dan, H.1
Kevin, G.2
-
16
-
-
84984824417
-
Deep networks with stochastic depth
-
Huang, Gao, Sun, Yu, Liu, Zhuang, Sedra, Daniel, and Weinberger, Kilian. Deep networks with stochastic depth. In ECCV, 2016
-
(2016)
ECCV
-
-
Gao, H.1
Yu, S.2
Zhuang, L.3
Daniel, S.4
Kilian, W.5
-
17
-
-
85035343801
-
Densely connected convolutional networks
-
Huang, Gao, Liu, Zhuang, Weinberger, Kilian Q, and van der Maaten, Laurens. Densely connected convolutional networks. In CVPR, 2017
-
(2017)
CVPR
-
-
Gao, H.1
Zhuang, L.2
Weinberger Kilian, Q.3
Van Der, M.4
-
19
-
-
84943803518
-
Hal. Deep unordered composition rivals syntactic methods for text classification
-
Iyycr, Mohit, Manjunatha, Varun, Boyd-Grabcr, Jordan, and Daumé III, Hal. Deep unordered composition rivals syntactic methods for text classification. In ACL, 2015
-
(2015)
ACL
-
-
Mohit, I.1
Varun, M.2
Jordan, B.3
-
20
-
-
11944266539
-
Information theory and statistical mechanics
-
Jaynes, Edwin T. Information theory and statistical mechanics. Physical review, 106(4):620, 1957
-
(1957)
Physical Review
, vol.106
, Issue.4
, pp. 620
-
-
Jaynes Edwin, T.1
-
21
-
-
84863155379
-
Calibrating predictive model estimates to support personalized medicine
-
Jiang, Xiaoqian, Osi, Melanie, Kim, Jihoon, and Ohno-Machado, Lucila. Calibrating predictive model estimates to support personalized medicine. Journal of the American Medical Informatics Association, 19(2):263-274, 2012
-
(2012)
Journal of the American Medical Informatics Association
, vol.19
, Issue.2
, pp. 263-274
-
-
Xiaoqian, J.1
Melanie, O.2
Jihoon, K.3
Lucila, O.4
-
24
-
-
84897485170
-
3d object representations for fine-grained categorization
-
Sydney, Australia
-
Krause, Jonathan, Stark, Michael, Deng, Jia, and Fei-Fei, Li. 3d object representations for fine-grained categorization. In IEEE Workshop on 3D Representation and Recognition (3dRR), Sydney, Australia, 2013
-
(2013)
IEEE Workshop on 3D Representation and Recognition (3dRR)
-
-
Jonathan, K.1
Michael, S.2
Jia, D.3
Li, F.4
-
27
-
-
84965174939
-
Calibrated structured prediction
-
Kuleshov, Volodymyr and Liang, Percy. Calibrated structured prediction. In NIPS, pp. 3474-3482, 2015
-
(2015)
NIPS
, pp. 3474-3482
-
-
Volodymyr, K.1
Percy, L.2
-
29
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
Yann, L.1
Léon, B.2
Yoshua, B.3
Patrick, H.4
-
30
-
-
0002704818
-
A practical Bayesian framework for backpropagation networks
-
MacKay, David JC. A practical Bayesian framework for backpropagation networks. Neural computation, 4(3): 448-472, 1992
-
(1992)
Neural Computation
, vol.4
, Issue.3
, pp. 448-472
-
-
MacKay David, J.C.1
-
31
-
-
84960117979
-
Obtaining well calibrated probabilities using Bayesian binning
-
Naeini, Mahdi Pakdaman, Cooper, Gregory F, and Hauskrecht, Milos. Obtaining well calibrated probabilities using Bayesian binning. In AAAI, pp. 2901, 2015
-
(2015)
AAAI
, pp. 2901
-
-
Mahdi Pakdaman, N.1
Cooper Gregory, F.2
Milos, H.3
-
32
-
-
85093637062
-
Reading digits in natural images with unsupervised feature learning
-
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y. Reading digits in natural images with unsupervised feature learning. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS, 2011
-
(2011)
Deep Learning and Unsupervised Feature Learning Workshop, NIPS
-
-
Yuval, N.1
Tao, W.2
Adam, C.3
Alessandro, B.4
Bo, W.5
Ng Andrew, Y.6
-
33
-
-
31844433358
-
Predicting good probabilities with supervised learning
-
