-
1
-
-
84866690401
-
Biasing the content of hippocampal replay during sleep
-
Bendor, D., Wilson, M.A., Biasing the content of hippocampal replay during sleep. Nature Neuroscience 15 (2012), 1439–1444.
-
(2012)
Nature Neuroscience
, vol.15
, pp. 1439-1444
-
-
Bendor, D.1
Wilson, M.A.2
-
2
-
-
84925400483
-
Egocentric field-of-view localization using first-person point-of-view devices
-
In IEEE winter conference on applications of computer vision (pp.).
-
Bettadapura, V., Essa, I., & Pantofaru, C. (2015). Egocentric field-of-view localization using first-person point-of-view devices. In IEEE winter conference on applications of computer vision (pp. 626–633).
-
(2015)
, pp. 626-633
-
-
Bettadapura, V.1
Essa, I.2
Pantofaru, C.3
-
3
-
-
84989317404
-
Model-free episodic control
-
arXiv preprint arXiv:1606.04460.
-
Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J.Z., Rae, J., Wierstra, D., & Hassabis, D. (2016). Model-free episodic control. arXiv preprint arXiv:1606.04460.
-
(2016)
-
-
Blundell, C.1
Uria, B.2
Pritzel, A.3
Li, Y.4
Ruderman, A.5
Leibo, J.Z.6
Rae, J.7
Wierstra, D.8
Hassabis, D.9
-
4
-
-
0013309537
-
Online learning and stochastic approximations
-
Cambridge University Press (Chapter 2)
-
Bottou, L., Online learning and stochastic approximations. On-line learning in neural networks, 1998, Cambridge University Press, 9–42 (Chapter 2).
-
(1998)
On-line learning in neural networks
, pp. 9-42
-
-
Bottou, L.1
-
5
-
-
84872521733
-
Stochastic gradient descent tricks
-
Springer
-
Bottou, L., Stochastic gradient descent tricks. Neural networks: Tricks of the trade, 2012, Springer, 421–436.
-
(2012)
Neural networks: Tricks of the trade
, pp. 421-436
-
-
Bottou, L.1
-
6
-
-
77958549525
-
Toward an architecture for never-ending language learning
-
In Proceedings of the Twenty-Fourth AAAI conference on artificial intelligence (pp.).
-
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R., & Mitchell, T.M. (2010). Toward an architecture for never-ending language learning. In Proceedings of the Twenty-Fourth AAAI conference on artificial intelligence (pp. 1306–1313).
-
(2010)
, pp. 1306-1313
-
-
Carlson, A.1
Betteridge, J.2
Kisiel, B.3
Settles, B.4
Hruschka, E.R.5
Mitchell, T.M.6
-
7
-
-
79251550466
-
Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
-
Carr, M.F., Jadhav, S.P., Frank, L.M., Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval. Nature Neuroscience 14 (2011), 147–153.
-
(2011)
Nature Neuroscience
, vol.14
, pp. 147-153
-
-
Carr, M.F.1
Jadhav, S.P.2
Frank, L.M.3
-
8
-
-
85083953532
-
Net2net: Accelerating learning via knowledge transfer
-
In International conference on learning representations.
-
Chen, T., Goodfellow, I., & Shlens, J. (2016). Net2net: Accelerating learning via knowledge transfer. In International conference on learning representations.
-
(2016)
-
-
Chen, T.1
Goodfellow, I.2
Shlens, J.3
-
9
-
-
84898803720
-
Neil: Extracting visual knowledge from web data
-
In Proceedings of the IEEE international conference on computer vision (pp.).
-
Chen, X., Shrivastava, A., & Gupta, A. (2013). Neil: Extracting visual knowledge from web data. In Proceedings of the IEEE international conference on computer vision (pp. 1409–1416).
-
(2013)
, pp. 1409-1416
-
-
Chen, X.1
Shrivastava, A.2
Gupta, A.3
-
10
-
-
84904482223
-
Decaf: A deep convolutional activation feature for generic visual recognition
-
In Proceedings of the 31th international conference on machine learning (pp.).
