Статті в журналах з теми "Interpretable deep learning"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Interpretable deep learning".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
Gangopadhyay, Tryambak, Sin Yong Tan, Anthony LoCurto, James B. Michael, and Soumik Sarkar. "Interpretable Deep Learning for Monitoring Combustion Instability." IFAC-PapersOnLine 53, no. 2 (2020): 832–37. http://dx.doi.org/10.1016/j.ifacol.2020.12.839.
Повний текст джерелаZheng, Hong, Yinglong Dai, Fumin Yu, and Yuezhen Hu. "Interpretable Saliency Map for Deep Reinforcement Learning." Journal of Physics: Conference Series 1757, no. 1 (January 1, 2021): 012075. http://dx.doi.org/10.1088/1742-6596/1757/1/012075.
Повний текст джерелаRuffolo, Jeffrey A., Jeremias Sulam, and Jeffrey J. Gray. "Antibody structure prediction using interpretable deep learning." Patterns 3, no. 2 (February 2022): 100406. http://dx.doi.org/10.1016/j.patter.2021.100406.
Повний текст джерелаArik, Sercan Ö., and Tomas Pfister. "TabNet: Attentive Interpretable Tabular Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6679–87. http://dx.doi.org/10.1609/aaai.v35i8.16826.
Повний текст джерелаBhambhoria, Rohan, Hui Liu, Samuel Dahan, and Xiaodan Zhu. "Interpretable Low-Resource Legal Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11819–27. http://dx.doi.org/10.1609/aaai.v36i11.21438.
Повний текст джерелаLin, Chih-Hsu, and Olivier Lichtarge. "Using interpretable deep learning to model cancer dependencies." Bioinformatics 37, no. 17 (May 27, 2021): 2675–81. http://dx.doi.org/10.1093/bioinformatics/btab137.
Повний текст джерелаLiao, WangMin, BeiJi Zou, RongChang Zhao, YuanQiong Chen, ZhiYou He, and MengJie Zhou. "Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis." IEEE Journal of Biomedical and Health Informatics 24, no. 5 (May 2020): 1405–12. http://dx.doi.org/10.1109/jbhi.2019.2949075.
Повний текст джерелаMatsubara, Takashi. "Bayesian deep learning: A model-based interpretable approach." Nonlinear Theory and Its Applications, IEICE 11, no. 1 (2020): 16–35. http://dx.doi.org/10.1587/nolta.11.16.
Повний текст джерелаLiu, Yi, Kenneth Barr, and John Reinitz. "Fully interpretable deep learning model of transcriptional control." Bioinformatics 36, Supplement_1 (July 1, 2020): i499—i507. http://dx.doi.org/10.1093/bioinformatics/btaa506.
Повний текст джерелаBrinkrolf, Johannes, and Barbara Hammer. "Interpretable machine learning with reject option." at - Automatisierungstechnik 66, no. 4 (April 25, 2018): 283–90. http://dx.doi.org/10.1515/auto-2017-0123.
Повний текст джерелаZinemanas, Pablo, Martín Rocamora, Marius Miron, Frederic Font, and Xavier Serra. "An Interpretable Deep Learning Model for Automatic Sound Classification." Electronics 10, no. 7 (April 2, 2021): 850. http://dx.doi.org/10.3390/electronics10070850.
Повний текст джерелаGagne II, David John, Sue Ellen Haupt, Douglas W. Nychka, and Gregory Thompson. "Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms." Monthly Weather Review 147, no. 8 (July 17, 2019): 2827–45. http://dx.doi.org/10.1175/mwr-d-18-0316.1.
Повний текст джерелаAbdel-Basset, Mohamed, Hossam Hawash, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed, and Karam M. Sallam. "Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds." Mathematics 10, no. 21 (November 6, 2022): 4153. http://dx.doi.org/10.3390/math10214153.
Повний текст джерелаBang, Seojin, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing. "Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11396–404. http://dx.doi.org/10.1609/aaai.v35i13.17358.
Повний текст джерелаXu, Lingfeng, Julie Liss, and Visar Berisha. "Dysarthria detection based on a deep learning model with a clinically-interpretable layer." JASA Express Letters 3, no. 1 (January 2023): 015201. http://dx.doi.org/10.1121/10.0016833.
Повний текст джерелаAn, Junkang, Yiwan Zhang, and Inwhee Joe. "Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models." Applied Sciences 13, no. 15 (July 29, 2023): 8782. http://dx.doi.org/10.3390/app13158782.
