المؤلفون: 黃名儀, Huang, Ming-I

المساهمون: 郁方 洪智鐸, Yu, Fang Hong, Chih-Duo

مصطلحات موضوعية: 政治大學, 深度神經網路, 動態符號執行測試, 公平性測試, NCCU, Concolic Testing, Fairness Testing, Deep Neural Networks

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Relation: Aggarwal, A., Lohia, P., Nagar, S., Dey, K., and Saha, D. (2019). Black box fairness testing of machine learning models. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 625–635. Albarghouthi, A., D’Antoni, L., Drews, S., and Nori, A. V. (2017). Fairsquare: prob- abilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA):1–30. Awwad, Y., Fletcher, R., Frey, D., Gandhi, A., Najafian, M., and Teodorescu, M. (2020). Exploring fairness in machine learning for international development. Technical report, CITE MIT D-Lab. Bastani, O., Zhang, X., and Solar-Lezama, A. (2019). Probabilistic verification of fairness properties via concentration. Proceedings of the ACM on Programming Languages, 3(OOPSLA):1–27. Biswas, S. and Rajan, H. (2020). Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. In Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pages 642–653. Biswas, S. and Rajan, H. (2021). Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline. In Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pages 981–993. Biswas, S. and Rajan, H. (2023). Fairify: Fairness verification of neural networks. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), pages 1546–1558. IEEE. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016). End to end learning for self- driving cars. arXiv preprint arXiv:1604.07316. 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In Proceedings of the 2017 11th Joint meeting on foundations of software engineering, pages 498–510. Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., and Vechev, M. (2018). Ai2: Safety and robustness certification of neural networks with abstract inter- pretation. In 2018 IEEE symposium on security and privacy (SP), pages 3–18. IEEE. Gohar, U., Biswas, S., and Rajan, H. (2023). Towards understanding fairness and its composition in ensemble machine learning. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), pages 1533–1545. IEEE. Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adver- sarial examples. arXiv preprint arXiv:1412.6572. Hort, M., Zhang, J. M., Sarro, F., and Harman, M. (2021). Fairea: A model behaviour mutation approach to benchmarking bias mitigation methods. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 994–1006. John, P. G., Vijaykeerthy, D., and Saha, D. (2020). Verifying individual fairness in machine learning models. In Conference on Uncertainty in Artificial Intelligence, pages 749– 758. PMLR. Katz, G., Barrett, C., Dill, D. L., Julian, K., and Kochenderfer, M. J. (2017). Reluplex: An efficient smt solver for verifying deep neural networks. In Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I 30, pages 97–117. Springer. Katz, G., Huang, D. A., Ibeling, D., Julian, K., Lazarus, C., Lim, R., Shah, P., Thakoor, S., Wu, H., Zeljić, A., et al. (2019). The marabou framework for verification and analysis of deep neural networks. In Computer Aided Verification: 31st International Conference, CAV 2019, New York City, NY, USA, July 15-18, 2019, Proceedings, Part I 31, pages 443–452. Springer. Khedr, H. and Shoukry, Y. (2023). Certifair: A framework for certified global fairness of neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 8237–8245. Kurakin, A., Goodfellow, I. J., and Bengio, S. (2018). Adversarial examples in the phys- ical world. In Artificial intelligence safety and security, pages 99–112. Chapman and Hall/CRC. Lam, L. and Suen, S. (1997). Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(5):553–568. Li, T., Xie, X., Wang, J., Guo, Q., Liu, A., Ma, L., and Liu, Y. (2023). Faire: Repair- ing fairness of neural networks via neuron condition synthesis. ACM Transactions on Software Engineering and Methodology, 33(1):1–24. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88. Ma, L., Juefei-Xu, F., Zhang, F., Sun, J., Xue, M., Li, B., Chen, C., Su, T., Li, L., Liu, Y., et al. (2018). Deepgauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, pages 120–131. Mohammadi, K., Sivaraman, A., and Farnadi, G. (2023). Feta: Fairness enforced verify- ing, training, and predicting algorithms for neural networks. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–11. Pei, K., Cao, Y., Yang, J., and Jana, S. (2017). Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles, pages 1–18. 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In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pages 1436–1447. IEEE. Zhang, L., Zhang, Y., and Zhang, M. (2021). Efficient white-box fairness testing through gradient search. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 103–114. Zhang, P., Wang, J., Sun, J., Dong, G., Wang, X., Wang, X., Dong, J. S., and Dai, T. (2020). White-box fairness testing through adversarial sampling. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, pages 949–960. Zheng, H., Chen, Z., Du, T., Zhang, X., Cheng, Y., Ji, S., Wang, J., Yu, Y., and Chen, J. (2022). Neuronfair: Interpretable white-box fairness testing through biased neuron identification. In 44th International Conference on Software Engineering, pages 1–13, New York, NY, USA. ACM.; G0111356047; https://nccur.lib.nccu.edu.tw//handle/140.119/153165; https://nccur.lib.nccu.edu.tw/bitstream/140.119/153165/1/604701.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/153165
https://nccur.lib.nccu.edu.tw/bitstream/140.119/153165/1/604701.pdf

