The DOI matches its Crossref record.
Learning Deep Architectures for AI
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 101–200 of 213
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2662008 · Cited by 333
Deep neural networks for anatomical brain segmentation
de Brebisson, A., & Montana, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301312 · Cited by 330
An Introduction to Neural Information Retrieval
Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification
Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2662199 · Cited by 297
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Learning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287
Self-citationVisualizing the Hidden Activity of Artificial Neural Networks
Rauber, P. E., Fadel, S. G., Falcao, A. X., & Telea, A. C. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598838 · Cited by 280
Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network
Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., & de Geus, P. (2017) · 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2017.00-19 · Cited by 285
Mobile sensor data anonymization
Malekzadeh, M., Clegg, R. G., Cavallaro, A., & Haddadi, H. (2019) · Proceedings of the International Conference on Internet of Things Design and Implementation · doi:10.1145/3302505.3310068 · Cited by 298
A discriminative deep model for pedestrian detection with occlusion handling
Ouyang, W., & Wang, X. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248062 · Cited by 281
Blockchain-enabled Federated Learning: A Survey
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys · doi:10.1145/3524104 · Cited by 286
Bian, C., He, H., & Yang, S. (2020) · Energy · doi:10.1016/j.energy.2019.116538 · Cited by 284
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274
A patch-based convolutional neural network for remote sensing image classification
Sharma, A., Liu, X., Yang, X., & Shi, D. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.07.017 · Cited by 260
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020) · Applied Sciences · doi:10.3390/app10134523 · Cited by 258
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
Glavic, M., Fonteneau, R., & Ernst, D. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1217 · Cited by 241
Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238
Multimodal emotion recognition using deep learning architectures
Ranganathan, H., Chakraborty, S., & Panchanathan, S. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477679 · Cited by 249
Studying Very Low Resolution Recognition Using Deep Networks
Wang, Z., Chang, S., Yang, Y., Liu, D., & Huang, T. S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.518 · Cited by 235
“3, is related to the domain adaption techniques from [3, 10, 31].”
BackgroundA renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235
Real-time scheduling for a smart factory using a reinforcement learning approach
Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225
The state of the art of deep learning models in medical science and their challenges
Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A. (2016) · Physical Review A · doi:10.1103/physreva.94.022308 · Cited by 215
Disentangling Factors of Variation for Facial Expression Recognition
Rifai, S., Bengio, Y., Courville, A., Vincent, P., & Mirza, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_58 · Cited by 215
Self-citationeToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212
“It is noteworthy that DBNs are first effective deep learning algorithms capable of extracting a deep hierarchical representation of the training data [41].”
BackgroundMapping the Landscape of Human-Level Artificial General Intelligence
Adams, S. S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., Hall, J. S., Samsonovich, A., Scheutz, M., Schlesinger, M., et al. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2322 · Cited by 213
A review of privacy-preserving techniques for deep learning
Boulemtafes, A., Derhab, A., & Challal, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.041 · Cited by 211
Cao, X.-C., Chen, B.-Q., Yao, B., & He, W.-P. (2019) · Computers in Industry · doi:10.1016/j.compind.2018.12.018 · Cited by 208
Exploring Representation Learning With CNNs for Frame-to-Frame Ego-Motion Estimation
Costante, G., Mancini, M., Valigi, P., & Ciarfuglia, T. A. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2505717 · Cited by 203
Simulated Annealing Algorithm for Deep Learning
Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.114 · Cited by 192
Disease inference from health-related questions via sparse deep learning
Nie, L., Wang, M., Zhang, L., Yan, S., Zhang, B., & Chua, T.-S. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2399298 · Cited by 195
The successor representation: Its computational logic and neural substrates
Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191
Deep learning for small and big data in psychiatry
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189
Review on self-supervised image recognition using deep neural networks
