-
2
-
-
84938888109
-
Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
-
B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33(8): 831-838, 2015.
-
(2015)
Nature Biotechnology
, vol.33
, Issue.8
, pp. 831-838
-
-
Alipanahi, B.1
Delong, A.2
Weirauch, M.T.3
Frey, B.J.4
-
3
-
-
85010746053
-
-
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané. Concrete problems in AI safety. arXiv preprint arXiv: 1606.06565, 2016.
-
(2016)
Concrete Problems in AI Safety
-
-
Amodei, D.1
Olah, C.2
Steinhardt, J.3
Christiano, P.4
Schulman, J.5
Mané, D.6
-
7
-
-
0030211964
-
Bagging predictors
-
L. Breiman. Bagging predictors. Machine learning, 24(2): 123-140, 1996.
-
(1996)
Machine Learning
, vol.24
, Issue.2
, pp. 123-140
-
-
Breiman, L.1
-
8
-
-
0035478854
-
Random forests
-
L. Breiman. Random forests. Machine learning, 45(1): 5-32, 2001.
-
(2001)
Machine Learning
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
9
-
-
0003010182
-
Verification of forecasts expressed in terms of probability
-
G. W. Brier. Verification of forecasts expressed in terms of probability. Monthly weather review, 1950.
-
(1950)
Monthly Weather Review
-
-
Brier, G.W.1
-
11
-
-
2542484580
-
Comparing bayes model averaging and stacking when model approximation error cannot be ignored
-
B. Clarke. Comparing Bayes model averaging and stacking when model approximation error cannot be ignored. J. Mach. Learn. Res. (JMLR), 4: 683-712, 2003.
-
(2003)
J. Mach. Learn. Res. (JMLR)
, vol.4
, pp. 683-712
-
-
Clarke, B.1
-
15
-
-
84998879817
-
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
-
Y. Gal and Z. Ghahramani. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In ICML, 2016.
-
(2016)
ICML
-
-
Gal, Y.1
Ghahramani, Z.2
-
16
-
-
33646430006
-
Extremely randomized trees
-
P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine learning, 63(1): 3-42, 2006.
-
(2006)
Machine Learning
, vol.63
, Issue.1
, pp. 3-42
-
-
Geurts, P.1
Ernst, D.2
Wehenkel, L.3
-
17
-
-
33947274775
-
Strictly proper scoring rules, prediction, and estimation
-
T. Gneiting and A. E. Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477): 359-378, 2007.
-
(2007)
Journal of the American Statistical Association
, vol.102
, Issue.477
, pp. 359-378
-
-
Gneiting, T.1
Raftery, A.E.2
-
18
-
-
85083951001
-
Explaining and harnessing adversarial examples
-
I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015.
-
(2015)
ICLR
-
-
Goodfellow, I.J.1
Shlens, J.2
Szegedy, C.3
-
19
-
-
85162557101
-
Practical variational inference for neural networks
-
A. Graves. Practical variational inference for neural networks. In NIPS, 2011.
-
(2011)
NIPS
-
-
Graves, A.1
-
21
-
-
85018868471
-
-
L. Hasenclever, S. Webb, T. Lienart, S. Vollmer, B. Lakshminarayanan, C. Blundell, and Y. W. Teh. Distributed Bayesian learning with stochastic natural-gradient expectation propagation and the posterior server. arXiv preprint arXiv: 1512.09327, 2015.
-
(2015)
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
-
-
Hasenclever, L.1
Webb, S.2
Lienart, T.3
Vollmer, S.4
Lakshminarayanan, B.5
Blundell, C.6
Teh, Y.W.7
-
22
-
-
84986274465
-
Deep residual learning for image recognition
-
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016.
-
(2016)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 770-778
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
24
-
-
84969909658
-
Probabilistic backpropagation for scalable learning of Bayesian neural networks
-
J. M. Hernández-Lobato and R. P. Adams. Probabilistic backpropagation for scalable learning of Bayesian neural networks. In ICML, 2015.
