SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

Untitled work

(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895

Generalization and network design strategies

LeCun, Y. (1989) · Cited by 885

Neural Networks for Fingerprint Recognition

Baldi, P., & Chauvin, Y. (1993) · Neural Computation · doi:10.1162/neco.1993.5.3.402 · Cited by 171

Automatic Signature Verification Based on Accelerometry

Herbst, N. M., & Liu, C. N. (1977) · IBM Journal of Research and Development · doi:10.1147/rd.213.0245 · Cited by 108

A time delay neural network architecture for speech recognition

Lang, K. (1989) · Cited by 76

Signature verification from position, velocity and acceleration signals: a comparative study

Réjean Plamondon, B., & Parizeau, M. (2003) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1988.28218 · Cited by 54

DYNAMIC APPROACHES TO HANDWRITTEN SIGNATURE VERIFICATION

Lorette, G., & Réjean Plamondon, B. (1990) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814439329_0002 · Cited by 26

A time delay neural network character recognizer for a touch terminal

Guyon, I., Albrecht, P., LeCun, Y., et al. (1990) · Cited by 8

Face Recognition: From Traditional to Deep Learning MethodsPreprint

Sáez-Trigueros, D., Meng, L., & Hartnett, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00116 · Cited by 152

Deep learning-based software engineering: progress, challenges, and opportunities

Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., Jiang, Y., Liu, B., Liu, H., Li, X., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4127-5 · Cited by 133

Local feature matching using deep learning: A survey

Xu, S., Chen, S., Xu, R., Wang, C., Lu, P., & Guo, L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102344 · Cited by 107

Drug target prediction through deep learning functional representation of gene signatures

Chen, H., King, F. J., Zhou, B., Wang, Y., Canedy, C. J., Hayashi, J., Zhong, Y., Chang, M. W., Pache, L., Wong, J. L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46089-y · Cited by 107

A Differential Approach for Gaze Estimation

Liu, G., Yu, Y., Mora, K. A. F., & Odobez, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957373 · Cited by 100

Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

Roth, S., & Richter, S. R. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00207 · Cited by 129

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Han, Y., Liu, H., Wang, Y., & Liu, C. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3216564 · Cited by 105

Artificial intelligence co-piloted auditing

Gu, H., Schreyer, M., Moffitt, K. C., & Vasarhelyi, M. A. (2024) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2024.100698 · Cited by 151

Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations

Bui, N. D. Q., Yu, Y., & Jiang, L. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462840 · Cited by 103

Deep Metric Learning Beyond Binary Supervision

Kim, S., Seo, M., Laptev, I., Cho, M., & Kwak, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00239 · Cited by 102

Attentive Recurrent Comparators

Shyam, P., Gupta, S., & Dukkipati, A. (2017) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · doi:10.48550/arxiv.1703.00767 · Cited by 98

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

Rohmetra, H., Raghunath, N., Narang, P., Chamola, V., Guizani, M., & Lakkaniga, N. R. (2021) · Computing · doi:10.1007/s00607-021-00937-7 · Cited by 104

Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models

Louis, A., DIJCK, G. V., & Spanakis, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30232 · Cited by 132

Semantic Text Matching for Long-Form Documents

Jiang, J., Zhang, M., Li, C., Bendersky, M., Golbandi, N., & Najork, M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313707 · Cited by 97

Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection

Büch, L., & Andrzejak, A. (2019) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2019.8668039 · Cited by 101

Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening

Alaverdyan, Z., Jung, J., Bouet, R., & Lartizien, C. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101618 · Cited by 96

Blood Pressure Estimation From PPG Signals Using Convolutional Neural Networks And Siamese Network

Schlesinger, O., Vigderhouse, N., Eytan, D., & Moshe, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053446 · Cited by 99

Noise Based Deepfake Detection via Multi-Head Relative-Interaction

Wang, T., & Chow, K. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26701 · Cited by 93

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

Hsu, C., Lin, C., Su, W.-T., et al. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2924554 · Cited by 90

Discriminative deep multi-task learning for facial expression recognition

Zheng, H., Wang, R., Ji, W., Zong, M., Wong, W. K., Lai, Z., & Lv, H. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.041 · Cited by 91

Complement Lexical Retrieval Model with Semantic Residual Embeddings

Gao, L., Dai, Z., Chen, T., Fan, Z., Durme, B. V., & Callan, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_10 · Cited by 100

