Artículos de revistas sobre el tema "Interpretable deep learning"
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Gangopadhyay, Tryambak, Sin Yong Tan, Anthony LoCurto, James B. Michael y Soumik Sarkar. "Interpretable Deep Learning for Monitoring Combustion Instability". IFAC-PapersOnLine 53, n.º 2 (2020): 832–37. http://dx.doi.org/10.1016/j.ifacol.2020.12.839.
Texto completoZheng, Hong, Yinglong Dai, Fumin Yu y Yuezhen Hu. "Interpretable Saliency Map for Deep Reinforcement Learning". Journal of Physics: Conference Series 1757, n.º 1 (1 de enero de 2021): 012075. http://dx.doi.org/10.1088/1742-6596/1757/1/012075.
Texto completoRuffolo, Jeffrey A., Jeremias Sulam y Jeffrey J. Gray. "Antibody structure prediction using interpretable deep learning". Patterns 3, n.º 2 (febrero de 2022): 100406. http://dx.doi.org/10.1016/j.patter.2021.100406.
Texto completoArik, Sercan Ö. y Tomas Pfister. "TabNet: Attentive Interpretable Tabular Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 6679–87. http://dx.doi.org/10.1609/aaai.v35i8.16826.
Texto completoBhambhoria, Rohan, Hui Liu, Samuel Dahan y Xiaodan Zhu. "Interpretable Low-Resource Legal Decision Making". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 11819–27. http://dx.doi.org/10.1609/aaai.v36i11.21438.
Texto completoLin, Chih-Hsu y Olivier Lichtarge. "Using interpretable deep learning to model cancer dependencies". Bioinformatics 37, n.º 17 (27 de mayo de 2021): 2675–81. http://dx.doi.org/10.1093/bioinformatics/btab137.
Texto completoLiao, WangMin, BeiJi Zou, RongChang Zhao, YuanQiong Chen, ZhiYou He y MengJie Zhou. "Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis". IEEE Journal of Biomedical and Health Informatics 24, n.º 5 (mayo de 2020): 1405–12. http://dx.doi.org/10.1109/jbhi.2019.2949075.
Texto completoMatsubara, Takashi. "Bayesian deep learning: A model-based interpretable approach". Nonlinear Theory and Its Applications, IEICE 11, n.º 1 (2020): 16–35. http://dx.doi.org/10.1587/nolta.11.16.
Texto completoLiu, Yi, Kenneth Barr y John Reinitz. "Fully interpretable deep learning model of transcriptional control". Bioinformatics 36, Supplement_1 (1 de julio de 2020): i499—i507. http://dx.doi.org/10.1093/bioinformatics/btaa506.
Texto completoBrinkrolf, Johannes y Barbara Hammer. "Interpretable machine learning with reject option". at - Automatisierungstechnik 66, n.º 4 (25 de abril de 2018): 283–90. http://dx.doi.org/10.1515/auto-2017-0123.
Texto completoZinemanas, Pablo, Martín Rocamora, Marius Miron, Frederic Font y Xavier Serra. "An Interpretable Deep Learning Model for Automatic Sound Classification". Electronics 10, n.º 7 (2 de abril de 2021): 850. http://dx.doi.org/10.3390/electronics10070850.
Texto completoGagne II, David John, Sue Ellen Haupt, Douglas W. Nychka y Gregory Thompson. "Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms". Monthly Weather Review 147, n.º 8 (17 de julio de 2019): 2827–45. http://dx.doi.org/10.1175/mwr-d-18-0316.1.
Texto completoAbdel-Basset, Mohamed, Hossam Hawash, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed y Karam M. Sallam. "Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds". Mathematics 10, n.º 21 (6 de noviembre de 2022): 4153. http://dx.doi.org/10.3390/math10214153.
Texto completoBang, Seojin, Pengtao Xie, Heewook Lee, Wei Wu y Eric Xing. "Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11396–404. http://dx.doi.org/10.1609/aaai.v35i13.17358.
Texto completoXu, Lingfeng, Julie Liss y Visar Berisha. "Dysarthria detection based on a deep learning model with a clinically-interpretable layer". JASA Express Letters 3, n.º 1 (enero de 2023): 015201. http://dx.doi.org/10.1121/10.0016833.
Texto completoAn, Junkang, Yiwan Zhang y Inwhee Joe. "Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models". Applied Sciences 13, n.º 15 (29 de julio de 2023): 8782. http://dx.doi.org/10.3390/app13158782.