المؤلفون: 胡程鈞, Hu, Cheng-Jun

المساهمون: 呂桔誠 林士貴, Lyu, Jye-Cherng Lin, Shih-Kuei

مصطلحات موضوعية: 房價預測, 機器學習, 深度學習, 深度神經網路, 生成對抗網路, 隨機森林, XGBoost, Housing Price Prediction, Machine Learning, Deep Learning, Deep Neural Network, Generative Adversarial Network, Random Forest

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Relation: 1. Aggarwal, K., Kirchmeyer, M., Yadav, P., Keerthi, S., and Gallinari, P. (2020). Benchmarking Regression Methods: A Comparison with CGAN. arXiv preprint arXiv:1905.12868. 2. Antipov, E.A. and Pokryshevskaya, E.B. (2012). Mass Appraisal of Residential Apartments: An Application of Random Forest for Valuation and A CART-Based Approach for Model Diagnostics. Expert Systems with Applications, 39(2), 1772-1778. 3. Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein Gan. arXiv preprint arXiv:1701.07875, 2(3), 4. 4. Aycock, S.A. (2000). The Impact of Fairness, Reference Point, and Human Decision Processing on Negotiation. Journal of Financial Service professionals, 54(2), 76-81. 5. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. 6. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees. 7. Chen, T.Q. and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. 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Public Library of Science, 16(2), 1-20. 14. Frew, J. and Jud, G.D. (2003). Estimating the Value of Apartment Buildings. Journal of Real Estate Research, 25(1), 77-86. 15. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. Advances in neural information processing systems, 27. 16. Goodman, A.C. and Thibodeau, T. (2003). Housing Market Segmentation and Hedonic Prediction Accuracy. Journal of Housing Economics, 12(3), 181-201. 17. Gui, J., Sun, Z., Wen, Y., Tao, D., and Ye, J. (2015). A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. arXiv preprint arXiv:2001.06937. 18. Harrison, D. and Rubinfeld, D.L. (1978). Hedonic Housing Prices and the Demand for Clean Air. Journal of Environmental Economics and Management, 5(1), 81-102. 19. Ho, W.K.O., Tang, B., and Wong S.W. (2021). Predicting Property Prices with Machine Learning Algorithms. Journal of Property Research, 38(1), 48-70. 20. Hsieh, C.F. and Lin, T.C. (2021). Housing Price Prediction by Using Generative Adversarial Networks. In 2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 49-53. 21. Huang, H., Yu, P.S., and Wang, C. (2018). An Introduction to Image Synthesis with Generative Adversarial Nets. arXiv preprint arXiv:1803.04469. 22. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. 23. Lin, H., Chen, C., Huang, G., and Jafari, A. (2021). Stock Price Prediction Using Generative Adversarial Networks. Journal of Computer Science, 17(3), 188-196. 24. Liu, B., Lv, J., Fan, X., Luo, J., and Zou, T. (2022). Application of an Improved DCGAN for Image Generation. Mobile Information Systems, 2022. 25. Liu, Z., Song, A., Sabar, N., Qin, K., Izuhara, T. (2023). Evolution Enhancing Property Price Prediction by Generating Artificial Transaction Data. 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Tay, D.P.H. and Ho, D.K.H. (1991). Artificial Intelligence and The Mass Appraisal of Residential Apartments. Journal of Property Valuation and Investment, 10, 525 -539. 39. Xu, X. and Zhang, Y. (2021). House Price Forecasting with Neural Networks. Intelligent Systems with Applications, 12, 200052. 40. Yilmaz, B. (2023). Housing GANs: Deep Generation of Housing Market Data. Computational Economics, 1-16. 41. Yiu, C.Y., Tang, B.S., Chiang, Y.H., and Choy, L.H.T. (2006). Alternative Theories of Appraisal Bias. Journal of Real Estate Literature, 14(3), 321-344. 42. Yu, L., Jiao, C., Xin, H., Wang, Y., and Wang, K. (2018). Prediction on Housing Price Based on Deep Learning. International Journal of Computer and Information Engineering, 12(2), 90-99. 43. Zhang, B., Sui, W., Huang, Z., Qi, M., and Li, M. (2023). Normalizing Flow based Uncertainty Estimation for Deep Regression Analysis. Available at SSRN 4698811. 44. Zhang, K., Zhong, G., Dong, J., Wang, S. and Wang, Y. (2018). Stock Market Prediction Based on Generative Adversarial Network. Procedia Computer Science 147, 400-406. 45. Zheng, T., Song. L., Wang, J., Teng, W., Xu, X., and Ma, C. (2020). Data Synthesis Using Dual Discriminator Conditional Generative Adversarial Networks for Imbalanced Fault Diagnosis of Rolling Bearings. Measurement, 158(1), 107741.; G0111ZB1009; https://nccur.lib.nccu.edu.tw//handle/140.119/150175; https://nccur.lib.nccu.edu.tw/bitstream/140.119/150175/1/100901.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/150175
https://nccur.lib.nccu.edu.tw/bitstream/140.119/150175/1/100901.pdf

