Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.241 · Cited by 38

Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation

Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, L., He, M., & Ge, Z. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104281 · Cited by 39

The Crowdless Future? Generative AI and Creative Problem Solving

Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343

Overcoming a Theoretical Limitation of Self-Attention

Chiang, D., & Cholak, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.527 · Cited by 39

The PAU Survey: Photometric redshifts using transfer learning from simulations

Eriksen, M., Alarcon, A., Cabayol, L., Carretero, J., Casas, R., Castander, F. J., De Vicente, J., Fernandez, E., Garcia-Bellido, J., Gaztanaga, E., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2265 · Cited by 39

Concept-Aware Video Captioning: Describing Videos With Effective Prior Information

Yang, B., Cao, M., & Zou, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3307969 · Cited by 39

VITO-Transformer: A Visual-Tactile Fusion Network for Object Recognition

Li, B., Bai, J., Qiu, S., Wang, H., & Guo, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3326241 · Cited by 39

Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects

Ma, X., Hsu, D., & Lee, W. S. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811597 · Cited by 39

Rethinking the Value of Transformer Components

Wang, W., & Tu, Z. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.529 · Cited by 39

SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers

Marchisio, A., Dura, D., Capra, M., Martina, M., Masera, G., & Shafique, M. (2023) · 2023 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191521 · Cited by 39

Multilingual Code Snippets Training for Program Translation

Zhu, M., Suresh, K., & Reddy, C. K. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21434 · Cited by 39

Understanding Self-attention Mechanism via Dynamical System Perspective

Huang, Z., Liang, M., Qin, J., Zhong, S., & Lin, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00136 · Cited by 44

Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality

Khosravi, P., Fuchs, T. J., & Ho, D. J. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-3630 · Cited by 41

End-to-End Video Question-Answer Generation With Generator-Pretester Network

Su, H.-T., Chang, C.-H., Shen, P.-W., Wang, Y.-S., Chang, Y.-L., Chang, Y.-C., Cheng, P.-J., & Hsu, W. H. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3051277 · Cited by 38

Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach

Françani, A. O., & Maximo, M. R. O. A. (2023) · arXiv · doi:10.48550/arxiv.2305.06121 · Cited by 38

Read and Attend: Temporal Localisation in Sign Language Videos

Varol, G., Momeni, L., Albanie, S., Afouras, T., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01658 · Cited by 39

Protein representations: Encoding biological information for machine learning in biocatalysis

Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38

SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT

Bajgoti, A., Gupta, R., Balaji, P., Dwivedi, R., Siwach, M., & Gupta, D. (2023) · IEEE Access · doi:10.1109/access.2023.3321801 · Cited by 38

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

Geng, S., Gao, P., Chatterjee, M., Hori, C., Le Roux, J., Zhang, Y., Li, H., & Cherian, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16231 · Cited by 38

RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.275 · Cited by 37

Exploring the Effectiveness of Institutional Policies and Regulations for Generative AI Usage in Higher Education

Jiang, Y., Xie, L., & Cao, X. (2025) · Higher Education Quarterly · doi:10.1111/hequ.70054 · Cited by 37

GPT-4 vs. GPT-3.5: A Concise Showdown

Koubaa, A. (2023) · doi:10.20944/preprints202303.0422.v1 · Cited by 37

Clinical insights: A comprehensive review of language models in medicine

Neveditsin, N., Lingras, P., & Mago, V. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000800 · Cited by 37

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model

Tang, S., Wang, Y., Kong, Z., Zhang, T., Li, Y., Ding, C., Wang, Y., Liang, Y., & Xu, D. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01038 · Cited by 37

CpG Transformer for imputation of single-cell methylomes

De Waele, G., Clauwaert, J., Menschaert, G., & Waegeman, W. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab746 · Cited by 37

Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans

Reviriego, P., Conde, J., Merino-Gómez, E., Martínez, G., & Hernández, J. A. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100602 · Cited by 37

Skill Induction and Planning with Latent Language

Sharma, P., Torralba, A., & Andreas, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.120 · Cited by 39

Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation

Xia, H., Ge, T., Wang, P., Chen, S.-Q., Wei, F., & Sui, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.257 · Cited by 40

Instructors and learners' perspectives on using ChatGPT in English as a foreign language courses and its effect on academic integrity

Almanea, M. (2026) · Computer Assisted Language Learning · doi:10.1080/09588221.2024.2410158 · Cited by 40

