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The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, unified way for prediction purposes.
Avrithis, Y., Tsapatsoulis, N., Kollias, S.: Broadcast news parsing using visual cues: A robust face detection approach. In: 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No. 00TH8532). vol. 3, pp. 1469–1472. IEEE (2000)
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Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. Tech. rep., Stanford (2006)
2006
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Rapantzikos, K., Tsapatsoulis, N., Avrithis, Y., Kollias, S.: Bottom-up spatiotemporal visual attention model for video analysis. IET Image Processing 1
2007
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2009
Earlier work this paper cites.
Kollia, I., Simou, N., Stafylopatis, A., Kollias, S.: Semantic image analysis using a symbolic neural architecture. Image Analysis & Stereology 29
2010
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Marek, K., et al.: The parkinson progression marker initiative (ppmi). Progress in Neurobiology 95
2011
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Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014)
2014
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Glimm, B., Kazakov, Y., Kollia, I., Stamou, G.: Lower and upper bounds for sparql queries over owl ontologies. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
2015
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Kappeler, A., et al.: Combining deep learning and unsupervised clustering to improve scene recognition performance. In: 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). pp. 1–6. IEEE (2015)
2015
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Kollias, D., Marandianos, G., Raouzaiou, A., Stafylopatis, A.G.: Interweaving deep learning and semantic techniques for emotion analysis in human-machine interaction. In: 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). pp. 1–6. IEEE (2015)
2015
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Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd International Conference on Machine Learning. vol. 37, pp. 97–105. PMLR (07–09 Jul 2015)
2015
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Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: European Conference on Computer Vision. pp. 597–613. Springer (2016)
2016
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Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
2016
Cited alongside, same era.
Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: European conference on computer vision. pp. 443–450. Springer (2016)
2016
Cited alongside, same era.
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International conference on machine learning. pp. 478–487 (2016)
2016
Cited alongside, same era.
Azizi, S., et al.: Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations. International journal of computer assisted radiology and surgery 12
2017
Cited alongside, same era.
Kollias, D., Yu, M., Tagaris, A., Leontidis, G., Stafylopatis, A., Kollias, S.: Adaptation and contextualization of deep neural network models. In: 2017 IEEE symposium series on computational intelligence (SSCI). pp. 1–8. IEEE (2017)
Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G., Kollias, S.: Machine learning for neurodegenerative disorder diagnosis—survey of practices and launch of benchmark dataset. International Journal on Artificial Intelligence Tools 27
2018
Later among the works it cites.
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A Survey on Deep Transfer Learning: 27th International Conference on Artificial Neural Networks, 2018, Proceedings, Part III, pp. 270–279 (10 2018)
2018
Later among the works it cites.
Esteva A., Robicquet A., e.a.: A guide to deep learning in healthcare. Nature medicine 25
2019
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Jiang, S., Kaiser, M., Yang, S., Kollias, S., Krasnogor, N.: A scalable test suite for continuous dynamic multiobjective optimization. IEEE transactions on cybernetics 50
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Tagaris, A., Kollias, D., Stafylopatis, A.: Assessment of parkinson’s disease based on deep neural networks. In: International Conference on Engineering Applications of Neural Networks. pp. 391–403. Springer (2017)
2017
Cited alongside, same era.
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: international conference on machine learning. pp. 3861–3870 (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex & Intelligent Systems 4
2018
Cited alongside, same era.
Mei, W., Deng, W.: Deep visual domain adaptation: A survey. Neurocomputing (02 2018)
2018
Cited alongside, same era.
Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., Long, J.: A survey of clustering with deep learning: From the perspective of network architecture. IEEE Access 6
2018
Cited alongside, same era.
Ribeiro, F.D.S., Gong, L., Calivá, F., Swainson, M., Gudmundsson, K., Yu, M., Leontidis, G., Ye, X., Kollias, S.: An end-to-end deep neural architecture for optical character verification and recognition in retail food packaging. In: 2018 25th IEEE International Conference on Image Processing (ICIP). pp. 2376–2380. IEEE (2018)
2018
Cited alongside, same era.
Kollia, I., Stafylopatis, A.G., Kollias, S.: Predicting parkinson’s disease using latent information extracted from deep neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2019)
2019
Later among the works it cites.
Ribeiro, F.D.S., Calivá, F., Swainson, M., Gudmundsson, K., Leontidis, G., Kollias, S.: Deep bayesian self-training. Neural Computing and Applications pp. 1–17 (2019)
2019
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2019
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2020
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He, X., Yang, X., Zhang, S., Zhao, J., Zhang, Y., Xing, E., Xie, P.: Sample-efficient deep learning for covid-19 diagnosis based on ct scans. medRxiv (2020)
2020
Closest in time.
Ribeiro, F.D.S., Leontidis, G., Kollias, S.D.: Capsule routing via variational bayes. In: AAAI. pp. 3749–3756 (2020)
2020
Closest in time.