-
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014). Decaf: A deep convolutional activation feature for generic visual recognition. In Proceedings of the 31th international conference on machine learning (pp. 647–655).
-
(2014)
, pp. 647-655
-
-
Donahue, J.1
Jia, Y.2
Vinyals, O.3
Hoffman, J.4
Zhang, N.5
Tzeng, E.6
Darrell, T.7
-
11
-
-
85020691200
-
From deep learning to episodic memories: Creating categories of visual experiences
-
In Proceedings of the third annual conference on advances in cognitive systems ACS.
-
Doshi, J., Kira, Z., & Wagner, A. (2015). From deep learning to episodic memories: Creating categories of visual experiences. In Proceedings of the third annual conference on advances in cognitive systems ACS.
-
(2015)
-
-
Doshi, J.1
Kira, Z.2
Wagner, A.3
-
12
-
-
80052250414
-
Adaptive subgradient methods for online learning and stochastic optimization
-
Duchi, J., Hazan, E., Singer, Y., Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12 (2011), 2121–2159.
-
(2011)
Journal of Machine Learning Research
, vol.12
, pp. 2121-2159
-
-
Duchi, J.1
Hazan, E.2
Singer, Y.3
-
13
-
-
33144466753
-
One-shot learning of object categories
-
Fei-Fei, L., Fergus, R., Perona, P., One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006), 594–611.
-
(2006)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.28
, pp. 594-611
-
-
Fei-Fei, L.1
Fergus, R.2
Perona, P.3
-
14
-
-
84908477926
-
An empirical investigation of catastrophic forgetting in gradient-based neural networks
-
arXiv preprint arXiv:1312.6211.
-
Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., & Bengio, Y. (2013). An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211.
-
(2013)
-
-
Goodfellow, I.J.1
Mirza, M.2
Xiao, D.3
Courville, A.4
Bengio, Y.5
-
15
-
-
84890543083
-
Speech recognition with deep recurrent neural networks
-
In IEEE international conference on acoustics, speech and signal processing (pp.).
-
Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In IEEE international conference on acoustics, speech and signal processing (pp. 6645–6649).
-
(2013)
, pp. 6645-6649
-
-
Graves, A.1
Mohamed, A.-R.2
Hinton, G.3
-
16
-
-
84930616355
-
Neural turing machines
-
arXiv preprint arXiv:1410.5401.
-
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural turing machines. arXiv preprint arXiv:1410.5401.
-
(2014)
-
-
Graves, A.1
Wayne, G.2
Danihelka, I.3
-
17
-
-
28644446433
-
Temporal codes and sparse representations: a key to understanding rapid processing in the visual system
-
Guyonneau, R., VanRullen, R., Thorpe, S.J., Temporal codes and sparse representations: a key to understanding rapid processing in the visual system. Journal of Physiology-Paris 98 (2004), 487–497.
-
(2004)
Journal of Physiology-Paris
, vol.98
, pp. 487-497
-
-
Guyonneau, R.1
VanRullen, R.2
Thorpe, S.J.3
-
18
-
-
84959457368
-
Automated construction of visual-linguistic knowledge via concept learning from cartoon videos
-
In Proceedings of the 29th AAAI conference on artificial intelligence (pp.).
-
Ha, J.-W., Kim, K.-M., & Zhang, B.-T. (2015). Automated construction of visual-linguistic knowledge via concept learning from cartoon videos. In Proceedings of the 29th AAAI conference on artificial intelligence (pp. 522–528).
-
(2015)
, pp. 522-528
-
-
Ha, J.-W.1
Kim, K.-M.2
Zhang, B.-T.3
-
19
-
-
84958589374
-
Deep residual learning for image recognition
-
arXiv preprint arXiv:1512.03385.
-
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385.
-
(2015)
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
20
-
-
84890539009
-
Multilingual acoustic models using distributed deep neural networks
-
In IEEE international conference on acoustics, speech and signal processing (pp.).