Texto completoWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu y Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification". Agronomy 12, n.º 5 (26 de abril de 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Texto completoWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu y Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification". Agronomy 12, n.º 5 (26 de abril de 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Texto completoWei, Kaihua, Bojian Chen, Jingcheng Zhang, Shanhui Fan, Kaihua Wu, Guangyu Liu y Dongmei Chen. "Explainable Deep Learning Study for Leaf Disease Classification". Agronomy 12, n.º 5 (26 de abril de 2022): 1035. http://dx.doi.org/10.3390/agronomy12051035.
Texto completoMonje, Leticia, Ramón A. Carrasco, Carlos Rosado y Manuel Sánchez-Montañés. "Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain". Mathematics 10, n.º 9 (23 de abril de 2022): 1428. http://dx.doi.org/10.3390/math10091428.
Texto completoZhang, Dongdong, Samuel Yang, Xiaohui Yuan y Ping Zhang. "Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram". iScience 24, n.º 4 (abril de 2021): 102373. http://dx.doi.org/10.1016/j.isci.2021.102373.
Texto completoFisher, Thomas, Harry Gibson, Yunzhe Liu, Moloud Abdar, Marius Posa, Gholamreza Salimi-Khorshidi, Abdelaali Hassaine, Yutong Cai, Kazem Rahimi y Mohammad Mamouei. "Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring". Remote Sensing 14, n.º 13 (26 de junio de 2022): 3072. http://dx.doi.org/10.3390/rs14133072.
Texto completoZokaeinikoo, M., X. Li y M. Yang. "An interpretable deep learning model to predict symptomatic knee osteoarthritis". Osteoarthritis and Cartilage 29 (abril de 2021): S354. http://dx.doi.org/10.1016/j.joca.2021.02.459.
Texto completoWang, Jilong, Rui Li, Renfa Li, Bin Fu y Danny Z. Chen. "HMCKRAutoEncoder: An Interpretable Deep Learning Framework for Time Series Analysis". IEEE Transactions on Emerging Topics in Computing 10, n.º 1 (1 de enero de 2022): 99–111. http://dx.doi.org/10.1109/tetc.2022.3143154.
Texto completode la Torre, Jordi, Aida Valls y Domenec Puig. "A deep learning interpretable classifier for diabetic retinopathy disease grading". Neurocomputing 396 (julio de 2020): 465–76. http://dx.doi.org/10.1016/j.neucom.2018.07.102.
Texto completoZhang, 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, n.º 5 (mayo de 2019): 236–45. http://dx.doi.org/10.1038/s42256-019-0052-1.
Texto completoRampal, Neelesh, Tom Shand, Adam Wooler y Christo Rautenbach. "Interpretable Deep Learning Applied to Rip Current Detection and Localization". Remote Sensing 14, n.º 23 (29 de noviembre de 2022): 6048. http://dx.doi.org/10.3390/rs14236048.
Texto completoHua, Xinyun, Lei Cheng, Ting Zhang y Jianlong Li. "Interpretable deep dictionary learning for sound speed profiles with uncertainties". Journal of the Acoustical Society of America 153, n.º 2 (febrero de 2023): 877–94. http://dx.doi.org/10.1121/10.0017099.
Texto completoSchmid, Ute y Bettina Finzel. "Mutual Explanations for Cooperative Decision Making in Medicine". KI - Künstliche Intelligenz 34, n.º 2 (10 de enero de 2020): 227–33. http://dx.doi.org/10.1007/s13218-020-00633-2.
Texto completoSieusahai, Alexander y 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, n.º 1 (4 de octubre de 2021): 82–90. http://dx.doi.org/10.1609/aiide.v17i1.18894.
Texto completoR. S. Deshpande, P. V. Ambatkar. "Interpretable Deep Learning Models: Enhancing Transparency and Trustworthiness in Explainable AI". Proceeding International Conference on Science and Engineering 11, n.º 1 (18 de febrero de 2023): 1352–63. http://dx.doi.org/10.52783/cienceng.v11i1.286.
Texto completoLi, Wentian, Xidong Feng, Haotian An, Xiang Yao Ng y Yu-Jin Zhang. "MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 792–99. http://dx.doi.org/10.1609/aaai.v34i01.5423.
Texto completoVerma, Abhinav. "Verifiable and Interpretable Reinforcement Learning through Program Synthesis". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9902–3. http://dx.doi.org/10.1609/aaai.v33i01.33019902.
Texto completoLyu, Daoming, Fangkai Yang, Bo Liu y Steven Gustafson. "SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 2970–77. http://dx.doi.org/10.1609/aaai.v33i01.33012970.
