Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Artificial intelligence in emergency medicine: a primer for the nonexpert

Smith, M. E., Zalesky, C. C., Lee, S., Gottlieb, M., Adhikari, S., Goebel, M., Wegman, M., Garg, N., & Lam, S. H. F. (2025) · JACEP Open · doi:10.1016/j.acepjo.2025.100051 · Cited by 18

Developer Behaviors in Validating and Repairing LLM-Generated Code Using IDE and Eye Tracking

Tang, N., Chen, M., Ning, Z., Bansal, A., Huang, Y., McMillan, C., & Li, T. J.-J. (2024) · 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00015 · Cited by 18

Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking

Akhtar, M., Cocarascu, O., & Simperl, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.30 · Cited by 18

Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.182 · Cited by 20

Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters

Schuster, T., Chen, S., Buthpitiya, S., Fabrikant, A., & Metzler, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.28 · Cited by 17

A Survey on the Integration and Optimization of Large Language Models in Edge Computing Environments

Bhardwaj, S., Singh, P., & Pandit, M. K. (2024) · 2024 16th International Conference on Computer and Automation Engineering (ICCAE) · doi:10.1109/iccae59995.2024.10569285 · Cited by 18

Complex-valued neural networks for voice anti-spoofing

Müller, N. M., Sperl, P., & Böttinger, K. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-901 · Cited by 17

AI Study Partner : Development of an LLM and Gen AI-Enhanced Study Assistant Tool

Information Science and Engineering & Bannari Amman Institute of Technology, & P, J. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem29626 · Cited by 17

RCDPT: Radar-Camera Fusion Dense Prediction Transformer

Lo, C.-C., & Vandewalle, P. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10096129 · Cited by 18

Multi-scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-charge in Battery Energy Storage Systems

Liu, H., Liang, F., Hu, T., Hong, J., & Ma, H. (2024) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2023.000726 · Cited by 17

Large language models at work in China’s labor market

Chen, Q., Ge, J., Xie, H., Xu, X., & Yang, Y. (2025) · China Economic Review · doi:10.1016/j.chieco.2025.102413 · Cited by 17

SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings

Papa, L., Alati, E., Russo, P., & Amerini, I. (2022) · IEEE Access · doi:10.1109/access.2022.3170425 · Cited by 17

Abstraction for Deep Reinforcement Learning

Shanahan, M., & Mitchell, M. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/780 · Cited by 17

Disciplinary variation of metadiscourse: A comparison of human-written and ChatGPT-generated English research article abstracts

Zhang, M., & Zhang, J. (2025) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2025.101540 · Cited by 17

Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments

Rigaki, M., Lukáš, O., Catania, C., & Garcia, S. (2024) · Proceedings of the 16th International Conference on Agents and Artificial Intelligence · doi:10.5220/0012391800003636 · Cited by 17

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

(2025) · doi:10.1016/j.compbiomed.2025.109695 · Cited by 17

A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving

Lu, J., Tang, S., Wang, J., Zhu, H., & Wang, Y. (2019) · 2019 Chinese Control And Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832398 · Cited by 17

Holistic Root Cause Analysis for Failures in Cloud-Native Systems Through Observability Data

Han, Y., Du, Q., Huang, Y., Li, P., Shi, X., Wu, J., Fang, P., Tian, F., & He, C. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3478759 · Cited by 16

Heterogeneous Data-Driven Failure Diagnosis for Microservice-Based Industrial Clouds Toward Consumer Digital Ecosystems

Xu, Y., Qiu, Z., Gao, H., Zhao, X., Wang, L., & Li, R. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3337351 · Cited by 16

Continuous document layout analysis: Human-in-the-loop AI-based data curation, database, and evaluation in the domain of public affairs

Peña, A., Morales, A., Fierrez, J., Ortega-Garcia, J., Puente, I., Cordova, J., & Cordova, G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102398 · Cited by 16

Extracting Cause of Death from Verbal Autopsy with Deep Learning interpretable methods

Blanco, A., Perez, A., Casillas, A., & Cobos, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3005769 · Cited by 17

Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

Rippel, O., Chavan, A., Lei, C., & Merhof, D. (2022) · Proceedings of the 2nd International Conference on Image Processing and Vision Engineering · doi:10.5220/0011063900003209 · Cited by 17

How effectively can machines defend against machine-generated fake news? An empirical study

Bhat, M. M., & Parthasarathy, S. (2020) · Proceedings of the First Workshop on Insights from Negative Results in NLP · doi:10.18653/v1/2020.insights-1.7 · Cited by 16

Generative artificial intelligence performs rudimentary structural biology modeling

Ille, A. M., Markosian, C., Burley, S. K., Mathews, M. B., Pasqualini, R., & Arap, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-69021-2 · Cited by 16

Untitled work

(2021) · doi:10.18653/v1/2021.naacl-main.108 · Cited by 16

How to protect copyright data in optimization of large language models?