-
Heigold, G., Vanhoucke, V., Senior, A., Nguyen, P., Ranzato, M., Devin, M., & Dean, J. (2013). Multilingual acoustic models using distributed deep neural networks. In IEEE international conference on acoustics, speech and signal processing (pp. 8619–8623).
-
(2013)
, pp. 8619-8623
-
-
Heigold, G.1
Vanhoucke, V.2
Senior, A.3
Nguyen, P.4
Ranzato, M.5
Devin, M.6
Dean, J.7
-
21
-
-
84969506912
-
Online tracking by learning discriminative saliency map with convolutional neural network
-
In Proceedings of the 32th international conference on machine learning (pp.).
-
Hong, S., You, T., Kwak, S., & Han, B. (2015). Online tracking by learning discriminative saliency map with convolutional neural network. In Proceedings of the 32th international conference on machine learning (pp. 597–606).
-
(2015)
, pp. 597-606
-
-
Hong, S.1
You, T.2
Kwak, S.3
Han, B.4
-
22
-
-
84986313422
-
Learning to select pre-trained deep representations with Bayesian evidence framework
-
In Proceedings of the IEEE international conference on computer vision (pp.).
-
Kim, Y.-D., Jang, T., Han, B., & Choi, S. (2016). Learning to select pre-trained deep representations with Bayesian evidence framework. In Proceedings of the IEEE international conference on computer vision (pp. 5318–5326).
-
(2016)
, pp. 5318-5326
-
-
Kim, Y.-D.1
Jang, T.2
Han, B.3
Choi, S.4
-
23
-
-
85020657410
-
Multimodal residual learning for visual qa
-
arXiv preprint arXiv:1606.01455.
-
Kim, J.-H., Lee, S.-W., Kwak, D.-H., Heo, M.-O., Kim, J., Ha, J.-W., & Zhang, B.-T. (2016). Multimodal residual learning for visual qa. arXiv preprint arXiv:1606.01455.
-
(2016)
-
-
Kim, J.-H.1
Lee, S.-W.2
Kwak, D.-H.3
Heo, M.-O.4
Kim, J.5
Ha, J.-W.6
Zhang, B.-T.7
-
24
-
-
84960309242
-
Tracking the flow of hippocampal computation: Pattern separation, pattern completion, and attractor dynamics
-
Knierim, J.J., Neunuebel, J.P., Tracking the flow of hippocampal computation: Pattern separation, pattern completion, and attractor dynamics. Neurobiology of Learning and Memory 129 (2016), 38–49.
-
(2016)
Neurobiology of Learning and Memory
, vol.129
, pp. 38-49
-
-
Knierim, J.J.1
Neunuebel, J.P.2
-
25
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
In Advances in neural information processing systems (pp.).
-
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
-
(2012)
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
26
-
-
84974531647
-
What learning systems do intelligent agents need? complementary learning systems theory updated
-
Kumaran, D., Hassabis, D., McClelland, J.L., What learning systems do intelligent agents need? complementary learning systems theory updated. Trends in Cognitive Sciences 20 (2016), 512–534.
-
(2016)
Trends in Cognitive Sciences
, vol.20
, pp. 512-534
-
-
Kumaran, D.1
Hassabis, D.2
McClelland, J.L.3
-
27
-
-
85006173402
-
Dual-memory deep learning architectures for lifelong learning of everyday human behaviors
-
In Proceedings of the international joint conference on artificial intelligence (pp.).
-
Lee, S.-W., Lee, C.-Y., Kwak, D.H., Kim, J., Kim, J., & Zhang, B.-T. (2016). Dual-memory deep learning architectures for lifelong learning of everyday human behaviors. In Proceedings of the international joint conference on artificial intelligence (pp. 1669–1675).
-
(2016)
, pp. 1669-1675
-
-
Lee, S.-W.1
Lee, C.-Y.2
Kwak, D.H.3
Kim, J.4
Kim, J.5
Zhang, B.-T.6
-
28
-
-
85083953135
-
Network in network
-
In International conference on learning representations.