A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in lithium-ion batteries

Jin, H., Ru, R., Cai, L., Meng, J., Wang, B., Peng, J., & Yang, S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2024.124629 · Cited by 39

“Math word problem generation with mathematical consistency and problem context constraints,”

Wang, Z., Lan, A., & Baraniuk, R. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.484 · Cited by 39

Breaking the barriers of data scarcity in drug–target affinity prediction

Pei, Q., Wu, L., Zhu, J., Xia, Y., Xie, S., Qin, T., Liu, H., Liu, T.-Y., & Yan, R. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad386 · Cited by 36

RefSum: Refactoring Neural Summarization

Liu, Y., Dou, Z.-Y., & Liu, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.113 · Cited by 36

Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression

Huang, J., & Kaewunruen, S. (2023) · Energies · doi:10.3390/en16020966 · Cited by 38

Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments

Jungherr, A. (2023) · doi:10.31235/osf.io/d84q6 · Cited by 37

Dynamic alignment and fusion of multimodal physiological patterns for stress recognition

Zhang, X., Wei, X., Zhou, Z., Zhao, Q., Zhang, S., Yang, Y., Li, R., & Hu, B. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3290177 · Cited by 38

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Isgut, M., Gloster, L., Choi, K., Venugopalan, J., & Wang, M. D. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3216531 · Cited by 35

LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models

Zhang, J., Guo, J., Sun, S., Lou, J.-G., & Zhang, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00664 · Cited by 35

Hierarchical multimodal fusion framework based on noisy label learning and attention mechanism for cancer classification with pathology and genomic features

Qiu, L., Zhao, L., Hou, R., Zhao, W., Zhang, S., Lin, Z., Teng, H., & Zhao, J. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102176 · Cited by 35

NeuTral Rewriter: A rule-based and neural approach to automatic rewriting into gender neutral alternatives

Vanmassenhove, E., Emmery, C., & Shterionov, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.704 · Cited by 35

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Yao, J., Fan, X., Li, B., & Qin, W. (2022) · Sensors · doi:10.3390/s22218577 · Cited by 35

A Transformer-Based Neural Network for Gait Prediction in Lower Limb Exoskeleton Robots Using Plantar Force

Ren, J., Wang, A., Li, H., Yue, X., & Meng, L. (2023) · Sensors · doi:10.3390/s23146547 · Cited by 34

Detecting Alzheimer’s Disease from Speech Using Neural Networks with Bottleneck Features and Data Augmentation

Liu, Z., Guo, Z., Ling, Z., & Li, Y. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413566 · Cited by 34

A Pilot Study Inquiring into the Impact of ChatGPT on Lab Report Writing in Introductory Engineering Labs

Kim, D., Majdara, A., & Olson, W. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.691 · Cited by 34

COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation

Wen, K., Xia, J., Huang, Y., Li, L., Xu, J., & Shao, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00221 · Cited by 37

How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech

Bizzoni, Y., Juzek, T. S., España-Bonet, C., Dutta Chowdhury, K., van Genabith, J., & Teich, E. (2020) · Proceedings of the 17th International Conference on Spoken Language Translation · doi:10.18653/v1/2020.iwslt-1.34 · Cited by 34

PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models

Feng, T., & Narayanan, S. (2023) · 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii59096.2023.10388152 · Cited by 34

Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Tay, Y., Dehghani, M., Abnar, S., Chung, H., Fedus, W., Rao, J., Narang, S., Tran, V., Yogatama, D., & Metzler, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.825 · Cited by 34

Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction

Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3592788 · Cited by 34

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

Wu, Y., Liu, Q., Qiu, Y., & Xie, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010367 · Cited by 34

Video ReCap: Recursive Captioning of Hour-Long Videos

Islam, M. M., Ho, N., Yang, X., Nagarajan, T., Torresani, L., & Bertasius, G. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01723 · Cited by 34

MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild

Chumachenko, K., Iosifidis, A., & Gabbouj, M. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00470 · Cited by 34

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks

Tripathy, R. K., Frohock, Z., Wang, H., Cary, G. A., Keegan, S., Carter, G. W., & Li, Y. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00519-9 · Cited by 36

LightSeq: A High Performance Inference Library for Transformers

Wang, X., Xiong, Y., Wei, Y., Wang, M., & Li, L. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers · doi:10.18653/v1/2021.naacl-industry.15 · Cited by 36