-
(2015)
ICML
-
-
Hernández-Lobato, J.M.1
Adams, R.P.2
-
25
-
-
85032751458
-
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
-
IEEE
-
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6): 82-97, 2012.
-
(2012)
Signal Processing Magazine
, vol.29
, Issue.6
, pp. 82-97
-
-
Hinton, G.1
Deng, L.2
Yu, D.3
Dahl, G.E.4
Mohamed, A.-R.5
Jaitly, N.6
Senior, A.7
Vanhoucke, V.8
Nguyen, P.9
Sainath, T.N.10
-
27
-
-
85088231359
-
-
ICLR submission
-
G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger. Snapshot ensembles: Train 1, get M for free. ICLR submission, 2017.
-
(2017)
Snapshot Ensembles: Train 1, Get M for Free
-
-
Huang, G.1
Li, Y.2
Pleiss, G.3
Liu, Z.4
Hopcroft, J.E.5
Weinberger, K.Q.6
-
28
-
-
0001940458
-
Adaptive mixtures of local experts
-
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural computation, 3(1): 79-87, 1991.
-
(1991)
Neural Computation
, vol.3
, Issue.1
, pp. 79-87
-
-
Jacobs, R.A.1
Jordan, M.I.2
Nowlan, S.J.3
Hinton, G.E.4
-
29
-
-
84965103544
-
Variational dropout and the local reparameterization trick
-
D. P. Kingma, T. Salimans, and M. Welling. Variational dropout and the local reparameterization trick. In NIPS, 2015.
-
(2015)
NIPS
-
-
Kingma, D.P.1
Salimans, T.2
Welling, M.3
-
32
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
35
-
-
84930630277
-
Deep learning
-
Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553): 436-444, 2015.
-
(2015)
Nature
, vol.521
, Issue.7553
, pp. 436-444
-
-
LeCun, Y.1
Bengio, Y.2
Hinton, G.3
-
36
-
-
85012994753
-
-
S. Lee, S. Purushwalkam, M. Cogswell, D. Crandall, and D. Batra. Why M heads are better than one: Training a diverse ensemble of deep networks. arXiv preprint arXiv: 1511.06314, 2015.
-
(2015)
Why M Heads are Better Than One: Training a Diverse Ensemble of Deep Networks
-
-
Lee, S.1
Purushwalkam, S.2
Cogswell, M.3
Crandall, D.4
Batra, D.5
-
37
-
-
85016979026
-
Stochastic multiple choice learning for training diverse deep ensembles
-
S. Lee, S. P. S. Prakash, M. Cogswell, V. Ranjan, D. Crandall, and D. Batra. Stochastic multiple choice learning for training diverse deep ensembles. In NIPS, 2016.
-
(2016)
NIPS
-
-
Lee, S.1
Prakash, S.P.S.2
Cogswell, M.3
Ranjan, V.4
Crandall, D.5
Batra, D.6
-
44
-
-
84965162688
-
Distributional smoothing by virtual adversarial examples
-
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii. Distributional smoothing by virtual adversarial examples. In ICLR, 2016.
-
(2016)
ICLR
-
-
Miyato, T.1
Maeda, S.-I.2
Koyama, M.3
Nakae, K.4
Ishii, S.5
-
45
-
-
77956509090
-
Rectified linear units improve restricted boltzmann machines
-
V. Nair and G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. In ICML, 2010.
-
(2010)
ICML
-
-
Nair, V.1
Hinton, G.E.2
-
49
-
-
33947197171
-
Evaluating predictive uncertainty challenge
-
Springer
-
J. Quinonero-Candela, C. E. Rasmussen, F. Sinz, O. Bousquet, and B. Schölkopf. Evaluating predictive uncertainty challenge. In Machine Learning Challenges. Springer, 2006.