Niculescu-Mizil, Alcxandru and Caruana, Rich. Predicting good probabilities with supervised learning. In ICML, pp. 625-632,2005
-
(2005)
ICML
, pp. 625-632
-
-
Alcxandru, N.1
Rich, C.2
-
34
-
-
85025706966
-
-
arXiv preprint arXiv: 1701.06548
-
Pereyra, Gabriel, Tucker, George, Chorowski, Jan, Kaiser, Lukasz, and Hinton, Geoffrey. Regularizing neural networks by penalizing confident output distributions. ArXiv preprint arXiv:1701.06548, 2017
-
(2017)
Regularizing Neural Networks by Penalizing Confident Output Distributions
-
-
Gabriel, P.1
George, T.2
Jan, C.3
Lukasz, K.4
Geoffrey, H.5
-
35
-
-
0003243224
-
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
-
Piatt, John et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3): 61-74, 1999
-
(1999)
Advances in Large Margin Classifiers
, vol.10
, Issue.3
, pp. 61-74
-
-
John, P.1
-
36
-
-
85083953063
-
Very deep convolutional networks for large-scale image recognition
-
Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015
-
(2015)
ICLR
-
-
Karen, S.1
Andrew, Z.2
-
37
-
-
84926358845
-
Recursive deep models for semantic com-positionality over a sentiment treebank
-
Sochcr, Richard, Pcrelygin, Alex, Wu, Jean, Chuang, Jason, Manning, Christopher D., Ng, Andrew, and Potts, Christopher. Recursive deep models for semantic com-positionality over a sentiment treebank. In EMNLP, pp. 1631-1642,2013
-
(2013)
EMNLP
, pp. 1631-1642
-
-
Richard, S.1
Alex, P.2
Jean, W.3
Jason, C.4
Manning Christopher, D.5
Andrew, N.6
Christopher, P.7
-
38
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929-1958, 2014
-
(2014)
Journal of Machine Learning Research
, vol.15
, pp. 1929-1958
-
-
Nitish, S.1
Geoffrey, H.2
Alex, K.3
Ilya, S.4
Ruslan, S.5
-
42
-
-
80052891795
-
-
Welinder, P., Branson, S., Mita, T., Wah, C, Schroff, F., Belongie, S., and Perona, P. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology, 2010
-
(2010)
Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology
-
-
Welinder, P.1
Branson, S.2
Mita, T.3
Wah, C.4
Schroff, F.5
Belongie, S.6
Perona, P.7
-
43
-
-
85048404035
-
-
arXiv preprint arXiv: 1610.05256,20\6
-
Xiong, Wayne, Droppo, Jasha, Huang, Xuedong, Seide, Frank, Seltzer, Mike, Stolcke, Andreas, Yu, Dong, and Zweig, Geoffrey. Achieving human parity in conversational speech recognition. ArXiv preprint arXiv:1610.05256,20\6
-
Achieving Human Parity in Conversational Speech Recognition
-
-
Wayne, X.1
Jasha, D.2
Xuedong, H.3
Frank, S.4
Mike, S.5
Andreas, S.6
Dong, Y.7
Geoffrey, Z.8
-
44
-
-
0003259364
-
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
-
Zadrozny, Bianca and Elkan, Charles. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In ICML, pp. 609-616, 2001
-
(2001)
ICML
, pp. 609-616
-
-
Bianca, Z.1
Charles, E.2
-
45
-
-
0242456763
-
Transforming classifier scores into accurate multiclass probability estimates
-
Zadrozny, Bianca and Elkan, Charles. Transforming classifier scores into accurate multiclass probability estimates. In KDD, pp. 694-699, 2002
-
(2002)
KDD
, pp. 694-699
-
-
Bianca, Z.1
Charles, E.2
-
46
-
-
84990053656
-
Wide residual networks
-
Zagoruyko, Sergey and Komodakis, Nikos. Wide residual networks. InβMVC, 2016
-
(2016)
βmVC
-
-
Sergey, Z.1
Nikos, K.2
-
47
-
-
85088231398
-
Understanding deep learning requires rethinking generalization
-
Zhang, Chiyuan, Bengio, Samy, Hardt, Moritz, Recht, Benjamin, and Vinyals, Oriol. Understanding deep learning requires rethinking generalization. In ICLR, 2017.
-
(2017)
ICLR
-
-
Chiyuan, Z.1
Samy, B.2
Moritz, H.3
Benjamin, R.4
Oriol, V.5
|