-
Lin, M., Chen, Q., & Yan, S. (2014). Network in network. In International conference on learning representations.
-
(2014)
-
-
Lin, M.1
Chen, Q.2
Yan, S.3
-
30
-
-
45849111049
-
An incremental feature learning algorithm based on least square support vector machine
-
In Proceedings of the 2nd annual international workshop on frontiers in algorithmics (pp.).
-
Liu, X., Zhang, G., Zhan, Y., & Zhu, E. (2008). An incremental feature learning algorithm based on least square support vector machine. In Proceedings of the 2nd annual international workshop on frontiers in algorithmics (pp. 330–338).
-
(2008)
, pp. 330-338
-
-
Liu, X.1
Zhang, G.2
Zhan, Y.3
Zhu, E.4
-
31
-
-
0029340352
-
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
-
McClelland, J.L., McNaughton, B.L., O'Reilly, R.C., Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 1995, 419.
-
(1995)
Psychological Review
, vol.102
, pp. 419
-
-
McClelland, J.L.1
McNaughton, B.L.2
O'Reilly, R.C.3
-
32
-
-
84954123466
-
Never-ending learning
-
In Proceedings of the Twenty-Ninth AAAI conference on artificial intelligence (pp.).
-
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., & Krishnamurthy, J. et al. (2015). Never-ending learning. In Proceedings of the Twenty-Ninth AAAI conference on artificial intelligence (pp. 2302–2310).
-
(2015)
, pp. 2302-2310
-
-
Mitchell, T.1
Cohen, W.2
Hruschka, E.3
Talukdar, P.4
Betteridge, J.5
Carlson, A.6
Dalvi, B.7
Gardner, M.8
Kisiel, B.9
Krishnamurthy, J.10
-
33
-
-
84986296977
-
Learning multi-domain convolutional neural networks for visual tracking
-
In Proceedings of the IEEE international conference on computer vision (pp.).
-
Nam, H., & Han, B. (2016). Learning multi-domain convolutional neural networks for visual tracking. In Proceedings of the IEEE international conference on computer vision (pp. 4293–4302).
-
(2016)
, pp. 4293-4302
-
-
Nam, H.1
Han, B.2
-
34
-
-
84973879016
-
Learning deconvolution network for semantic segmentation
-
In Proceedings of the IEEE international conference on computer vision (pp.).
-
Noh, H., Hong, S., & Han, B. (2015). Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision (pp. 1520–1528).
-
(2015)
, pp. 1520-1528
-
-
Noh, H.1
Hong, S.2
Han, B.3
-
35
-
-
84906085462
-
Complementary learning systems
-
O'Reilly, R.C., Bhattacharyya, R., Howard, M.D., Ketz, N., Complementary learning systems. Cognitive Science 38 (2014), 1229–1248.
-
(2014)
Cognitive Science
, vol.38
, pp. 1229-1248
-
-
O'Reilly, R.C.1
Bhattacharyya, R.2
Howard, M.D.3
Ketz, N.4
-
36
-
-
27944478140
-
Online bagging and boosting
-
In IEEE international conference on systems, man and cybernetics (pp.).
-
Oza, N.C. (2005). Online bagging and boosting. In IEEE international conference on systems, man and cybernetics (pp. 2340–2345).
-
(2005)
, pp. 2340-2345
-
-
Oza, N.C.1
-
37
-
-
0035521110
-
Learn++: An incremental learning algorithm for supervised neural networks
-
Polikar, R., Upda, L., Upda, S.S., Honavar, V., Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews 31 (2001), 497–508.
-
(2001)
IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews
, vol.31
, pp. 497-508
-
-
Polikar, R.1
Upda, L.2
Upda, S.S.3
Honavar, V.4
-
38
-
-
84897541086
-
Ella: An efficient lifelong learning algorithm
-
In Proceedings of the 30th international conference on machine learning (pp.).
-
Ruvolo, P.L., & Eaton, E. (2013). Ella: An efficient lifelong learning algorithm. In Proceedings of the 30th international conference on machine learning (pp. 507–515).