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data

Packhäuser, K., Gündel, S., Münster, N., Syben, C., Christlein, V., & Maier, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19045-3 · Cited by 120

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval

Dai, G., Xie, J., & Fang, Y. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2817042 · Cited by 88

Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery

Weld, G., Jang, E., Li, A., Zeng, A., Heimerl, K., & Froehlich, J. E. (2019) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3308561.3353798 · Cited by 94

Siamese object tracking for unmanned aerial vehicle: a review and comprehensive analysis

Fu, C., Lu, K., Zheng, G., Ye, J., Cao, Z., Li, B., & Lu, G. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10558-5 · Cited by 99

A Survey on Malware Detection with Graph Representation Learning

Bilot, T., Madhoun, N. E., Agha, K. A., & Zouaoui, A. (2024) · ACM Computing Surveys · doi:10.1145/3664649 · Cited by 112

Learning to Match Aerial Images with Deep Attentive Architectures

Altwaijry, H., Trulls, E., Hays, J. H., Fua, P., & Belongie, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.385 · Cited by 87

Label-Sensitive Deep Metric Learning for Facial Age Estimation

Liu, H., Lu, J., Feng, J., & Zhou, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2746062 · Cited by 84

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

Ling, X., Wu, L., Wang, S., Ma, T., Xu, F., Liu, A. X., Wu, C., & Ji, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3102234 · Cited by 91

Self-Supervised Change Detection in Multiview Remote Sensing Images

Chen, Y., & Bruzzone, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3089453 · Cited by 90

Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data

Zhang, Q., Ge, L., Zhang, R., Metternicht, G. I., Du, Z., Kuang, J., & Xu, M. G. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112575 · Cited by 87

Transfer Learning in Natural Language Processing

Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.020 · Cited by 94

Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

Chowdhury, A., Rosenthal, J., Waring, J., & Umeton, R. (2021) · Informatics · doi:10.3390/informatics8030059 · Cited by 88

GWCNN: A Metric Alignment Layer for Deep Shape Analysis

Ezuz, D., Solomon, J., Kim, V. G., & Ben‐Chen, M. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13244 · Cited by 86

Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

Luo, H., Zhang, J., Liu, X., Zhang, L., & Liu, J. (2024) · Remote Sensing · doi:10.3390/rs16050773 · Cited by 96

Learning Visual Similarity for Inspecting Defective Railway Fasteners

Liu, J., Huang, Y., Zou, Q., Tian, M., Wang, S., Zhao, X., Dai, P., & Ren, S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2911015 · Cited by 83

VulHawk: Cross-architecture Vulnerability Detection with Entropy-based Binary Code Search

Luo, Z., Wang, P., Wang, B., Tang, Y. Z., Xie, W., Xu, Z., Liu, D., & Lü, K. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.24415 · Cited by 93

Offline signature verification using a region based deep metric learning network

Liu, L., Huang, L., Yin, F., & Chen, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108009 · Cited by 86

Zero-Learning Fast Medical Image Fusion

Lahoud, F., & Susstrunk, S. E. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011178 · Cited by 82

Sampling Matters in Deep Embedding LearningPreprint

Wu, C.-Y., Manmatha, R., Smola, A. J., & Krähenbühl, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07567 · Cited by 82

Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey

Tandel, N. H., Prajapati, H. B., & Dabhi, V. K. (2020) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs48705.2020.9074184 · Cited by 80

Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning

Lin, H., Deng, J. D., Albers, D., & Siebert, F. W. (2020) · IEEE Access · doi:10.1109/access.2020.3021357 · Cited by 81

Machine learning for continuous liquid interface production: Printing speed modelling

He, H., Yang, Y., & Pan, Y. (2019) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.01.004 · Cited by 89

Water level prediction from social media images with a multi-task ranking approach

Chaudhary, P., D’Aronco, S., Leitão, J. P., Konrad Schindler, B., & Wegner, J. D. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.07.003 · Cited by 89

Structured Prediction Energy NetworksPreprint

Belanger, D., & McCallum, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06350 · Cited by 79

Generative Adversarial Residual Pairwise Networks for One Shot LearningPreprint

Mehrotra, A., & Dukkipati, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.08033 · Cited by 109

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J., & Stoica, I. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.482 · Cited by 129

A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling

Thiollière, R., Dunbar, E., Synnaeve, G., Versteegh, M., & Emmanuel Dupoux, A. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-640 · Cited by 80