Make RepVGG Greater Again: A Quantization-Aware Approach

Chu, X., Li, L., & Zhang, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29045 · Cited by 53

EQ-ViT: Algorithm-Hardware Co-Design for End-to-End Acceleration of Real-Time Vision Transformer Inference on Versal ACAP Architecture

Dong, P., Zhuang, J., Yang, Z., Ji, S., Li, Y., Xu, D., Huang, H., Hu, J., Jones, A. K., Shi, Y., et al. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443692 · Cited by 35

Cyberbullying Detection using LSTM-CNN architecture and its applications

Gada, M., Damania, K., & Sankhe, S. (2021) · 2021 International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci50826.2021.9402412 · Cited by 33

Embodied navigation with multi-modal information: A survey from tasks to methodology

Wu, Y., Zhang, P., Gu, M., Zheng, J., & Bai, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102532 · Cited by 34

Roles and Utilization of Attention Heads in Transformer-based Neural Language Models

Jo, J.-Y., & Myaeng, S.-H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.311 · Cited by 33

What Language Model to Train if You Have One Million GPU Hours?

Le Scao, T., Wang, T., Hesslow, D., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., Press, O., et al. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.54 · Cited by 33

Capturing large genomic contexts for accurately predicting enhancer-promoter interactions

Chen, K., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab577 · Cited by 33

Multimodal Emotion Recognition in Deep Learning:a Survey

Gu, X., Shen, Y., & Xu, J. (2021) · 2021 International Conference on Culture-oriented Science & Technology (ICCST) · doi:10.1109/iccst53801.2021.00027 · Cited by 36

A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures

Baughan, A., Wang, X., Liu, A., Mercurio, A., Chen, J., & Ma, X. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581152 · Cited by 32

Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024

Žigutytė, L., Sorz-Nechay, T., Clusmann, J., & Kather, J. N. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101209 · Cited by 33

AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes

Cai, L., Huang, S., Zhang, Y., Lu, J., & Zhang, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103631 · Cited by 33

SiET: Spatial information enhanced transformer for multivariate time series anomaly detection

Xiong, W., Wang, P., Sun, X., & Wang, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111928 · Cited by 32

AB-Gen: Antibody Library Design with Generative Pre-Trained Transformer and Deep Reinforcement Learning

Xu, X., Xu, T., Zhou, J., Liao, X., Zhang, R., Wang, Y., Zhang, L., & Gao, X. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.03.004 · Cited by 32

A Review of Multimodal Fusion–Based Deep Learning for Alzheimer’s Disease

Zhang, R., Sheng, J., Zhang, Q., Wang, J., & Wang, B. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.04.035 · Cited by 32

RNA language models predict mutations that improve RNA function

Shulgina, Y., Trinidad, M. I., Langeberg, C. J., Nisonoff, H., Chithrananda, S., Skopintsev, P., Nissley, A. J., Patel, J., Boger, R. S., Shi, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54812-y · Cited by 32

A dataset and benchmark for hospital course summarization with adapted large language models

Aali, A., Van Veen, D., Arefeen, Y. I., Hom, J., Bluethgen, C., Reis, E. P., Gatidis, S., Clifford, N., Daws, J., Tehrani, A. S., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae312 · Cited by 32

The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions

Zhou, X., Nie, Y., Tan, H., & Bansal, M. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.659 · Cited by 32

PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis

Song, M., Wang, J., Yu, Z., Wang, J., Yang, L., Lu, Y., Li, B., Wang, X., Wang, X., Huang, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103248 · Cited by 31

ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review

Khowaja, S. A., Khuwaja, P., Dev, K., Wang, W., & Nkenyereye, L. (2023) · doi:10.36227/techrxiv.22619932.v2 · Cited by 183

A Multitask Multimodal Framework for Sentiment and Emotion-Aided Cyberbullying Detection

Maity, K., Kumar, A., & Saha, S. (2022) · IEEE Internet Computing · doi:10.1109/mic.2022.3158583 · Cited by 30

Untitled work

(2019) · doi:10.1609/aaai.v33i01.33016618 · Cited by 31

Fundus Disease Image Classification based on Improved Transformer

Yang, H., Chen, J., & Xu, M. (2021) · 2021 International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc52316.2021.9608181 · Cited by 31