Повний текст джерелаWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu, and Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification." Agronomy 12, no. 5 (April 26, 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Повний текст джерелаWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu, and Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification." Agronomy 12, no. 5 (April 26, 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Повний текст джерелаWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu, and Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification." Agronomy 12, no. 5 (April 26, 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Повний текст джерелаMonje, Leticia, Ramón A. Carrasco, Carlos Rosado, and Manuel Sánchez-Montañés. "Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain." Mathematics 10, no. 9 (April 23, 2022): 1428. http://dx.doi.org/10.3390/math10091428.
Повний текст джерелаZhang, Dongdong, Samuel Yang, Xiaohui Yuan, and Ping Zhang. "Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram." iScience 24, no. 4 (April 2021): 102373. http://dx.doi.org/10.1016/j.isci.2021.102373.
Повний текст джерелаFisher, Thomas, Harry Gibson, Yunzhe Liu, Moloud Abdar, Marius Posa, Gholamreza Salimi-Khorshidi, Abdelaali Hassaine, Yutong Cai, Kazem Rahimi, and Mohammad Mamouei. "Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring." Remote Sensing 14, no. 13 (June 26, 2022): 3072. http://dx.doi.org/10.3390/rs14133072.
Повний текст джерелаZokaeinikoo, M., X. Li, and M. Yang. "An interpretable deep learning model to predict symptomatic knee osteoarthritis." Osteoarthritis and Cartilage 29 (April 2021): S354. http://dx.doi.org/10.1016/j.joca.2021.02.459.
Повний текст джерелаWang, Jilong, Rui Li, Renfa Li, Bin Fu, and Danny Z. Chen. "HMCKRAutoEncoder: An Interpretable Deep Learning Framework for Time Series Analysis." IEEE Transactions on Emerging Topics in Computing 10, no. 1 (January 1, 2022): 99–111. http://dx.doi.org/10.1109/tetc.2022.3143154.
Повний текст джерелаde la Torre, Jordi, Aida Valls, and Domenec Puig. "A deep learning interpretable classifier for diabetic retinopathy disease grading." Neurocomputing 396 (July 2020): 465–76. http://dx.doi.org/10.1016/j.neucom.2018.07.102.
Повний текст джерелаZhang, Zizhao, Pingjun Chen, Mason McGough, Fuyong Xing, Chunbao Wang, Marilyn Bui, Yuanpu Xie, et al. "Pathologist-level interpretable whole-slide cancer diagnosis with deep learning." Nature Machine Intelligence 1, no. 5 (May 2019): 236–45. http://dx.doi.org/10.1038/s42256-019-0052-1.
Повний текст джерелаRampal, Neelesh, Tom Shand, Adam Wooler, and Christo Rautenbach. "Interpretable Deep Learning Applied to Rip Current Detection and Localization." Remote Sensing 14, no. 23 (November 29, 2022): 6048. http://dx.doi.org/10.3390/rs14236048.
Повний текст джерелаHua, Xinyun, Lei Cheng, Ting Zhang, and Jianlong Li. "Interpretable deep dictionary learning for sound speed profiles with uncertainties." Journal of the Acoustical Society of America 153, no. 2 (February 2023): 877–94. http://dx.doi.org/10.1121/10.0017099.
Повний текст джерелаSchmid, Ute, and Bettina Finzel. "Mutual Explanations for Cooperative Decision Making in Medicine." KI - Künstliche Intelligenz 34, no. 2 (January 10, 2020): 227–33. http://dx.doi.org/10.1007/s13218-020-00633-2.
Повний текст джерелаSieusahai, Alexander, and Matthew Guzdial. "Explaining Deep Reinforcement Learning Agents in the Atari Domain through a Surrogate Model." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17, no. 1 (October 4, 2021): 82–90. http://dx.doi.org/10.1609/aiide.v17i1.18894.
Повний текст джерелаR. S. Deshpande, P. V. Ambatkar. "Interpretable Deep Learning Models: Enhancing Transparency and Trustworthiness in Explainable AI." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 1352–63. http://dx.doi.org/10.52783/cienceng.v11i1.286.
Повний текст джерелаLi, Wentian, Xidong Feng, Haotian An, Xiang Yao Ng, and Yu-Jin Zhang. "MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 792–99. http://dx.doi.org/10.1609/aaai.v34i01.5423.
Повний текст джерелаVerma, Abhinav. "Verifiable and Interpretable Reinforcement Learning through Program Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9902–3. http://dx.doi.org/10.1609/aaai.v33i01.33019902.
Повний текст джерелаLyu, Daoming, Fangkai Yang, Bo Liu, and Steven Gustafson. "SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2970–77. http://dx.doi.org/10.1609/aaai.v33i01.33012970.
