Exploiting Generative AI to Scale up Intelligent Tutoring Systems

MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection

Coccomini, D. A., Zilos, G. K., Amato, G., Caldelli, R., Falchi, F., Papadopoulos, S., & Gennaro, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409054 · Cited by 33

ViT-LLMR: Vision Transformer-based lower limb motion recognition from fusion signals of MMG and IMU

Zhang, H., Yang, K., Cao, G., & Xia, C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104508 · Cited by 28

Preventing generation of verbatim memorization in language models gives a false sense of privacy

Ippolito, D., Tramer, F., Nasr, M., Zhang, C., Jagielski, M., Lee, K., Choquette Choo, C., & Carlini, N. (2023) · Proceedings of the 16th International Natural Language Generation Conference · doi:10.18653/v1/2023.inlg-main.3 · Cited by 28

Artificial intelligence—friend or foe in fake news campaigns

Węcel, K., Sawiński, M., Stróżyna, M., Lewoniewski, W., Księżniak, E., Stolarski, P., & Abramowicz, W. (2023) · Economics and Business Review · doi:10.18559/ebr.2023.2.736 · Cited by 28

Accuracy Comparison of CNN, LSTM, and Transformer for Activity Recognition Using IMU and Visual Markers

Trujillo-Guerrero, M. F., Román-Niemes, S., Jaén-Vargas, M., Cadiz, A., Fonseca, R., & Serrano-Olmedo, J. J. (2023) · IEEE Access · doi:10.1109/access.2023.3318563 · Cited by 28

Personalized Large Language Models

Woźniak, S., Koptyra, B., Janz, A., Kazienko, P., & Kocoń, J. (2024) · 2024 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw65004.2024.00071 · Cited by 28

Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation

Xie, A., Lee, L., Xiao, T., & Finn, C. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611331 · Cited by 28

BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction

Diaf, A., Korba, A. A., Elislem Karabadji, N., & Ghamri-Doudane, Y. (2024) · GLOBECOM 2024 - 2024 IEEE Global Communications Conference · doi:10.1109/globecom52923.2024.10901770 · Cited by 28

Deep Learning Mental Health Dialogue System

Brocki, L., Dyer, G. C., Gładka, A., & Chung, N. C. (2023) · 2023 IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp57234.2023.00097 · Cited by 28

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference

Taniguchi, T. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1353870 · Cited by 28

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

Yi, S., Goel, R., Khatri, C., Cervone, A., Chung, T., Hedayatnia, B., Venkatesh, A., Gabriel, R., & Hakkani-Tur, D. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8608 · Cited by 26

Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

Kim, H., Ohmura, Y., Nagakubo, A., & Kuniyoshi, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3262423 · Cited by 28

A Unimodal Representation Learning and Recurrent Decomposition Fusion Structure for Utterance-Level Multimodal Embedding Learning

Mai, S., Hu, H., & Xing, S. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3082398 · Cited by 26

BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk

Wu, Y., Liu, Z., Wu, L., Chen, M., & Tong, W. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.729834 · Cited by 26

Multi-Modal Dynamic Graph Transformer for Visual Grounding

Chen, S., & Li, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01509 · Cited by 28

“Mathematical word problem generation from commonsense knowledge graph and equations,”

Liu, T., Fang, Q., Ding, W., Li, H., Wu, Z., & Liu, Z. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.348 · Cited by 29

Finding Generalizable Evidence by Learning to Convince Q&A Models

Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., & Cho, K. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1244 · Cited by 27

Learning Multi-Intersection Traffic Signal Control via Coevolutionary Multi-Agent Reinforcement Learning

Chen, W., Yang, S., Li, W., Hu, Y., Liu, X., & Gao, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3410023 · Cited by 27

Transformer-enabled weakly supervised abnormal event detection in intelligent video surveillance systems

Paulraj, S., & Vairavasundaram, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109496 · Cited by 27

Developing an AI-Assisted Low-Resource Spoken Language Learning App for Children

Getman, Y., Phan, N., Al-Ghezi, R., Voskoboinik, E., Singh, M., Grósz, T., Kurimo, M., Salvi, G., Svendsen, T., Strömbergsson, S., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3304274 · Cited by 27

Recommendations to Create Programming Exercises to Overcome ChatGPT

Berrezueta-Guzman, J., & Krusche, S. (2023) · 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet58097.2023.00031 · Cited by 27