المؤلفون: 陳威妤, Chen, Wei-Yu

المساهمون: 廖文宏, Liao, Wen-Hung

مصطلحات موضوعية: 語者辨識, 跨語言音料庫, 深度神經網路, 轉換器, 對抗例攻擊, Speaker Recognition, Cross-lingual Dataset, Deep Neural Networks, Transformer, Adversarial Attack

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Relation: [1] Eberhard, D. M., Simons, G. F., Fennig, C. D. (eds).Ethnologue: Languages of the World. 23rd Edition. Dallas, TX: SIL International, 2020.\n[2] Grenier, Gilles Zhang, et al “The value of language skills”, IZA World of Labor, 2021.\n[3] Nawaz, Shah, et al. "Cross-modal Speaker Verification and Recognition: A Multilingual Perspective." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.\n[4] Wu, Yi-Chieh, and Wen-Hung Liao. "Toward Text-independent Cross-lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset." 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021.\n[5] Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.\n[6] Parmar, Niki, et al. "Image Transformer." International Conference on Machine Learning. PMLR, 2018.\n[7] Dosovitskiy, Alexey, et al. "An image is worth 16x16 words: transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).\n[8] Wu, Haiping, et al. "Cvt: Introducing convolutions to Vision Transformers." arXiv preprint arXiv:2103.15808 (2021).\n[9] Kua, Jia Min Karen, Julien Epps, and Eliathamby Ambikairajah. "i-Vector with sparse representation classification for speaker verification." Speech Communication 55.5 (2013): 707-720.\n[10] Variani, Ehsan, et al. "Deep neural networks for small footprint text-dependent speaker verification." 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2014.\n[11] Snyder, David, et al. "Deep Neural Network Embeddings for Text-Independent Speaker Verification." Interspeech. 2017.\n[12] Ravanelli, Mirco, and Yoshua Bengio. "Speaker recognition from raw waveform with sincnet." 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018.\n[13] Ghezaiel, Wajdi, Luc Brun, and Olivier Lézoray. "Hybrid network for end-to-end text-independent speaker identification." 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021.\n[14] LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.\n[15] Nagrani, Arsha, Joon Son Chung, and Andrew Zisserman. "Voxceleb: a large-scale speaker identification dataset." arXiv preprint arXiv:1706.08612 (2017).\n[16] Chatfield, Ken, et al. "Return of the devil in the details: Delving deep into convolutional nets." arXiv preprint arXiv:1405.3531 (2014).\n[17] Ding, Shaojin, et al. "Autospeech: Neural architecture search for speaker recognition." arXiv preprint arXiv:2005.03215 (2020).\n[18] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014.\n[19] Gong, Yuan, Yu-An Chung, and James Glass. 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"ASVspoof 2019: a large-scale public database of synthetized, converted and replayed speech." Computer Speech & Language （2020）: 101114.\n[26] Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 （2014）\n[27] Olivier, Raphael, Bhiksha Raj, and Muhammad Shah. "High-Frequency Adversarial Defense for Speech and Audio." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.\n[28] Madry, Aleksander, et al. "Towards deep learning models resistant to adversarial attacks." arXiv preprint arXiv:1706.06083 (2017).\n[29] Panayotov, Vassil, et al. "Librispeech: an asr corpus based on public domain audio books." 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2015.\n[30] Salamon, Justin, Christopher Jacoby, and Juan Pablo Bello. "A dataset and taxonomy for urban sound research." Proceedings of the 22nd ACM international conference on Multimedia. 2014.\n[31] Shao, Rulin, et al. "On the adversarial robustness of visual transformer." arXiv preprint arXiv:2103.15670 (2021).\n[32] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.\n[33] Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014).\n[34] Jati, Arindam, et al. "Adversarial attack and defense strategies for deep speaker recognition systems." Computer Speech & Language 68 (2021): 101199.; G0108753131; https://nccur.lib.nccu.edu.tw//handle/140.119/139218; https://nccur.lib.nccu.edu.tw/bitstream/140.119/139218/1/313101.pdf