Повний текст джерелаZhang, Ting-He, Md Musaddaqul Hasib, Yu-Chiao Chiu, Zhi-Feng Han, Yu-Fang Jin, Mario Flores, Yidong Chen, and Yufei Huang. "Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions." Cancers 14, no. 19 (September 29, 2022): 4763. http://dx.doi.org/10.3390/cancers14194763.
Повний текст джерелаMichau, Gabriel, Chi-Ching Hsu, and Olga Fink. "Interpretable Detection of Partial Discharge in Power Lines with Deep Learning." Sensors 21, no. 6 (March 19, 2021): 2154. http://dx.doi.org/10.3390/s21062154.
Повний текст джерелаMonga, Vishal, Yuelong Li, and Yonina C. Eldar. "Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing." IEEE Signal Processing Magazine 38, no. 2 (March 2021): 18–44. http://dx.doi.org/10.1109/msp.2020.3016905.
Повний текст джерелаIsleyen, Ergin, Sebnem Duzgun, and R. McKell Carter. "Interpretable deep learning for roof fall hazard detection in underground mines." Journal of Rock Mechanics and Geotechnical Engineering 13, no. 6 (December 2021): 1246–55. http://dx.doi.org/10.1016/j.jrmge.2021.09.005.
Повний текст джерелаVinuesa, Ricardo, and Beril Sirmacek. "Interpretable deep-learning models to help achieve the Sustainable Development Goals." Nature Machine Intelligence 3, no. 11 (November 2021): 926. http://dx.doi.org/10.1038/s42256-021-00414-y.
Повний текст джерелаHammelman, Jennifer, and David K. Gifford. "Discovering differential genome sequence activity with interpretable and efficient deep learning." PLOS Computational Biology 17, no. 8 (August 9, 2021): e1009282. http://dx.doi.org/10.1371/journal.pcbi.1009282.
Повний текст джерелаZia, Tehseen, Nauman Bashir, Mirza Ahsan Ullah, and Shakeeb Murtaza. "SoFTNet: A concept-controlled deep learning architecture for interpretable image classification." Knowledge-Based Systems 240 (March 2022): 108066. http://dx.doi.org/10.1016/j.knosys.2021.108066.
Повний текст джерелаGao, Xinjian, Tingting Mu, John Yannis Goulermas, Jeyarajan Thiyagalingam, and Meng Wang. "An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts." IEEE Transactions on Image Processing 29 (2020): 3911–26. http://dx.doi.org/10.1109/tip.2020.2965275.
Повний текст джерелаCaicedo-Torres, William, and Jairo Gutierrez. "ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU." Journal of Biomedical Informatics 98 (October 2019): 103269. http://dx.doi.org/10.1016/j.jbi.2019.103269.
Повний текст джерелаAtutxa, Aitziber, Arantza Díaz de Ilarraza, Koldo Gojenola, Maite Oronoz, and Olatz Perez-de-Viñaspre. "Interpretable deep learning to map diagnostic texts to ICD-10 codes." International Journal of Medical Informatics 129 (September 2019): 49–59. http://dx.doi.org/10.1016/j.ijmedinf.2019.05.015.
Повний текст джерелаAbid, Firas Ben, Marwen Sallem, and Ahmed Braham. "Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors." IEEE Transactions on Instrumentation and Measurement 69, no. 6 (June 2020): 3506–15. http://dx.doi.org/10.1109/tim.2019.2932162.
Повний текст джерелаJha, Manoj, Akshay Kumar Kawale, and Chandan Kumar Verma. "Interpretable Model for Antibiotic Resistance Prediction in Bacteria using Deep Learning." Biomedical and Pharmacology Journal 10, no. 4 (December 25, 2017): 1963–68. http://dx.doi.org/10.13005/bpj/1316.
Повний текст джерелаShamsuzzaman, Md. "Explainable and Interpretable Deep Learning Models." Global Journal of Engineering Sciences 5, no. 5 (June 9, 2020). http://dx.doi.org/10.33552/gjes.2020.05.000621.
Повний текст джерелаAhsan, Md Manjurul, Md Shahin Ali, Md Mehedi Hassan, Tareque Abu Abdullah, Kishor Datta Gupta, Ulas Bagci, Chetna Kaushal, and Naglaa F. Soliman. "Monkeypox Diagnosis with Interpretable Deep Learning." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3300793.
Повний текст джерелаDelaunay, Antoine, and Hannah M. Christensen. "Interpretable Deep Learning for Probabilistic MJO Prediction." Geophysical Research Letters, August 24, 2022. http://dx.doi.org/10.1029/2022gl098566.
Повний текст джерелаAhn, Daehwan, Dokyun Lee, and Kartik Hosanagar. "Interpretable Deep Learning Approach to Churn Management." SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3981160.
Повний текст джерела