Ohri, K., & Kumar, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107090 · Cited by 179
On the Expressive Power of Deep Architectures
Bengio, Y., & Delalleau, O. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24412-4_3 · Cited by 182
Self-citationGeospatial data to images: a deep-learning framework for traffic forecasting
Jiang, W., & Zhang, L. (2019) · Tsinghua Science and Technology · doi:10.26599/tst.2018.9010033 · Cited by 176
Yin, J., & Zhao, W. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.10.002 · Cited by 173
Mocanu, E., Nguyen, P. H., Kling, W. L., & Gibescu, M. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.01.030 · Cited by 171
A deep learning approach for fault diagnosis of induction motors in manufacturing
Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017) · Chinese Journal of Mechanical Engineering · doi:10.1007/s10033-017-0189-y · Cited by 167
Data Evaluation and Enhancement for Quality Improvement of Machine Learning
Chen, H., Chen, J., & Ding, J. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3070863 · Cited by 166
Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161
Becker, A. S., Mueller, M., Stoffel, E., Marcon, M., Ghafoor, S., & Boss, A. (2018) · The British Journal of Radiology · doi:10.1259/bjr.20170576 · Cited by 157
Classifying Radio Galaxies with the Convolutional Neural Network
Aniyan, A. K., & Thorat, K. (2017) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aa7333 · Cited by 162
fpgaConvNet: Mapping regular and irregular convolutional neural networks on FPGAs
Venieris, S. I., & Bouganis, C.-S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2844093 · Cited by 161
Are we predictive engines? Perils, prospects, and the puzzle of the porous perceiver
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12002440 · Cited by 158
Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.021 · Cited by 155
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
FashionQ: An AI-Driven Creativity Support Tool for Facilitating Ideation in Fashion Design
Jeon, Y., Jin, S., Shih, P. C., & Han, K. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445093 · Cited by 145
A Comprehensive Study of Activity Recognition Using Accelerometers
Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics · doi:10.3390/informatics5020027 · Cited by 143
Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., & Bando, T. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2649541 · Cited by 141
Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound
Wu, K., Chen, X., & Ding, M. (2014) · Optik · doi:10.1016/j.ijleo.2014.01.114 · Cited by 139
A bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities
Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2015.7299406 · Cited by 136
Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach
Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26631 · Cited by 135
Promise and pitfalls of g-ratio estimation with MRI
Campbell, J. S. W., Leppert, I. R., Narayanan, S., Boudreau, M., Duval, T., Cohen-Adad, J., Pike, G. B., & Stikov, N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.038 · Cited by 129
Rolinger, L., Rüdt, M., & Hubbuch, J. (2020) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-020-02407-z · Cited by 126
Deep Learning for Detecting Robotic Grasps
Lenz, I., Lee, H., & Saxena, A. (2013) · Robotics: Science and Systems IX · doi:10.15607/rss.2013.ix.012 · Cited by 126
Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3168533 · Cited by 121
A Survey on Differentially Private Machine Learning [Review Article]
Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.2976185 · Cited by 115
Zhang, L., Yan, Q., & Zhang, L. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.10.002 · Cited by 114
Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., et al. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3331297 · Cited by 113
Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110
Soft errors in DNN accelerators: A comprehensive review
Ibrahim, Y., Wang, H., Liu, J., Wei, J., Chen, L., Rech, P., Adam, K., & Guo, G. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113969 · Cited by 102
Towards real-time Speech Emotion Recognition using deep neural networks
Fayek, H. M., Lech, M., & Cavedon, L. (2015) · 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS) · doi:10.1109/icspcs.2015.7391796 · Cited by 103
Improving Atmospheric River Forecasts With Machine Learning
Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M. (2019) · Geophysical Research Letters · doi:10.1029/2019gl083662 · Cited by 96
Decision trees do not generalize to new variations
Bengio, Y., Delalleau, O., & Simard, C. (2010) · Computational Intelligence · doi:10.1111/j.1467-8640.2010.00366.x · Cited by 89
Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications
Sharma, P., Sarma, K. K., & Mastorakis, N. E. (2020) · IEEE Access · doi:10.1109/access.2020.3044453 · Cited by 83
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey
Ganapathy, N., Swaminathan, R., & Deserno, T. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1667083 · Cited by 83
Growing random forest on deep convolutional neural networks for scene categorization
