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Volumn 92, Issue , 2017, Pages 17-28

Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors

Author keywords

Complementary learning systems; Deep neural networks; Dual memory architecture; Hypernetworks; Lifelog dataset; Online learning

Indexed keywords

DEEP LEARNING; DEEP NEURAL NETWORKS; E-LEARNING; GLASS; INTELLIGENT AGENTS; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; MEMORY ARCHITECTURE; NETWORK ARCHITECTURE; ONLINE SYSTEMS; WEARABLE TECHNOLOGY;

EID: 85014999216     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2017.02.008     Document Type: Article
Times cited : (10)

References (58)
  • 1
    • 84866690401 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 4
    • 0013309537 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 14
    • 84908477926 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 24
    • 84960309242 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고
    • 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
  • 33
    • 84986296977 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 36
    • 27944478140 scopus 로고    scopus 로고
    • 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
  • 38
    • 84897541086 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 43
    • 84878402147 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 52
    • 84937508363 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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


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