Texto completoZhang, Ting-He, Md Musaddaqul Hasib, Yu-Chiao Chiu, Zhi-Feng Han, Yu-Fang Jin, Mario Flores, Yidong Chen y Yufei Huang. "Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions". Cancers 14, n.º 19 (29 de septiembre de 2022): 4763. http://dx.doi.org/10.3390/cancers14194763.
Texto completoMichau, Gabriel, Chi-Ching Hsu y Olga Fink. "Interpretable Detection of Partial Discharge in Power Lines with Deep Learning". Sensors 21, n.º 6 (19 de marzo de 2021): 2154. http://dx.doi.org/10.3390/s21062154.
Texto completoMonga, Vishal, Yuelong Li y Yonina C. Eldar. "Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing". IEEE Signal Processing Magazine 38, n.º 2 (marzo de 2021): 18–44. http://dx.doi.org/10.1109/msp.2020.3016905.
Texto completoIsleyen, Ergin, Sebnem Duzgun y R. McKell Carter. "Interpretable deep learning for roof fall hazard detection in underground mines". Journal of Rock Mechanics and Geotechnical Engineering 13, n.º 6 (diciembre de 2021): 1246–55. http://dx.doi.org/10.1016/j.jrmge.2021.09.005.
Texto completoVinuesa, Ricardo y Beril Sirmacek. "Interpretable deep-learning models to help achieve the Sustainable Development Goals". Nature Machine Intelligence 3, n.º 11 (noviembre de 2021): 926. http://dx.doi.org/10.1038/s42256-021-00414-y.
Texto completoHammelman, Jennifer y David K. Gifford. "Discovering differential genome sequence activity with interpretable and efficient deep learning". PLOS Computational Biology 17, n.º 8 (9 de agosto de 2021): e1009282. http://dx.doi.org/10.1371/journal.pcbi.1009282.
Texto completoZia, Tehseen, Nauman Bashir, Mirza Ahsan Ullah y Shakeeb Murtaza. "SoFTNet: A concept-controlled deep learning architecture for interpretable image classification". Knowledge-Based Systems 240 (marzo de 2022): 108066. http://dx.doi.org/10.1016/j.knosys.2021.108066.
Texto completoGao, Xinjian, Tingting Mu, John Yannis Goulermas, Jeyarajan Thiyagalingam y 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.
Texto completoCaicedo-Torres, William y Jairo Gutierrez. "ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU". Journal of Biomedical Informatics 98 (octubre de 2019): 103269. http://dx.doi.org/10.1016/j.jbi.2019.103269.
Texto completoAtutxa, Aitziber, Arantza Díaz de Ilarraza, Koldo Gojenola, Maite Oronoz y Olatz Perez-de-Viñaspre. "Interpretable deep learning to map diagnostic texts to ICD-10 codes". International Journal of Medical Informatics 129 (septiembre de 2019): 49–59. http://dx.doi.org/10.1016/j.ijmedinf.2019.05.015.
Texto completoAbid, Firas Ben, Marwen Sallem y Ahmed Braham. "Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors". IEEE Transactions on Instrumentation and Measurement 69, n.º 6 (junio de 2020): 3506–15. http://dx.doi.org/10.1109/tim.2019.2932162.
Texto completoJha, Manoj, Akshay Kumar Kawale y Chandan Kumar Verma. "Interpretable Model for Antibiotic Resistance Prediction in Bacteria using Deep Learning". Biomedical and Pharmacology Journal 10, n.º 4 (25 de diciembre de 2017): 1963–68. http://dx.doi.org/10.13005/bpj/1316.
Texto completoShamsuzzaman, Md. "Explainable and Interpretable Deep Learning Models". Global Journal of Engineering Sciences 5, n.º 5 (9 de junio de 2020). http://dx.doi.org/10.33552/gjes.2020.05.000621.
Texto completoAhsan, Md Manjurul, Md Shahin Ali, Md Mehedi Hassan, Tareque Abu Abdullah, Kishor Datta Gupta, Ulas Bagci, Chetna Kaushal y Naglaa F. Soliman. "Monkeypox Diagnosis with Interpretable Deep Learning". IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3300793.
Texto completoDelaunay, Antoine y Hannah M. Christensen. "Interpretable Deep Learning for Probabilistic MJO Prediction". Geophysical Research Letters, 24 de agosto de 2022. http://dx.doi.org/10.1029/2022gl098566.
Texto completoAhn, Daehwan, Dokyun Lee y Kartik Hosanagar. "Interpretable Deep Learning Approach to Churn Management". SSRN Electronic Journal, 2020. http://dx.doi.org/10.2139/ssrn.3981160.
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