-
1
-
-
84998773543
-
Normalization propagation: A parametric technique for removing internal covariate shift in deep networks
-
D. Arpit, Y. Zhou, B.U. Kota, and V. Govindaraju. Normalization propagation: A parametric technique for removing internal covariate shift in deep networks. In ICML, 2016.
-
(2016)
ICML
-
-
Arpit, D.1
Zhou, Y.2
Kota, B.U.3
Govindaraju, V.4
-
3
-
-
85079594941
-
Designing neural network architectures using reinforcement learning
-
B. Baker, O. Gupta, N. Naik, and R. Raskar. Designing neural network architectures using reinforcement learning. In ICLR, 2017.
-
(2017)
ICLR
-
-
Baker, B.1
Gupta, O.2
Naik, N.3
Raskar, R.4
-
4
-
-
84857855190
-
Random search for hyperparameter optimization
-
J. Bergstra and Y. Bengio. Random search for hyperparameter optimization. In JMLR, 2012.
-
(2012)
JMLR
-
-
Bergstra, J.1
Bengio, Y.2
-
6
-
-
85083953532
-
Net2Net: Accelerating learning via knowledge transfer
-
T. Chen, I. Goodfellow, and J. Shiens. Net2net: Accelerating learning via knowledge transfer. In ICLR, 2016.
-
(2016)
ICLR
-
-
Chen, T.1
Goodfellow, I.2
Shiens, J.3
-
8
-
-
80053446757
-
N analysis of single layer networks in unsupervised feature learning
-
A. Coates, H. Lee, and A.Y. Ng. n analysis of single layer networks in unsupervised feature learning. In AISTATS, 2011.
-
(2011)
AISTATS
-
-
Coates, A.1
Lee, H.2
Ng, A.Y.3
-
9
-
-
84898971588
-
Predicting parameters in deep learning
-
M. Denil, B. Shakibi, L. Dinh, M. A. Ranzato, and N. de Freitas. Predicting parameters in deep learning. In NIPS, 2013.
-
(2013)
NIPS
-
-
Denil, M.1
Shakibi, B.2
Dinh, L.3
Ranzato, M.A.4
De Freitas, N.5
-
11
-
-
85144243631
-
Shake-shake regularization of 3-branch residual networks
-
X. Gastaldi. Shake-shake regularization of 3-branch residual networks. ICLR 2017 Workshop, 2017.
-
(2017)
ICLR 2017 Workshop
-
-
Gastaldi, X.1
-
13
-
-
84986274465
-
Deep residual learning for image recognition
-
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
-
(2016)
CVPR
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
14
-
-
84984824417
-
Deep networks with stochastic depth
-
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger. Deep networks with stochastic depth. In ECCV, 2016.
-
(2016)
ECCV
-
-
Huang, G.1
Sun, Y.2
Liu, Z.3
Sedra, D.4
Weinberger, K.Q.5
-
16
-
-
84969584486
-
Batch normalization: Accelerating deep network training by reducing internal covariate shift
-
S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
-
(2015)
ICML
-
-
Ioffe, S.1
Szegedy, C.2
-
20
-
-
85075204691
-
FractalNet: Ultra-deep neural networks without residuals
-
G. Larsson, M. Maire, and G. Shakhnarovich. Fractalnet: Ultra-deep neural networks without residuals. In ICLR, 2017.
-
(2017)
ICLR
-
-
Larsson, G.1
Maire, M.2
Shakhnarovich, G.3
-
21
-
-
85049152514
-
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
-
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. In ICLR, 2017.
-
(2017)
ICLR
-
-
Li, L.1
Jamieson, K.2
DeSalvo, G.3
Rostamizadeh, A.4
Talwalkar, A.5
-
22
-
-
85081410026
-
SGDR: Stochastic gradient descent with warm restarts
-
I. Loshchilov and F. Hutter. Sgdr: Stochastic gradient descent with warm restarts. In ICLR, 2017.
-
(2017)
ICLR
-
-
Loshchilov, I.1
Hutter, F.2
-
23
-
-
34548549772
-
Cartesian genetic programming
-
J.F. Miller and P. Thomson. Cartesian genetic programming. In EuroGP, 2000.
-
(2000)
EuroGP
-
-
Miller, J.F.1
Thomson, P.2
-
26
-
-
85048592974
-
-
arXiv Preprint
-
E. Real, S. Moore, A. Selle, S. Saxena, Y.L. Suematsu, Q. Le, and A. Kurakin. Large-scale evolution of image classifiers. arXiv Preprint arXiv: 1703.01041, 2017.