Chu, T., Song, Z., & Yang, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29741 · Cited by 16

Automation of Legal Precedents Retrieval: Findings from a Literature Review

Mentzingen, H., António, N., & Bacao, F. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/6660983 · Cited by 16

A Needs Assessment to Support Faculty Members’ Awareness of Generative AI Technologies to Support Instruction

Mathew, R., & Stefaniak, J. E. (2024) · TechTrends · doi:10.1007/s11528-024-00964-z · Cited by 15

Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments

Queyrut, S., Schiavoni, V., & Felber, P. (2023) · 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00069 · Cited by 18

Integrating automated knowledge extraction with large language models for explainable medical decision-making.

Zhang, H., Li, J., Wang, Y., & Song, Y. (2023) · 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385557 · Cited by 15

Uncertainty-Aware Machine Support for Paper Reviewing on the Interspeech 2019 Submission Corpus

Stappen, L., Rizos, G., Hasan, M., Hain, T., & Schuller, B. W. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2862 · Cited by 15

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network

Zhang, X., Yin, H., Ling, F., Zhan, J., & Zhou, Y. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011330 · Cited by 15

Ad Headline Generation using Self-Critical Masked Language Model

Kanungo, Y. S., Negi, S., & Rajan, A. (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.33 · Cited by 15

Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers

Alami, A., & Ernst, N. (2025) · 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) · doi:10.1109/chase66643.2025.00016 · Cited by 15

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

Kulikova, A. V., Diaz, D. J., Chen, T., Cole, T. J., Ellington, A. D., & Wilke, C. O. (2023) · Scientific Reports · doi:10.1038/s41598-023-40247-w · Cited by 15

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Breimann, S., Kamp, F., Steiner, H., & Frishman, D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168717 · Cited by 15

Is Genre Enough? A Theory of Genre Signaling as Generative AI Rhetoric

Omizo, R., & Hart-Davidson, B. (2024) · Rhetoric Society Quarterly · doi:10.1080/02773945.2024.2343615 · Cited by 16

Fifty Shades of ChatGPT: Aktueller Diskussions- und Forschungsstand zu Sex und künstlicher Intelligenz

Döring, N. (2023) · Zeitschrift für Sexualforschung · doi:10.1055/a-2142-9527 · Cited by 14

Fine-tuning protein language models unlocks the potential of underrepresented viral proteomes

Sawhney, R., Ferrell, B. D., Dejean, T., Schreiber, Z., Harrigan, W., Polson, S. W., Wommack, K. E., & Belcaid, M. (2025) · PeerJ · doi:10.7717/peerj.19919 · Cited by 14

TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models

Yang, Z., Cui, Y., & Chen, Z. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.4 · Cited by 14

Debiasing pretrained text encoders by paying attention to paying attention

Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.651 · Cited by 14

Medical multimodal large language models: A systematic review

Hu, Y., Xu, C., Lin, B., Yang, W., & Tang, Y. Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.09.005 · Cited by 14

Unveiling the risks of ChatGPT in diagnostic surgical pathology

Guastafierro, V., Corbitt, D. N., Bressan, A., Fernandes, B., Mintemur, Ö., Magnoli, F., Ronchi, S., La Rosa, S., Uccella, S., & Renne, S. L. (2025) · Virchows Archiv · doi:10.1007/s00428-024-03918-1 · Cited by 14

scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing

Zheng, X., Meng, D., Chen, D., Wong, W.-K., To, K.-H., Zhu, L., Wu, J., Liang, Y., Leung, K.-S., Wong, M.-H., et al. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012083 · Cited by 14

RaScaNet: Learning Tiny Models by Raster-Scanning Images

Yoo, J., Lee, D., Son, C., Jung, S., Yoo, B., Choi, C., Han, J.-J., & Han, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01346 · Cited by 14

HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors

Zhang, H.-K., Zhang, Y.-G., Zhou, Z., & Li, Y.-F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29615 · Cited by 14

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

Zhang, T., Liu, Y., Li, B., Zeng, Z., Wang, P., You, Y., Miao, C., & Cui, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.247 · Cited by 14

Region Based Robust Facial Expression Analysis

Lian, Z., Li, Y., Tao, J., Huang, J., & Niu, M. (2018) · 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470391 · Cited by 14

Trained on 100 million words and still in shape: BERT meets British National Corpus

Samuel, D., Kutuzov, A., Øvrelid, L., & Velldal, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.146 · Cited by 14

Rethinking teacher education in an AI world: perceptions, readiness and institutional support for generative AI integration

Ceallaigh, T. J. Ó., O’ Brien, E., Tømte, C., Kulaksız, T., & Connolly, C. (2025) · European Journal of Teacher Education · doi:10.1080/02619768.2025.2563696 · Cited by 14

The NLP Task Effectiveness of Long-Range Transformers

Qin, G., Feng, Y., & Van Durme, B. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.273 · Cited by 14

Distinguishing Fact from Fiction: A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era.