CALYPSO: LLMs as Dungeon Master's Assistants

Zhu, A., Martin, L., Head, A., & Callison-Burch, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v19i1.27534 · Cited by 31

BiViT: Extremely Compressed Binary Vision Transformers

He, Y., Lou, Z., Zhang, L., Liu, J., Wu, W., Zhou, H., & Zhuang, B. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00520 · Cited by 31

Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication

Fu, L., Newman, B., Jakesch, M., & Kreps, S. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581351 · Cited by 31

Missing-modality enabled multi-modal fusion architecture for medical data

Wang, M., Fan, S., Li, Y., Xie, Z., & Chen, H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104796 · Cited by 31

Double layer detection of internal threat in enterprise systems based on deep learning

He, D., Lv, X., Xu, X., Chan, S., & Choo, K.-K. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3372771 · Cited by 31

Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Nanda, N., Lee, A., & Wattenberg, M. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/2023.blackboxnlp-1.2 · Cited by 55

MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition

Cheng, D., Zhang, L., Qin, L., Wang, S., Wu, H., & Song, A. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3373019 · Cited by 31

Aligning Subtitles in Sign Language Videos

Bull, H., Afouras, T., Varol, G., Albanie, S., Momeni, L., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01135 · Cited by 30

AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

Chronopoulou, A., Peters, M., Fraser, A., & Dodge, J. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.153 · Cited by 30

Pre-training in Medical Data: A Survey

Qiu, Y., Lin, F., Chen, W., & Xu, M. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1382-8 · Cited by 30

An Efficient Hardware Accelerator for Sparse Transformer Neural Networks

Fang, C., Guo, S., Wu, W., Lin, J., Wang, Z., Hsu, M. K., & Liu, L. (2022) · 2022 IEEE International Symposium on Circuits and Systems (ISCAS) · doi:10.1109/iscas48785.2022.9937659 · Cited by 30

ProkBERT family: genomic language models for microbiome applications

Ligeti, B., Szepesi-Nagy, I., Bodnár, B., Ligeti-Nagy, N., & Juhász, J. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1331233 · Cited by 30

Leveraging protein language models for accurate multiple sequence alignments

(2023) · doi:10.1101/gr.277675.123 · Cited by 30

Towards Fully 8-bit Integer Inference for the Transformer Model

Lin, Y., Li, Y., Liu, T., Xiao, T., Liu, T., & Zhu, J. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/520 · Cited by 30

Asking questions like educational experts: automatically generating question-answer pairs on real-world examination data

Qu, F., Jia, X., & Wu, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.202 · Cited by 30

RankGen: Improving Text Generation with Large Ranking Models

Krishna, K., Chang, Y., Wieting, J., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.15 · Cited by 30

Accelerating Transformer Inference for Translation via Parallel Decoding

Santilli, A., Severino, S., Postolache, E., Maiorca, V., Mancusi, M., Marin, R., & Rodola, E. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.689 · Cited by 29

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

Shen, Y., Tay, Y., Zheng, C., Bahri, D., Metzler, D., & Courville, A. (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.559 · Cited by 29

A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering

Naseem, T., Ravishankar, S., Mihindukulasooriya, N., Abdelaziz, I., Lee, Y.-S., Kapanipathi, P., Roukos, S., Gliozzo, A., & Gray, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.18653/v1/2021.acl-short.34 · Cited by 29

scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism

Jiang, Q., Chen, S., Chen, X., & Jiang, R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae265 · Cited by 29

ModelShield: Adaptive and Robust Watermark Against Model Extraction Attack

Pang, K., Qi, T., Wu, C., Bai, M., Jiang, M., & Huang, Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3530691 · Cited by 29

Open data and algorithms for open science in AI-driven molecular informatics

Brinkhaus, H. O., Rajan, K., Schaub, J., Zielesny, A., & Steinbeck, C. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102542 · Cited by 29

Feature Augmented Memory with Global Attention Network for VideoQA

Cai, J., Yuan, C., Shi, C., Li, L., Cheng, Y., & Shan, Y. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/139 · Cited by 29

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

Van Veen, D., Van Uden, C., Attias, M., Pareek, A., Bluethgen, C., Polacin, M., Chiu, W., Delbrouck, J.-B., Zambrano Chaves, J., Langlotz, C., et al. (2023) · The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks · doi:10.18653/v1/2023.bionlp-1.42 · Cited by 25