-
(2017)
Large-Scale Evolution of Image Classifiers
-
-
Real, E.1
Moore, S.2
Selle, A.3
Saxena, S.4
Suematsu, Y.L.5
Le, Q.6
Kurakin, A.7
-
28
-
-
85017457992
-
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
-
T. Salimans and D.P. Kingma. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In NIPS, 2016.
-
(2016)
NIPS
-
-
Salimans, T.1
Kingma, D.P.2
-
29
-
-
80053448548
-
On random weights and unsupervised feature learning
-
A.M. Saxe, P.W. Koh, Z. Chen, M. Bhand, B. Suresh, and A.Y. Ng. On random weights and unsupervised feature learning. In ICML, 2011.
-
(2011)
ICML
-
-
Saxe, A.M.1
Koh, P.W.2
Chen, Z.3
Bhand, M.4
Suresh, B.5
Ng, A.Y.6
-
30
-
-
85019232626
-
Convolutional neural fabrics
-
S. Saxena and J. Verbeek. Convolutional neural fabrics. In NIPS, 2016.
-
(2016)
NIPS
-
-
Saxena, S.1
Verbeek, J.2
-
31
-
-
0346377064
-
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
-
J. Schmidhuber. Learning to control fast-weight memories: An alternative to dynamic recurrent networks. In Neural Computation, volume 4, pages 131–139, 1992.
-
(1992)
Neural Computation
, vol.4
, pp. 131-139
-
-
Schmidhuber, J.1
-
32
-
-
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
-
33
-
-
84869201485
-
Practical Bayesian optimization of machinelearning algorithms
-
J. Snoek, H. Larochelle, and R.P. Adams. Practical bayesian optimization of machinelearning algorithms. In NIPS, 2012.
-
(2012)
NIPS
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.P.3
-
34
-
-
85071148037
-
Practical Bayesian optimization of machine learning algorithms
-
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M.M.A. Patwary, Prabhat, and R. P. Adams. Practical bayesian optimization of machine learning algorithms. In ICML, 2015.
-
(2015)
ICML
-
-
Snoek, J.1
Rippel, O.2
Swersky, K.3
Kiros, R.4
Satish, N.5
Sundaram, N.6
Patwary, M.M.A.7
Prabhat8
Adams, R.P.9
-
35
-
-
84867720412
-
-
arXiv Preprint
-
N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv Preprint arXiv: 1207.0580, 2012.
-
(2012)
Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors
-
-
Srivastava, N.1
Hinton, G.E.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
37
-
-
67650188046
-
A hypercube-based encoding for evolving large-scale neural networks
-
K.O. Stanley, D.B. D’Ambrosio, and J Gauci. A hypercube-based encoding for evolving large-scale neural networks. In Artificial Life, 15(2):185-212, 2009.
-
(2009)
Artificial Life
, vol.15
, Issue.2
, pp. 185-212
-
-
Stanley, K.O.1
D’Ambrosio, D.B.2
Gauci, J.3
-
38
-
-
85026354551
-
A genetic programming approach to designing convolutional neural network architectures
-
M. Suganuma, S. Shirakawa, and T. Nagao. A genetic programming approach to designing convolutional neural network architectures. In GECCO, 2017.
-
(2017)
GECCO
-
-
Suganuma, M.1
Shirakawa, S.2
Nagao, T.3
-
40
-
-
32444434467
-
Modeling systems with internal state using evolino
-
D. Wierstra, F.J. Gomez, and J. Schmidhuber. Modeling systems with internal state using evolino. In GECCO, 2005.
-
(2005)
GECCO
-
-
Wierstra, D.1
Gomez, F.J.2
Schmidhuber, J.3
-
41
-
-
84949636429
-
3D shapenets: A deep representation for volumetric shapes
-
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 3d shapenets: A deep representation for volumetric shapes. In CVPR, 2015.
-
(2015)
CVPR
-
-
Wu, Z.1
Song, S.2
Khosla, A.3
Yu, F.4
Zhang, L.5
Tang, X.6
Xiao, J.7
-
42
-
-
84937508363
-
How transferable are features in deep neural networks?
-
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In NIPS, 2014.
-
(2014)
NIPS
-
-
Yosinski, J.1
Clune, J.2
Bengio, Y.3
Lipson, H.4
-
44
-
-
85068717703
-
Neural architecture search with reinforcement learning
-
B. Zoph and Q. Le. Neural architecture search with reinforcement learning. In ICLR, 2017.
-
(2017)
ICLR
-
-
Zoph, B.1
Le, Q.2
|