الاتاحة: https://nccur.lib.nccu.edu.tw//handle/140.119/139218
https://doi.org/10.6814/NCCU202200288
https://nccur.lib.nccu.edu.tw/bitstream/140.119/139218/1/313101.pdf

المؤلفون: 林承憲, Lin, Cheng-Shian

المساهمون: 林政宏, Lin, Cheng-Hung

مصطلحات موضوعية: 深度神經網路, 資料增強, 分布外偵測, 異常偵測, 離群偵測, deep neural network, data augmentation, out-of-distribution detection, anomaly detection, outlier detection

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Relation: 60775054H-38383; https://etds.lib.ntnu.edu.tw/thesis/detail/a775f2d5bdf2be29b9279ae9b928e822/; http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/116951

الاتاحة: http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/116951
https://hdl.handle.net/20.500.12235/116951
https://etds.lib.ntnu.edu.tw/thesis/detail/a775f2d5bdf2be29b9279ae9b928e822/

المؤلفون: 謝承璋, Hsieh, Cheng-Chang

المساهمون: 林政宏, Lin, Cheng-Hung

مصطلحات موضوعية: 視網膜前膜, 光學相干斷層掃描, 光學相干斷層掃描血管成像術, 眼底螢光血管攝影, 深度神經網路, 多流網路, 視力偵測, deep learning, multi-stream network, visual acuity, Epiretinal membranes, optical coherence tomography, optical coherence tomography angiography, fundus fluorescence angiography

وصف الملف: application/pdf

Relation: 60775055H-40308; https://etds.lib.ntnu.edu.tw/thesis/detail/1fb50ea8d2716a603fdc50466e37b66a/; http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/116952

الاتاحة: http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/116952
https://hdl.handle.net/20.500.12235/116952
https://etds.lib.ntnu.edu.tw/thesis/detail/1fb50ea8d2716a603fdc50466e37b66a/

المؤلفون: 張立廉, CHANG, LI-LIEN

المساهمون: 資訊與通訊系, 朱鴻棋, CHU, HUNG-CHI

مصطلحات موضوعية: 深度神經網路, 流量分類, 應用服務, 深度學習, DNN, Traffic Classification, Application Services, Deep Learning

وصف الملف: 2050681 bytes; application/pdf

Relation: 110CYUT0652004; http://ir.lib.cyut.edu.tw:8080/handle/310901800/41236; http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/41236/1/110CYUT0652004-004.pdf

الاتاحة: http://ir.lib.cyut.edu.tw:8080/handle/310901800/41236
http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/41236/1/110CYUT0652004-004.pdf

المؤلفون: 陳冠文, CHEN, KUAN-WEN

المساهمون: 徐建業, HSU, CHIEN-YEH, 資訊管理研究所

مصطلحات موضوعية: 胰臟癌, 逐步迴歸, 羅吉斯迴歸, 深度神經網路, 集成學習, 風險識別模型, Pancreatic Cancer, Stepwise Regression, Logistic Regression, Deep Neural Networks, Ensemble Learning, Identification Model

Time: 15

Relation: http://140.131.94.7/handle/987654321/7778

الاتاحة: http://140.131.94.7/handle/987654321/7778

المؤلفون: 陳奕臣, CHEN, YI-CHEN

المساهمون: 葉建華, 資訊工程學系碩士班

مصطلحات موضوعية: 生物辨識, 心電圖, 不平衡資料處理, 深度神經網路

Time: 34

وصف الملف: 9898500 bytes; application/pdf

Relation: http://ir.lib.au.edu.tw/dspace/handle/987654321/5697

الاتاحة: http://ir.lib.au.edu.tw/dspace/handle/987654321/5697

المؤلفون: 林允文

المساهمون: 資訊工程學系, 吳宗憲, Wu, Chung-Hsien

مصطلحات موضوعية: 語音降噪, 自動語音辨識, 音訊品質, 音素可信度, STOI, PESQ, 深度神經網路, 卷積神經網路, speech enhancement, automatic speech recognition, speech quality, phone confidence, deep neural network, convolutional neural network, psy, lang