-
(2013)
, pp. 507-515
-
-
Ruvolo, P.L.1
Eaton, E.2
-
39
-
-
84946037134
-
Convolutional, long short-term memory, fully connected deep neural networks
-
In IEEE international conference on acoustics, speech and signal processing (pp.).
-
Sainath, T.N., Vinyals, O., Senior, A., & Sak, H. (2015). Convolutional, long short-term memory, fully connected deep neural networks. In IEEE international conference on acoustics, speech and signal processing (pp. 4580–4584).
-
(2015)
, pp. 4580-4584
-
-
Sainath, T.N.1
Vinyals, O.2
Senior, A.3
Sak, H.4
-
41
-
-
84938920775
-
Scalable Bayesian optimization using deep neural networks
-
In Proceedings of the 32nd international conference on machine learning (pp.).
-
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., & Adams, R. (2015). Scalable Bayesian optimization using deep neural networks. In Proceedings of the 32nd international conference on machine learning (pp. 2171–2180).
-
(2015)
, pp. 2171-2180
-
-
Snoek, J.1
Rippel, O.2
Swersky, K.3
Kiros, R.4
Satish, N.5
Sundaram, N.6
Patwary, M.7
Prabhat, M.8
Adams, R.9
-
42
-
-
84965143740
-
End-to-end memory networks
-
Sukhbaatar, S., Weston, J., Fergus, R., et al. End-to-end memory networks. Advances in neural information processing systems, 2015, 2440–2448.
-
(2015)
Advances in neural information processing systems
, pp. 2440-2448
-
-
Sukhbaatar, S.1
Weston, J.2
Fergus, R.3
-
43
-
-
84878402147
-
Lstm neural networks for language modeling
-
In Interspeech (pp.).
-
Sundermeyer, M., Schlüter, R., & Ney, H. (2012). Lstm neural networks for language modeling. In Interspeech (pp. 194–197).
-
(2012)
, pp. 194-197
-
-
Sundermeyer, M.1
Schlüter, R.2
Ney, H.3
-
44
-
-
84928547704
-
Sequence to sequence learning with neural networks
-
In Advances in neural information processing systems (pp.).
-
Sutskever, I., Vinyals, O., & Le, Q.V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104–3112).
-
(2014)
, pp. 3104-3112
-
-
Sutskever, I.1
Vinyals, O.2
Le, Q.V.3
-
45
-
-
84937522268
-
Going deeper with convolutions
-
In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.).
-
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
-
(2015)
, pp. 1-9
-
-
Szegedy, C.1
Liu, W.2
Jia, Y.3
Sermanet, P.4
Reed, S.5
Anguelov, D.6
Erhan, D.7
Vanhoucke, V.8
Rabinovich, A.9
-
46
-
-
0042496037
-
Discovering structure in multiple learning tasks: The tc algorithm
-
In Proceedings of the 13th international conference on machine learning (pp.).
-
Thrun, S., & O'Sullivan, J. (1996). Discovering structure in multiple learning tasks: The tc algorithm. In Proceedings of the 13th international conference on machine learning (pp. 489–497).
-
(1996)
, pp. 489-497
-
-
Thrun, S.1
O'Sullivan, J.2
-
47
-
-
0026849958
-
Computational constraints suggest the need for two distinct input systems to the hippocampal ca3 network
-
Treves, A., Rolls, E.T., Computational constraints suggest the need for two distinct input systems to the hippocampal ca3 network. Hippocampus 2 (1992), 189–199.
-
(1992)
Hippocampus
, vol.2
, pp. 189-199
-
-
Treves, A.1
Rolls, E.T.2
-
48
-
-
84962815548
-
Matconvnet–convolutional neural networks for matlab
-
In Proceedings of the ACM international conference on multimedia (pp.).
-
Vedaldi, A., & Lenc, K. (2015). Matconvnet–convolutional neural networks for matlab. In Proceedings of the ACM international conference on multimedia (pp. 689–692).
