Journal articles on the topic 'Interpretable deep learning'
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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.
Full textZheng, 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.
Full textRuffolo, 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.
Full textArik, 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.
Full textBhambhoria, 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.
Full textLin, 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.
Full textLiao, 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.
Full textMatsubara, 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.
Full textLiu, 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.
Full textBrinkrolf, 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.
Full textZinemanas, 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.
Full textGagne 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.
Full textAbdel-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.
Full textBang, 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.
Full textXu, 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.
Full textAn, 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.
Full textWei, 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.
Full textWei, 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.
Full textWei, 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.
Full textMonje, 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.
Full textZhang, 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.
Full textFisher, 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.
Full textZokaeinikoo, 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.
Full textWang, 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.
Full textde 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.
Full textZhang, 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.
Full textRampal, 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.
Full textHua, 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.
Full textSchmid, 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.
Full textSieusahai, 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.
Full textR. 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.
Full textLi, 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.
Full textVerma, 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.
Full textLyu, 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.
Full textZhang, 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.
Full textMichau, 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.
Full textMonga, 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.
Full textIsleyen, 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.
Full textVinuesa, 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.
Full textHammelman, 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.
Full textZia, 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.
Full textGao, 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.
Full textCaicedo-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.
Full textAtutxa, 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.
Full textAbid, 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.
Full textJha, 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.
Full textShamsuzzaman, 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.
Full textAhsan, 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.
Full textDelaunay, 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.
Full textAhn, 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.
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