Relation: http://ir.lib.ncku.edu.tw/handle/987654321/200936

الاتاحة: http://ir.lib.ncku.edu.tw/handle/987654321/200936

المؤلفون: 楊佳玲

مصطلحات موضوعية: 特徵提取, 電壓衰退, 機器學習, 深度神經網路, 晶片電力網路分 析, Feature selection, IR drop, Machine learning, Neural network, Power/grid network analysis

وصف الملف: 169 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/77723; http://ir.lib.ntust.edu.tw/bitstream/987654321/77723/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/77723
http://ir.lib.ntust.edu.tw/bitstream/987654321/77723/1/index.html

المؤلفون: 謝義桐

مصطلحات موضوعية: 對抗式樣本, 對抗式攻擊, 深度神經網路, Adversarial example, Adversarial attack, Deep Neural Networks

وصف الملف: 169 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/77541; http://ir.lib.ntust.edu.tw/bitstream/987654321/77541/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/77541
http://ir.lib.ntust.edu.tw/bitstream/987654321/77541/1/index.html

المؤلفون: 吳紫源

مصطلحات موضوعية: 翻拍人臉, 二維圖像防偽檢測, 局部二值模式, 圖像失真分析, 深度神經網路, remaking face images, face liveness detection, local binary pattern, image distortion analysis, deep neural network

وصف الملف: 169 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/77478; http://ir.lib.ntust.edu.tw/bitstream/987654321/77478/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/77478
http://ir.lib.ntust.edu.tw/bitstream/987654321/77478/1/index.html

المؤلفون: 張家綺

مصطلحات موضوعية: 使用者介面, 手繪草圖, 深度神經網路, 網頁佈局, 使用者介面骨架, 超文本標記語言, Graphical User Interface, Hand-Drawn Sketch, Deep Neural Networks, Webpage Layout, GUI Skeleton, HTML

وصف الملف: 169 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/77489; http://ir.lib.ntust.edu.tw/bitstream/987654321/77489/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/77489
http://ir.lib.ntust.edu.tw/bitstream/987654321/77489/1/index.html

المؤلفون: 郭柏賢, Guo, Bo-Xain

المساهمون: 吳順德, Wu, Shuen-De

مصطلحات موضوعية: 故障診斷, 卷積神經網路, 深度神經網路, 機器學習, Fault Diagnosis, Convolution Neural Network, Deep Learning Neural Network, Machine Learning

Relation: G060573019H; http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060573019H%22.&%22.id.&; http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/97061

الاتاحة: http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/97061
https://hdl.handle.net/20.500.12235/97061
http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060573019H%22.&%22.id.&

المؤلفون: 水谷英二

المساهمون: 國立臺灣科技大學工業管理系

مصطلحات موضوعية: 深度神經網路學習 (deep neural network learning), 反向傳播(backpropagation), 不定海森矩陣(indefinite Hessian matrix), 動態規劃(dynamic programming), 負曲率 (negative curvature), 鞍點 (saddle points)

وصف الملف: 116 bytes; text/html

Relation: http://ir.lib.ntust.edu.tw/handle/987654321/74984; http://ir.lib.ntust.edu.tw/bitstream/987654321/74984/1/index.html

الاتاحة: http://ir.lib.ntust.edu.tw/handle/987654321/74984
http://ir.lib.ntust.edu.tw/bitstream/987654321/74984/1/index.html

المؤلفون: 洪奕瑾, Yi-Chin Hung

المساهمون: 土木工程學系所, 楊明德, Ming-Der Yang

مصطلحات موضوعية: 高光譜影像, 最小噪聲轉換, 簡單的線性迭代聚類, 深度神經網路, hyperspectral image, Minimum Noise Fraction, Simple Linear Iterative Clustering, Deep Neural Network

Relation: http://hdl.handle.net/11455/97484

الاتاحة: http://hdl.handle.net/11455/97484

المؤلفون: 藍家馨, Lan, Jia-Shin

المساهمون: 電機資訊學院: 資訊工程學研究所, 指導教授: 徐宏民, 藍家馨, Lan, Jia-Shin

مصطلحات موضوعية: 人臉屬性偵測, 深度神經網路, 多標籤分類問題, facial attribute detection, DNN, multi-label classification

Time: 79

وصف الملف: 1509648 bytes; application/pdf

Relation: http://ntur.lib.ntu.edu.tw/handle/246246/275480; http://ntur.lib.ntu.edu.tw/bitstream/246246/275480/1/ntu-105-R03922004-1.pdf

الاتاحة: http://ntur.lib.ntu.edu.tw/handle/246246/275480
https://doi.org/10.6342/NTU201602385
http://ntur.lib.ntu.edu.tw/bitstream/246246/275480/1/ntu-105-R03922004-1.pdf