-
(2015)
, pp. 689-692
-
-
Vedaldi, A.1
Lenc, K.2
-
49
-
-
84999036961
-
Network morphism
-
In Proceedings of the 33th international conference on machine learning.
-
Wei, T., Wang, C., Rui, R., & Chen, C.W. (2016). Network morphism. In Proceedings of the 33th international conference on machine learning.
-
(2016)
-
-
Wei, T.1
Wang, C.2
Rui, R.3
Chen, C.W.4
-
50
-
-
85020678483
-
Memory networks
-
In International conference on learning representations.
-
Weston, J., Chopra, S., & Bordes, A. (2014). Memory networks. In International conference on learning representations.
-
(2014)
-
-
Weston, J.1
Chopra, S.2
Bordes, A.3
-
51
-
-
84902213589
-
Performance-optimized hierarchical models predict neural responses in higher visual cortex
-
Yamins, D.L., Hong, H., Cadieu, C.F., Solomon, E.A., Seibert, D., DiCarlo, J.J., Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences 111 (2014), 8619–8624.
-
(2014)
Proceedings of the National Academy of Sciences
, vol.111
, pp. 8619-8624
-
-
Yamins, D.L.1
Hong, H.2
Cadieu, C.F.3
Solomon, E.A.4
Seibert, D.5
DiCarlo, J.J.6
-
52
-
-
84937508363
-
How transferable are features in deep neural networks?
-
In Advances in neural information processing systems (pp.).
-
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (pp. 3320–3328).
-
(2014)
, pp. 3320-3328
-
-
Yosinski, J.1
Clune, J.2
Bengio, Y.3
Lipson, H.4
-
53
-
-
84990820289
-
Video paragraph captioning using hierarchical recurrent neural networks
-
arXiv preprint arXiv:1510.07712.
-
Yu, H., Wang, J., Huang, Z., Yang, Y., & Xu, W. (2015). Video paragraph captioning using hierarchical recurrent neural networks. arXiv preprint arXiv:1510.07712.
-
(2015)
-
-
Yu, H.1
Wang, J.2
Huang, Z.3
Yang, Y.4
Xu, W.5
-
54
-
-
84906489074
-
Visualizing and understanding convolutional networks
-
In Proceedings of European conference on computer vision (pp.).
-
Zeiler, M.D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Proceedings of European conference on computer vision (pp. 818–833).
-
(2014)
, pp. 818-833
-
-
Zeiler, M.D.1
Fergus, R.2
-
55
-
-
47849109461
-
Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory
-
Zhang, B.-T., Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory. IEEE Computational Intelligence Magazine 3 (2008), 49–63.
-
(2008)
IEEE Computational Intelligence Magazine
, vol.3
, pp. 49-63
-
-
Zhang, B.-T.1
-
56
-
-
84881567005
-
Sparse population code models of word learning in concept drift
-
In Proceedings of the 34th annual conference of cogitive science society (pp.).
-
Zhang, B.-T., Ha, J.-W., & Kang, M. (2012). Sparse population code models of word learning in concept drift. In Proceedings of the 34th annual conference of cogitive science society (pp. 1221–1226).
-
(2012)
, pp. 1221-1226
-
-
Zhang, B.-T.1
Ha, J.-W.2
Kang, M.3
-
57
-
-
84954202765
-
Online incremental feature learning with denoising autoencoders
-
In International conference on artificial intelligence and statistics (pp.).
-
Zhou, G., Sohn, K., & Lee, H. (2012). Online incremental feature learning with denoising autoencoders. In International conference on artificial intelligence and statistics (pp. 1453–1461).
-
(2012)
, pp. 1453-1461
-
-
Zhou, G.1
Sohn, K.2
Lee, H.3
-
58
-
-
1942484421
-
Online convex programming and generalized infinitesimal gradient ascent
-
In Proceedings of the 20th international conference on machine learning (pp.).
-
Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th international conference on machine learning (pp. 928–936).
-
(2003)
, pp. 928-936
-
-
Zinkevich, M.1
|