Deep learning approaches for similarity computation: A survey

Yang, P., Wang, H., Yang, J., Qian, Z., Zhang, Y., & Lin, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3422484 · Cited by 28

Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries

Yao, J., & Kowal, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100585 · Cited by 30

A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models

Guo, C., Cheng, F., Du, Z., Kiessling, J., Ku, J., Li, S., Li, Z., Ma, M., Molom-Ochir, T., Morris, B., et al. (2025) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2024.3476008 · Cited by 32

An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures

Singla, T., Anandayuvaraj, D., Kalu, K. G., Schorlemmer, T. R., & Davis, J. C. (2023) · Proceedings of the 2023 Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses · doi:10.1145/3605770.3625214 · Cited by 26

DeepSS2GO: protein function prediction from secondary structure

Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26

Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models

Wingate, D., Shoeybi, M., & Sorensen, T. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.412 · Cited by 26

How to leverage the multimodal EHR data for better medical prediction?

Yang, B., & Wu, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.329 · Cited by 26

Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model

(2024) · doi:10.1021/acs.jctc.4c00255 · Cited by 26

TIS Transformer: remapping the human proteome using deep learning

Clauwaert, J., McVey, Z., Gupta, R., & Menschaert, G. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad021 · Cited by 26

PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

Dong, C., Pang, C., Li, Z., Zeng, X., & Hu, X. (2022) · IEEE Access · doi:10.1109/access.2022.3223997 · Cited by 26

Transformer Memory for Interactive Visual Navigation in Cluttered Environments

Li, W., Hong, R., Shen, J., Yuan, L., & Lu, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3241803 · Cited by 26

Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition

Haresamudram, H., Essa, I., & Plötz, T. (2023) · 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom56429.2023.10099197 · Cited by 27

Logiqa 2.0—An improved dataset for logical reasoning in natural language understanding

Liu, H., Liu, J., Cui, L., Teng, Z., Duan, N., Zhou, M., & Zhang, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3293046 · Cited by 26

Transformer-based sensor fusion for autonomous vehicles: A comprehensive review

Abdulmaksoud, A., & Ahmed, R. (2025) · IEEE Access · doi:10.1109/access.2025.3545032 · Cited by 25

Improved prediction of MHC-peptide binding using protein language models

Hashemi, N., Hao, B., Ignatov, M., Paschalidis, I. C., Vakili, P., Vajda, S., & Kozakov, D. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1207380 · Cited by 25

Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture

Pearce, K., Alghowinem, S., & Breazeal, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26903 · Cited by 25

Self-Supervised Pre-training for Time Series Classification

Shi, P., Ye, W., & Qin, Z. (2021) · 2021 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533426 · Cited by 25

Reason first, then respond: Modular Generation for Knowledge-infused Dialogue

Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.527 · Cited by 25

Attention-based approach to predict drug–target interactions across seven target superfamilies

Schulman, A., Rousu, J., Aittokallio, T., & Tanoli, Z. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae496 · Cited by 25

Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship

Lehmann, F., Markert, N., Dang, H., & Buschek, D. (2022) · Mensch und Computer 2022 · doi:10.1145/3543758.3543947 · Cited by 25

Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition

Suh, S., Rey, V. F., & Lukowicz, P. (2022) · 2022 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom53586.2022.9762387 · Cited by 25

Deep learning for report generation on chest X-ray images

Ouis, M. Y., & A. Akhloufi, M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102320 · Cited by 25

Deeploy: Enabling Energy-Efficient Deployment of Small Language Models on Heterogeneous Microcontrollers

Scherer, M., Macan, L., Jung, V. J. B., Wiese, P., Bompani, L., Burrello, A., Conti, F., & Benini, L. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443718 · Cited by 26

Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake Detection

She, H., Hu, Y., Liu, B., Li, J., & Li, C.-T. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3451356 · Cited by 24

MRFTrans: Multimodal representation fusion transformer for monocular 3D semantic scene completion

Xu, R., Zhang, J., Sun, J., Wang, C., Wu, Y., Xu, S., Meng, W., & Zhang, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102493 · Cited by 24

Enriched music representations with multiple cross-modal contrastive learning

Ferraro, A., Favory, X., Drossos, K., Kim, Y., & Bogdanov, D. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3071082 · Cited by 23