Explaining the Success of Nearest Neighbor Methods in Prediction

Chen, G. H., & Devavrat Shah, B. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834550 · Cited by 79

Collaborative Deep Metric Learning for Video Understanding

Lee, J., Abu-El-Haija, S., Varadarajan, B., & Natsev, A. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219856 · Cited by 83

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., Micheloni, C., Carneiro, G., & Fontanarosa, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101631 · Cited by 80

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Abbasiantaeb, Z., & Momtazi, S. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1412 · Cited by 86

View-Invariant Probabilistic Embedding for Human Pose

Sun, J. J., Zhao, J., Chen, L.-C., Schroff, F., Adam, H., & Liu, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_4 · Cited by 79

Exploring Multiple Instance Learning (MIL): A brief survey

Waqas, M., Ahmed, S. U., Tahir, M. A., Wu, J., & Qureshi, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123893 · Cited by 89

Single-View Place Recognition under Seasonal ChangesPreprint

Olid, D., Fácil, J. M., & Civera, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06516 · Cited by 92

A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors

Maqsood, S., Xu, S., Tran, S. N., Garg, S., Springer, M., Karunanithi, M., & Mohawesh, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116788 · Cited by 83

Together we stand: Siamese Networks for Similar Question Retrieval

Das, A., Yenala, H., Chinnakotla, M. K., & Shrivastava, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1036 · Cited by 78

Multi attention module for visual tracking

Chen, B., Li, P., Sun, C., Wang, D., Yang, G., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.10.005 · Cited by 76

Incremental Few-Shot Instance Segmentation

Ganea, D. A., Boom, B., & Poppe, R. W. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00124 · Cited by 77

How Transferable are Neural Networks in NLP Applications?Preprint

Mou, L., Zhao, M., Yan, R., Li, G., Xu, Y. H., Zhang, L., & Jin, Z. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06111 · Cited by 83

Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating

Alharthi, A. S., Casson, A. J., & Ozanyan, K. B. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3018262 · Cited by 83

Dynamic Conditional Networks for Few-Shot Learning

Zhao, F., Zhao, J. C., Yan, S., & Feng, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_2 · Cited by 75

Learning Graph Representations with Embedding PropagationPreprint

García-Durán, A., & Niepert, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.03059 · Cited by 82

Towards a Unified Framework for Fair and Stable Graph Representation LearningPreprint

Agarwal, C., Lakkaraju, H., & Žitnik, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.13186 · Cited by 91

Survey on 3D face reconstruction from uncalibrated images

Morales, A., Piella, G., & Sukno, F. M. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100400 · Cited by 80

DeepHunter: A Graph Neural Network Based Approach for Robust Cyber Threat Hunting

Wei, R., Cai, L., Zhao, L., et al. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-90019-9_1 · Cited by 72

Semantic Textual Similarity with Siamese Neural Networks

Ranasinghe, T., Orǎsan, C., & Mitkov, R. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_116 · Cited by 77

Duplicate Questions Pair Detection Using Siamese MaLSTM

Imtiaz, Z., Umer, M., Ahmad, M. I., Ullah, S., Choi, G. S., & Mehmood, A. (2020) · IEEE Access · doi:10.1109/access.2020.2969041 · Cited by 76

An Improved Neural Baseline for Temporal Relation Extraction

Qiang, N., Subramanian, S., & Roth, D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1642 · Cited by 77

Dual Contradistinctive Generative Autoencoder

Parmar, G., Li, D., Lee, K., & Tu, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00088 · Cited by 71

Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data

Zhang, Z., George Vosselman, A., Gerke, M., et al. (2019) · Remote Sensing · doi:10.3390/rs11202417 · Cited by 71

Proceedings of the BioNLP 2018 workshop

(2018) · doi:10.18653/v1/w18-23 · Cited by 74

Semantically Decomposing the Latent Spaces of Generative Adversarial NetworksPreprint

Donahue, C., Lipton, Z. C., Balsubramani, A., & McAuley, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07904 · Cited by 89

Building and Interpreting Deep Similarity Models

Eberle, O., Büttner, J., Kräutli, F., Müller, K., Valleriani, M., & Montavon, G. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3020738 · Cited by 74

Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings

Settle, S., Levin, K., Kamper, H., & Livescu, K. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1592 · Cited by 73

Multiple Landmark Detection Using Multi-agent Reinforcement Learning

Vlontzos, A., Alansary, A., Kamnitsas, K., Rueckert, D., & Kainz, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_29 · Cited by 67