-
(2006)
Machine Learning Challenges
-
-
Quinonero-Candela, J.1
Rasmussen, C.E.2
Sinz, F.3
Bousquet, O.4
Schölkopf, B.5
-
50
-
-
31844431858
-
Healing the relevance vector machine through augmentation
-
C. E. Rasmussen and J. Quinonero-Candela. Healing the relevance vector machine through augmentation. In ICML, 2005.
-
(2005)
ICML
-
-
Rasmussen, C.E.1
Quinonero-Candela, J.2
-
51
-
-
84947041871
-
ImageNet large scale visual recognition challenge
-
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3): 211-252, 2015.
-
(2015)
International Journal of Computer Vision (IJCV)
, vol.115
, Issue.3
, pp. 211-252
-
-
Russakovsky, O.1
Deng, J.2
Su, H.3
Krause, J.4
Satheesh, S.5
Ma, S.6
Huang, Z.7
Karpathy, A.8
Khosla, A.9
Bernstein, M.10
Berg, A.C.11
Fei-Fei, L.12
-
52
-
-
85018925999
-
Swapout: Learning an ensemble of deep architectures
-
S. Singh, D. Hoiem, and D. Forsyth. Swapout: Learning an ensemble of deep architectures. In NIPS, 2016.
-
(2016)
NIPS
-
-
Singh, S.1
Hoiem, D.2
Forsyth, D.3
-
53
-
-
85015791874
-
Bayesian optimization with robust Bayesian neural networks
-
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter. Bayesian optimization with robust Bayesian neural networks. In Advances in Neural Information Processing Systems, pages 4134-4142, 2016.
-
(2016)
Advances in Neural Information Processing Systems
, pp. 4134-4142
-
-
Springenberg, J.T.1
Klein, A.2
Falkner, S.3
Hutter, F.4
-
54
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 2014.
-
(2014)
JMLR
-
-
Srivastava, N.1
Hinton, G.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
55
-
-
85083953343
-
Intriguing properties of neural networks
-
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. Intriguing properties of neural networks. In ICLR, 2014.
-
(2014)
ICLR
-
-
Szegedy, C.1
Zaremba, W.2
Sutskever, I.3
Bruna, J.4
Erhan, D.5
Goodfellow, I.6
Fergus, R.7
-
56
-
-
84986296808
-
Rethinking the inception architecture for computer vision
-
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818-2826, 2016.
-
(2016)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 2818-2826
-
-
Szegedy, C.1
Vanhoucke, V.2
Ioffe, S.3
Shlens, J.4
Wojna, Z.5
-
57
-
-
85037351312
-
-
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv: 1705.07204, 2017.
-
(2017)
Ensemble Adversarial Training: Attacks and Defenses
-
-
Tramèr, F.1
Kurakin, A.2
Papernot, N.3
Boneh, D.4
McDaniel, P.5
-
59
-
-
80053452150
-
Bayesian learning via stochastic gradient langevin dynamics
-
M. Welling and Y. W. Teh. Bayesian learning via stochastic gradient Langevin dynamics. In ICML, 2011.
-
(2011)
ICML
-
-
Welling, M.1
Teh, Y.W.2
-
60
-
-
0026692226
-
Stacked generalization
-
D. H. Wolpert. Stacked generalization. Neural networks, 5(2): 241-259, 1992.
-
(1992)
Neural Networks
, vol.5
, Issue.2
, pp. 241-259
-
-
Wolpert, D.H.1
-
61
-
-
84958257565
-
Predicting effects of noncoding variants with deep learning-based sequence model
-
J. Zhou and O. G. Troyanskaya. Predicting effects of noncoding variants with deep learning-based sequence model. Nature methods, 12(10): 931-934, 2015.
-
(2015)
Nature Methods
, vol.12
, Issue.10
, pp. 931-934
-
-
Zhou, J.1
Troyanskaya, O.G.2
|