Covid-19 automatic diagnosis with ct images using the novel transformer architecture

Costa, G. S. S., Paiva, A. C., Braz Júnior, G., & Ferreira, M. M. (2021) · Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2021) · doi:10.5753/sbcas.2021.16073 · Cited by 24

DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model

Tuli, S., Casale, G., Cherkasova, L., & Jennings, N. R. (2023) · IEEE INFOCOM 2023 - IEEE Conference on Computer Communications · doi:10.1109/infocom53939.2023.10229049 · Cited by 23

Automatic Product Copywriting for E-commerce

Zhang, X., Zou, Y., Zhang, H., Zhou, J., Diao, S., Chen, J., Ding, Z., He, Z., He, X., Xiao, Y., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21508 · Cited by 24

Large Language Models for Adverse Drug Events: A Clinical Perspective

Zitu, M. M., Owen, D., Manne, A., Wei, P., & Li, L. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14155490 · Cited by 24

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Xu, G., Wang, Q., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab529 · Cited by 24

AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models

Yin, Y., Chen, C., Shang, L., Jiang, X., Chen, X., & Liu, Q. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.400 · Cited by 24

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Kreiss, E., Bennett, C., Hooshmand, S., Zelikman, E., Ringel Morris, M., & Potts, C. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.309 · Cited by 24

Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests

van Duijn, M., van Dijk, B., Kouwenhoven, T., de Valk, W., Spruit, M., & vanderPutten, P. (2023) · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/2023.conll-1.25 · Cited by 24

PriMock57: A Dataset Of Primary Care Mock Consultations

Papadopoulos Korfiatis, A., Moramarco, F., Sarac, R., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.65 · Cited by 23

Question answering systems with deep learning-based symbolic processing

Honda, H., & Hagiwara, M. (2019) · IEEE Access · doi:10.1109/access.2019.2948081 · Cited by 23

Trade-off between Robustness and Accuracy of Vision Transformers

Li, Y., & Xu, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00730 · Cited by 23

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.198 · Cited by 23

Cross-modal multiscale multi-instance learning for long-term ECG classification

Chen, L., Lian, C., Zeng, Z., Xu, B., & Su, Y. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119230 · Cited by 23

Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation

Mohseni Behbahani, Y., Laine, E., & Carbone, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad231 · Cited by 23

SwinIoT: A Hierarchical Transformer-Based Framework for Behavioral Anomaly Detection in IoT-Driven Smart Cities

Mancy, H., & Naith, Q. H. (2025) · IEEE Access · doi:10.1109/access.2025.3551207 · Cited by 23

QuickEdit: Editing Text and Translations by Crossing Words Out

Grangier, D., & Auli, M. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1025 · Cited by 20

Online learning for the hyoid bone tracking during swallowing with neck movement adjustment using semantic segmentation

Lee, D., Lee, W. H., Seo, H. G., Oh, B.-M., Lee, J. C., & Kim, H. C. (2020) · IEEE Access · doi:10.1109/access.2020.3019532 · Cited by 22

Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans

Lampinen, A. (2024) · Computational Linguistics · doi:10.1162/coli_a_00525 · Cited by 32

Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting

Yan, B., Liu, R., Kuo, D., Adithan, S., Reis, E., Kwak, S., Venugopal, V., O’Connell, C., Saenz, A., Rajpurkar, P., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.977 · Cited by 22

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Meng, C., Trinh, L., Xu, N., & Liu, Y. (2021) · doi:10.21203/rs.3.rs-402058/v1 · Cited by 21

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network

Zhao, M., Yuan, M., Yang, Y., & Xu, S. X. (2023) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3225296 · Cited by 22

Quantformer: Learning Extremely Low-Precision Vision Transformers

Wang, Z., Wang, C., Xu, X., Zhou, J., & Lu, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3229313 · Cited by 22

An Integrated CNN-BiLSTM-Transformer Framework for Improved Anomaly Detection Using Surveillance Videos

Natha, S., Siraj, M., Ahmed, F., Altamimi, M., & Syed, M. (2025) · IEEE Access · doi:10.1109/access.2025.3574835 · Cited by 22

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging

Turgut, Ö., Müller, P., Hager, P., Shit, S., Starck, S., Menten, M. J., Martens, E., & Rueckert, D. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103451 · Cited by 22