Bai, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.038 · Cited by 81
Detecting predatory conversations in social media by deep convolutional neural networks
Ebrahimi, M., Suen, C. Y., & Ormandjieva, O. (2016) · Digital Investigation · doi:10.1016/j.diin.2016.07.001 · Cited by 78
Wu, D., Lawhern, V. J., Hairston, W. D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2544108 · Cited by 74
Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification
Ben Said, A., Mohamed, A., Elfouly, T., Harras, K., & Wang, Z. J. (2017) · 2017 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc.2017.7925709 · Cited by 86
Integrating Learning and Reasoning with Deep Logic Models
Marra, G., Giannini, F., Diligenti, M., & Gori, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-46147-8_31 · Cited by 71
Enhancing the prediction of disease-gene associations with multimodal deep learning
Luo, P., Li, Y., Tian, L.-P., & Wu, F.-X. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz155 · Cited by 67
Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116113 · Cited by 61
A review on the recent applications of deep learning in predictive drug toxicological studies
Sinha, K., Ghosh, N., & Sil, P. C. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00375 · Cited by 64
iHear Food: Eating Detection Using Commodity Bluetooth Headsets
Gao, Y., Zhang, N., Wang, H., Ding, X., Ye, X., Chen, G., & Cao, Y. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.14 · Cited by 63
The cascading neural network: building the Internet of Smart Things
Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59
Ensemble Deep Learning for Biomedical Time Series Classification
Jin, L.-P., & Dong, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6212684 · Cited by 60
Arabic Cyberbullying Detection: Using Deep Learning
Haidar, B., Chamoun, M., & Serhrouchni, A. (2018) · 2018 7th International Conference on Computer and Communication Engineering (ICCCE) · doi:10.1109/iccce.2018.8539303 · Cited by 58
A deep learning framework for hybrid heterogeneous transfer learning
Zhou, J. T., Pan, S. J., & Tsang, I. W. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2019.06.001 · Cited by 53
Kim, S., Jung, H., Lee, M., Choi, Y. Y., & Choi, J.-I. (2023) · eTransportation · doi:10.1016/j.etran.2023.100243 · Cited by 55
Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides
Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201600011 · Cited by 52
Predicting Seizures from Electroencephalography Recordings: A Knowledge Transfer Strategy
Liang, J., Lu, R., Zhang, C., & Wang, F. (2016) · 2016 IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2016.27 · Cited by 49
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49
Fusing multi-scale information in convolution network for MR image super-resolution reconstruction
Liu, C., Wu, X., Yu, X., Tang, Y., Zhang, J., & Zhou, J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0546-9 · Cited by 47
Regularizing deep networks with prior knowledge: A constraint-based approach
Roychowdhury, S., Diligenti, M., & Gori, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106989 · Cited by 46
Gao, J., Yang, J., Wang, G., & Li, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.055 · Cited by 44
Nie, S., Yu, J., Chen, P., Wang, Y., & Zhang, J. Q. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2016.08.034 · Cited by 42
Detection of DDoS attacks in SDN-based VANET using optimized TabNet
Setitra, M. A., & Fan, M. (2024) · Computer Standards & Interfaces · doi:10.1016/j.csi.2024.103845 · Cited by 40
Kohiyama, M., Oka, K., & Yamashita, T. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2552 · Cited by 38
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
Montúfar, G. F. (2014) · Neural Computation · doi:10.1162/neco_a_00601 · Cited by 38
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis
Malekzadeh, M., Clegg, R. G., & Haddadi, H. (2018) · 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi.2018.00025 · Cited by 76
Extending and Analyzing Self-supervised Learning Across Domains
Wallace, B., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_43 · Cited by 35
Abdi, G., Samadzadegan, F., & Reinartz, P. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042604 · Cited by 36
Yu, J., Wu, X., Lv, M., Zhang, Y., Zhang, X., Li, J., Zhu, M., Huang, J., & Zhang, Q. (2020) · Oncology Letters · doi:10.3892/ol.2020.12250 · Cited by 29
Shirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24
Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait
Fernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., Ferreira, C., Gama, J., Erlhagen, W., & Bicho, E. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621466 · Cited by 17
Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16
Metadata
- Authors
- Bengio, Y.
- Venue
- Foundations and Trends® in Machine Learning
- Publisher
- Emerald
- Published
- 2009
- Volume
- 2
- Issue
- 1
- Pages
- 1-127
- ISSN
- 1935-8237 (ISSN-L)
- 1935-8245
- OpenAlex
- W4231109964
- Cited by
- 6,938
- References
- 0
- FWCI
- 42.65
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