Automatic Vulnerability Detection in Embedded Devices and Firmware

Qasem, A., Shirani, P., Debbabi, M., Wang, L., Lebel, B., & Agba, B. L. (2021) · ACM Computing Surveys · doi:10.1145/3432893 · Cited by 67

CoSQA: 20,000+ Web Queries for Code Search and Question Answering

Huang, J., Tang, D., Shou, L., Gong, M., Xu, K., Jiang, D., Zhou, M., & Duan, N. (2021) · doi:10.18653/v1/2021.acl-long.442 · Cited by 110

Self-Supervised Learning with Data Augmentations Provably Isolates Content from StylePreprint

von Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., & Locatello, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04619 · Cited by 104

Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input

Harwath, D. F., Recasens, A., Surís, D., Chuang, G., Torralba, A. B., & Glass, J. R. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01205-0 · Cited by 79

Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

Yan, K., Wang, X., Lü, L., Zhang, L., Harrison, A. P., Bagheri, M. H., & Summers, R. M. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_20 · Cited by 72

A review on Single Image Super Resolution techniques using generative adversarial network

Singla, K., Pandey, R., & Ghanekar, U. (2022) · Optik · doi:10.1016/j.ijleo.2022.169607 · Cited by 66

Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer

Schneider, S., Taylor, G. W., & Kremer, S. C. (2020) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw50321.2020.9096925 · Cited by 67

End-to-End Training of Neural Retrievers for Open-Domain Question Answering

Sachan, D. S., Patwary, M. A., Shoeybi, M., Kant, N., Ping, W., Hamilton, W. L., & Catanzaro, B. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.519 · Cited by 88

Extracting Speaker-Specific Information with a Regularized Siamese Deep Network

Chen, K., & Salman, A. (2011) · Research Explorer (The University of Manchester) · Cited by 66

FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI

Riaz, A., Asad, M., Al-Arif, S. M. M. R., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67159-8_9 · Cited by 65

Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features

Horiguchi, S., Ikami, D., & Aizawa, K. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2911075 · Cited by 70

Neural Snowball for Few-Shot Relation Learning

Gao, T., Han, X., Xie, R., Liu, Z., Lin, F., Lin, L., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6281 · Cited by 66

Few-Shot Sound Event Detection

Wang, Y., Salamon, J., Bryan, N. J., & Bello, J. P. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054708 · Cited by 71

Deep Learning of Representations

Bengio, Y., & Courville, A. C. (2013) · Intelligent systems reference library · doi:10.1007/978-3-642-36657-4_1 · Cited by 66

End-to-End Retrieval in Continuous SpacePreprint

Gillick, D., Presta, A., & Tomar, G. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08008 · Cited by 65

One-Shot Instance SegmentationPreprint

Michaelis, C., Ustyuzhaninov, I., Bethge, M., & Ecker, A. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11507 · Cited by 99

VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture

Bashir, R. M. S., Shahzad, M., & Fraz, M. M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.01.008 · Cited by 67

Chinese medical question answer selection via hybrid models based on CNN and GRU

Zhang, Y., Lü, W., Ou, W., Zhang, G., Zhang, X., Cheng, J., & Zhang, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7240-1 · Cited by 65

Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model

Zheng, Z., Ermon, S., Kim, D., Zhang, L., & Zhong, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3475824 · Cited by 80

DeepNeuron: an open deep learning toolbox for neuron tracing

Zhou, Z., Kuo, H.-C., Peng, H., & Long, F. (2018) · Brain Informatics · doi:10.1186/s40708-018-0081-2 · Cited by 62

Training neural audio classifiers with few data

Pons, J., Serrà Julià, J., & Serra, X. (2019) · Repositori digital de la UPF (Universitat Pompeu Fabra) · doi:10.1109/icassp.2019.8682591 · Cited by 64

An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics

Bammey, Q., von Gioi, R. G., & Morel, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01420 · Cited by 64

Deconvolutional Latent-Variable Model for Text Sequence Matching

Shen, D., Zhang, Y., Henao, R., Su, Q., & Carin, L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11991 · Cited by 65

COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment

Yang, K., Liu, S., Zhao, J., Wang, Y., & Xie, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5696 · Cited by 66

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Bidirectional Sort and Choosing a Row to Update or Delete by Click Any Cell in DataGrid

Tian-yuan, X. (2006) · Jisuanji gongcheng