Mosca, E., Abdalla, M. H. I., Basso, P., Musumeci, M., & Groh, G. (2023) · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) · doi:10.18653/v1/2023.trustnlp-1.17 · Cited by 14

Background Reconstruction via 3D-Transformer Network for Hyperspectral Anomaly Detection

Wu, Z., & Wang, B. (2023) · Remote Sensing · doi:10.3390/rs15184592 · Cited by 13

TAMO:Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent With Multi-Modality Observation Data in Cloud-Native Systems

Zhang, X., Wang, Q., Li, M., Yuan, Y., Xiao, M., Zhuang, F., & Yu, D. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3629066 · Cited by 15

Integrating transformers into physics-informed neural networks: An approach to lithium-ion battery state-of-health prognostics

Zhan, Y., Yan, K., & Zheng, X. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111173 · Cited by 14

Beyond Medical Imaging - A Review of Multimodal Deep Learning in Radiology

Heiliger, L., Sekuboyina, A., Menze, B., Egger, J., & Kleesiek, J. (2022) · doi:10.36227/techrxiv.19103432 · Cited by 13

LS-Tree: Model Interpretation When the Data Are Linguistic

Chen, J., & Jordan, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5749 · Cited by 14

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Ghilardi, L., & Furfaro, R. (2023) · Sensors · doi:10.3390/s23187844 · Cited by 14

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs

Chen, J., Yoon, J., Ebrahimi, S., Arik, S., Pfister, T., & Jha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.345 · Cited by 13

FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering

Kedia, A., Zaidi, M. A., & Lee, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.285 · Cited by 14

Grasp Stability Assessment Through Spatio-Temporal Attention Mechanism and Adaptive Gate Fusion

Li, S., Sun, W., Liang, Q., Sun, J., & Liu, C. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3493117 · Cited by 13

Escaping the Drug-Bias Trap: Using Debiasing Design to Improve Interpretability and Generalization of Drug-Target Interaction Prediction

Zhang, P., Ma, J., & Chen, T. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3576488 · Cited by 13

Paraphrastic Representations at Scale

Wieting, J., Gimpel, K., Neubig, G., & Berg-kirkpatrick, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2022.emnlp-demos.38 · Cited by 13

On the Application of Sentence Transformers to Automatic Short Answer Grading in Blended Assessment

Ahmed, A., Joorabchi, A., & Hayes, M. J. (2022) · 2022 33rd Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc55427.2022.9826194 · Cited by 13

Using In-Context Learning to Improve Dialogue Safety

Meade, N., Gella, S., Hazarika, D., Gupta, P., Jin, D., Reddy, S., Liu, Y., & Hakkani-Tur, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.796 · Cited by 13

A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications

Nassar, M., Rogers, A. B., Talo', F., Sanchez, S., Shafique, Z., Finn, R. D., & McEntyre, J. (2022) · GigaScience · doi:10.1093/gigascience/giac077 · Cited by 13

Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture

Katı, B. E., Küçüksille, E. U., & Sarıman, G. (2025) · Applied Sciences · doi:10.3390/app15020525 · Cited by 12

Gibbon: Efficient Co-Exploration of NN Model and Processing-In-Memory Architecture

Sun, H., Wang, C., Zhu, Z., Ning, X., Dai, G., Yang, H., & Wang, Y. (2022) · 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date54114.2022.9774605 · Cited by 15

A physics-constrained Informer-LSTM network for battery state-of-charge estimation

Xie, Y., Han, Z., Zhang, J., & Liu, L. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118381 · Cited by 15

Multi-stage Pre-training over Simplified Multimodal Pre-training Models

Liu, T., Feng, F., & Wang, X. (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.199 · Cited by 11

Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning

Wu, Z., Yu, C., Chen, C., Hao, J., & Zhuo, H. H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26241 · Cited by 12

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Gueto-Tettay, C., Tang, D., Happonen, L., Heusel, M., Khakzad, H., Malmström, J., & Malmström, L. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010457 · Cited by 12

Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost

(2024) · doi:10.1002/pro.5239 · Cited by 12

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

Clement, C., Lu, S., Liu, X., Tufano, M., Drain, D., Duan, N., Sundaresan, N., & Svyatkovskiy, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.387 · Cited by 12

Towards a science exocortex

Yager, K. G. (2024) · Digital Discovery · doi:10.1039/d4dd00178h · Cited by 12

Guiding Visual Question Generation

Vedd, N., Wang, Z., Rei, M., Miao, Y., & Specia, L. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.118 · Cited by 12

Improving Controllable Text Generation with Position-Aware Weighted Decoding

Gu, Y., Feng, X., Ma, S., Wu, J., Gong, H., & Qin, B. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.272 · Cited by 12

Untitled work

(2023) · doi:10.18653/v1/2023.acl-short.38 · Cited by 12

Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages

Khraisha, Q., Put, S., Kappenberg, J., Warraitch, A., & Hadfield, K. (2023) · arXiv · doi:10.48550/arxiv.2310.17526 · Cited by 268

Automatic Post-Traumatic Stress Disorder Diagnosis via Clinical Transcripts: A Novel Text Augmentation with Large Language Models

Wu, Y., Chen, J., Mao, K., & Zhang, Y. (2023) · 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas58349.2023.10388714 · Cited by 13

Std-net: spatio-temporal decomposition network for video demoiréing with sparse transformers

Niu, Y., Xu, R., Lin, Z., & Liu, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3386604 · Cited by 12

The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution

Altakrori, M., Cheung, J. C. K., & Fung, B. C. M. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.359 · Cited by 11

“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models

Patel, R., & Pavlick, E. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.790 · Cited by 11

Chatbot-assisted quantum chemistry for explicitly solvated molecules

(2025) · doi:10.1039/d4sc08677e · Cited by 11

Exploring the Robustness of In-Context Learning with Noisy Labels

Cheng, C., Yu, X., Wen, H., Sun, J., Yue, G., Zhang, Y., & Wei, Z. (2025) · ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49660.2025.10889281 · Cited by 11

Automated invoice processing: Machine learning-based information extraction for long tail suppliers

Krieger, F., Drews, P., & Funk, B. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200285 · Cited by 11

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma

Rudroff, T., Rainio, O., & Klén, R. (2024) · Brain Sciences · doi:10.3390/brainsci14111111 · Cited by 11

Unleashing the potential of cell painting assays for compound activities and hazards prediction

Odje, F., Meijer, D., von Coburg, E., van der Hooft, J. J. J., Dunst, S., Medema, M. H., & Volkamer, A. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1401036 · Cited by 11

XAI-based Clinical Decision Support System: A Systematic Review

Kim, S., Kim, D., Kim, M., Ko, H., & Jeong, O. (2024) · doi:10.20944/preprints202406.0721.v1 · Cited by 72

RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation

Zhao, Y., Zhao, Z., Li, J., Dong, S., & Gao, S. (2024) · 2024 International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00132 · Cited by 10

Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health

Li, L., Kong, S., Zhao, H., Li, C., Teng, Y., & Wang, Y. (2025) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13781 · Cited by 10

Transformer Network-Aided Relative Pose Estimation for Non-cooperative Spacecraft Using Vision Sensor

Ahmed, J., Arshad, A., Bang, H., & Choi, Y. (2024) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-023-00703-3 · Cited by 11

Multimodal Fusion with Co-attention Mechanism

Li, P., & Li, X. (2020) · 2020 IEEE 23rd International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190483 · Cited by 14

Untitled work

(2021) · doi:10.18653/v1/2021.findings-acl.427 · Cited by 10

TransHLA: a Hybrid Transformer model for HLA-presented epitope detection

Lu, T., Wang, X., Nie, W., Huo, M., & Li, S. (2025) · GigaScience · doi:10.1093/gigascience/giaf008 · Cited by 10

On-the-Fly Attention Modulation for Neural Generation

Dong, Y., Bhagavatula, C., Lu, X., Hwang, J. D., Bosselut, A., Cheung, J. C. K., & Choi, Y. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.107 · Cited by 10

Generating Meaning: Active Inference and the Scope and Limits of Passive AI

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2023) · doi:10.31234/osf.io/8xgzv · Cited by 8

Fault Localization for Microservice Applications with System Logs and Monitoring Metrics

Zhang, Q., Jia, T., Wu, Z., Wu, Q., Jia, L., Li, D., Tao, Y., & Xiao, Y. (2022) · 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda55098.2022.9778893 · Cited by 13

What BERT Sees: Cross-Modal Transfer for Visual Question Generation

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