DSI++: Updating Transformer Memory with New Documents

Mehta, S., Gupta, J., Tay, Y., Dehghani, M., Tran, V., Rao, J., Najork, M., Strubell, E., & Metzler, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.510 · Cited by 21

A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis

Pan, J., Yin, X., Zhang, Z., Liu, S., Zhang, Y., Ma, Z., & Wang, Y. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053390 · Cited by 21

D-Score: Holistic Dialogue Evaluation Without Reference

Zhang, C., Lee, G., D'Haro, L. F., & Li, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3074012 · Cited by 21

Ethical framework for responsible foundational models in medical imaging

Jha, D., Durak, G., Das, A., Sanjotra, J., Susladkar, O., Sarkar, S., Rauniyar, A., Kumar Tomar, N., Peng, L., Li, S., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1544501 · Cited by 21

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

Feng, S.-H., Xia, C.-Q., & Shen, H.-B. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab744 · Cited by 21

Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

Liu, T., Zheng, X., Chang, B., & Sui, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17583 · Cited by 21

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.482 · Cited by 21

Hybrid ML-EMT-Based Digital Twin for Device-Level HIL Real-Time Emulation of Ship-Board Microgrid on FPGA

Zhang, S., Liang, T., & Dinavahi, V. (2023) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2023.3282776 · Cited by 21

Neural Scaling of Deep Chemical Models

Frey, N., Soklaski, R., Axelrod, S., Samsi, S., Gomez-Bombarelli, R., Coley, C., & Gadepally, V. (2022) · doi:10.26434/chemrxiv-2022-3s512 · Cited by 20

Foundation models for electrocardiogram interpretation: clinical implications

Nolin-Lapalme, A., Sowa, A., Delfrate, J., Tastet, O., Corbin, D., Kulbay, M., Ozdemir, D., Noël, M.-J., Marois-Blanchet, F.-C., Harvey, F., et al. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehaf1119 · Cited by 20

Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval

Zhu, D., Song, D., Chen, Y., Lumezanu, C., Cheng, W., Zong, B., Ni, J., Mizoguchi, T., Yang, T., & Chen, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5497 · Cited by 20

Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions

Erdem Güler, S., & Patlar Akbulut, F. (2025) · IEEE Access · doi:10.1109/access.2025.3538642 · Cited by 21

Visual-Semantic Transformer for Face Forgery Detection

Xu, Y., Jia, G., Huang, H., Duan, J., & He, R. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484407 · Cited by 24

A review on machine learning-guided design of energy materials

Kim, S., Xu, J., Shang, W., Xu, Z., Lee, E., & Luo, T. (2024) · Progress in Energy · doi:10.1088/2516-1083/ad7220 · Cited by 19

MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure Recognition

Xie, R., Pang, K., Bader, G. D., & Wang, B. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00321 · Cited by 17

ACT: Adversarial Convolutional Transformer for Time Series Forecasting

Li, Y., Wang, H., Li, J., Liu, C., & Tan, J. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892791 · Cited by 19

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

Deng, Z., Peng, H., Xia, C., Li, J., He, L., & Yu, P. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.555 · Cited by 19

ESM ‐scan—A tool to guide amino acid substitutions

Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G. (2024) · Protein Science · doi:10.1002/pro.5221 · Cited by 19

Few-Shot Class-Incremental Learning with Meta-Learned Class Structures

Zheng, G., & Zhang, A. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00058 · Cited by 19

A cross attention approach to diagnostic explainability using clinical practice guidelines for depression

Dalal, S., Tilwani, D., Gaur, M., Jain, S., Shalin, V. L., & Sheth, A. P. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3483577 · Cited by 19

Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification

Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19

Argument Mining Driven Analysis of Peer-Reviews

Fromm, M., Faerman, E., Berrendorf, M., Bhargava, S., Qi, R., Zhang, Y., Dennert, L., Selle, S., Mao, Y., & Seidl, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16607 · Cited by 19

The Impact of AI and Cross-Border Data Regulation on International Trade in Digital Services: A Large Language Model

Sun, R., & Trefler, D. (2023) · doi:10.3386/w31925 · Cited by 19

PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants

(2024) · doi:10.1093/bib/bbae324 · Cited by 19

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Simultaneous learning of pivots and representations for cross-domain sentiment classification

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