Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Context-aware Alignment and Mutual Masking for 3D-Language Pre-training

Jin, Z., Hayat, M., Yang, Y., Guo, Y., & Lei, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01057 · Cited by 56

The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process

Abdar, M., Khosravi, A., Islam, S. M. S., Acharya, U. R., & Vasilakos, A. V. (2022) · IEEE Systems, Man, and Cybernetics Magazine · doi:10.1109/msmc.2022.3150144 · Cited by 55

Embedding-based alignment: combining protein language models with dynamic programming alignment to detect structural similarities in the twilight-zone

Pantolini, L., Studer, G., Pereira, J., Durairaj, J., Tauriello, G., & Schwede, T. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btad786 · Cited by 55

ChatGPT in higher education—a student's perspective

Shuhaiber, A., Kuhail, M. A., & Salman, S. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100565 · Cited by 55

Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?

Wang, C., Liu, P., & Zhang, Y. (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.251 · Cited by 55

Training language models with memory augmentation

Zhong, Z., Lei, T., & Chen, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.382 · Cited by 60

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Tejero, R., Huang, Y. J., Ramelot, T. A., & Montelione, G. T. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.877000 · Cited by 56

Graph representation learning for single-cell biology

Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55

Efficient Multimodal Fusion via Interactive Prompting

Li, Y., Quan, R., Zhu, L., & Yang, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00256 · Cited by 60

Neural Machine Translation of Logographic Language Using Sub-character Level Information

Zhang, L., & Komachi, M. (2018) · Proceedings of the Third Conference on Machine Translation: Research Papers · doi:10.18653/v1/w18-6303 · Cited by 53

Protecting Intellectual Property of Language Generation APIs with Lexical Watermark

He, X., Xu, Q., Lyu, L., Wu, F., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21321 · Cited by 56

A systematic review of current AI techniques used in the context of the SDGs

Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-024-00668-5 · Cited by 55

Detecting and Grounding Multi-Modal Media Manipulation and Beyond

Shao, R., Wu, T., Wu, J., Nie, L., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367749 · Cited by 53

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Geva, M., Bastings, J., Filippova, K., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.751 · Cited by 53

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease

Kim, J., Wang, K., Weng, C., & Liu, C. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.08.010 · Cited by 53

Taming unbalanced training workloads in deep learning with partial collective operations

Li, S., Ben-Nun, T., Girolamo, S. D., Alistarh, D., & Hoefler, T. (2020) · Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3332466.3374528 · Cited by 57

Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-Modal Pretraining

Zhan, X., Wu, Y., Dong, X., Wei, Y., Lu, M., Zhang, Y., Xu, H., & Liang, X. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01157 · Cited by 54

Automatic Poetry Generation from Prosaic Text

Van de Cruys, T. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.223 · Cited by 50

MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records

Xu, Z., So, D. R., & Dai, A. M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17260 · Cited by 52

Large language models overcome the challenges of unstructured text data in ecology

Castro, A., Pinto, J., Reino, L., Pipek, P., & Capinha, C. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102742 · Cited by 52

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Wang, Z., Li, Y., Zhai, J., Yang, S., Sun, B., & Liang, P. (2024) · Talanta · doi:10.1016/j.talanta.2024.126138 · Cited by 55

A Length-Extrapolatable Transformer

Sun, Y., Dong, L., Patra, B., Ma, S., Huang, S., Benhaim, A., Chaudhary, V., Song, X., & Wei, F. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.816 · Cited by 59

SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference

Wang, W., Zhou, S., Sun, W., Sun, P., & Liu, Y. (2023) · 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) · doi:10.1109/iccad57390.2023.10323725 · Cited by 51

Improving Cyberbullying Detection with User Interaction

Ge, S., Cheng, L., & Liu, H. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449828 · Cited by 51

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16090 · Cited by 51

Untitled work

(2020) · doi:10.18653/v1/2020.findings-emnlp.190 · Cited by 51

Technological folie à deux: feedback loops between AI chatbots and mental health

Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., Summerfield, C., Shanahan, M., & Nour, M. M. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00595-8 · Cited by 51

Attentron: Few-Shot Text-to-Speech Utilizing Attention-Based Variable-Length Embedding

Choi, S., Han, S., Kim, D., & Ha, S. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2096 · Cited by 56

Multi-task paired masking with alignment modeling for medical vision-language pre-training

Zhang, K., Yang, Y., Yu, J., Jiang, H., Fan, J., Huang, Q., & Han, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3325965 · Cited by 51

LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Liu, S.-Y., Liu, Z., Huang, X., Dong, P., & Cheng, K.-T. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.39 · Cited by 50

DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation

Mao, Y., Liu, G., Wang, X., Gao, W., & Li, X. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.229 · Cited by 50

Potential of multimodal large language models for data mining of medical images and free-text reports

Zhang, Y., Pan, Y., Zhong, T., Dong, P., Xie, K., Liu, Y., Jiang, H., Wu, Z., Liu, Z., Zhao, W., et al. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100103 · Cited by 52

Contrastive Learning Models for Sentence Representations

Xu, L., Xie, H., Li, Z., Wang, F. L., Wang, W., & Li, Q. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3593590 · Cited by 50

Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Baek, J., Aji, A., & Saffari, A. (2023) · Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023) · doi:10.18653/v1/2023.matching-1.7 · Cited by 51

Log Sequence Anomaly Detection Based on Local Information Extraction and Globally Sparse Transformer Model

Zhang, C., Wang, X., Zhang, H., Zhang, H., & Han, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3125967 · Cited by 49

Can you spot the bot? Identifying AI-generated writing in college essays

Waltzer, T., Pilegard, C., & Heyman, G. D. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00158-3 · Cited by 49

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder

Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.-Y., & Overwijk, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.220 · Cited by 49

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations

Zheng, W., Yu, J., Xia, R., & Wang, S. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.861 · Cited by 51

SADeepSense: Self-Attention Deep Learning Framework for Heterogeneous On-Device Sensors in Internet of Things Applications

Yao, S., Zhao, Y., Shao, H., Liu, D., Liu, S., Hao, Y., Piao, A., Hu, S., Lu, S., & Abdelzaher, T. F. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737500 · Cited by 52

On the Paradox of Learning to Reason from Data

Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Van den Broeck, G. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/375 · Cited by 58

Knowledge of cultural moral norms in large language models

Ramezani, A., & Xu, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.26 · Cited by 54

Adapting Language Models to Compress Contexts

Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.232 · Cited by 59

Untitled work

(2023) · doi:10.1109/iros55552.2023.10341379 · Cited by 48

A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining

Peng, H., Huang, S., Chen, S., Li, B., Geng, T., Li, A., Jiang, W., Wen, W., Bi, J., Liu, H., et al. (2022) · Proceedings of the 59th ACM/IEEE Design Automation Conference · doi:10.1145/3489517.3530585 · Cited by 48

MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

Zhang, Z., Lin, Y., Liu, Z., Li, P., Sun, M., & Zhou, J. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.71 · Cited by 48

EQG-RACE: Examination-Type Question Generation

Jia, X., Zhou, W., Sun, X., & Wu, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17553 · Cited by 48

Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers

Thing, V. L. L. (2023) · 2023 IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10225004 · Cited by 48

DTITD: An Intelligent Insider Threat Detection Framework Based on Digital Twin and Self-Attention Based Deep Learning Models

Wang, Z. Q., & El Saddik, A. (2023) · IEEE Access · doi:10.1109/access.2023.3324371 · Cited by 49

Damage localization and characterization using one-dimensional convolutional neural network and a sparse network of transducers

Sattarifar, A., & Nestorović, T. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105273 · Cited by 46

A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)

Patil, R., & Gudivada, A. V. (2024) · doi:10.20944/preprints202402.0357.v1 · Cited by 296

Self-Supervised Anomaly Detection from Distributed Traces

Bogatinovski, J., Nedelkoski, S., Cardoso, J., & Kao, O. (2020) · 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc48980.2020.00054 · Cited by 50

Human Gaze Assisted Artificial Intelligence: A Review

Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48

ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis

Yang, Z., Tian, T., Kong, J., & Chen, H. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c06677 · Cited by 46

Learning towards conversational AI: A survey

Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46

Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network

Li, Y., Pan, Y., Yao, T., Chen, J., & Mei, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17034 · Cited by 49

Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues

Das, A., Selek, S., Warner, A. R., Zuo, X., Hu, Y., Kuttichi Keloth, V., Li, J., Zheng, W. J., & Xu, H. (2022) · Proceedings of the 21st Workshop on Biomedical Language Processing · doi:10.18653/v1/2022.bionlp-1.27 · Cited by 47

Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

Pan, X., Qin, P., Li, Y., Xue, H., & Chen, W. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00290 · Cited by 47

KnowCTI: Knowledge-based cyber threat intelligence entity and relation extraction

Wang, G., Liu, P., Huang, J., Bin, H., Wang, X., & Zhu, H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103824 · Cited by 47

Language models: past, present, and future

Li, H. (2022) · Communications of the ACM · doi:10.1145/3490443 · Cited by 45

Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System

Huang, J., Yang, Y., Yu, H., Li, J., & Zheng, X. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00138 · Cited by 50

Transcriptomics-Guided Slide Representation Learning in Computational Pathology

Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51

AVATAR: A Parallel Corpus for Java-Python Program Translation

Ahmad, W. U., Tushar, M. G. R., Chakraborty, S., & Chang, K.-W. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.143 · Cited by 46

Untitled work

(2020) · doi:10.14778/3494124.3494128 · Cited by 46

Early computational detection of potential high-risk SARS-CoV-2 variants

(2023) · doi:10.1016/j.compbiomed.2023.106618 · Cited by 46

Large Language Models with Controllable Working Memory

Li, D., Rawat, A. S., Zaheer, M., Wang, X., Lukasik, M., Veit, A., Yu, F., & Kumar, S. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.112 · Cited by 46

T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions

Wang, M., Xie, P., Du, Y., & Hu, X. (2023) · Applied Sciences · doi:10.3390/app13127111 · Cited by 46

Query-Key Normalization for Transformers

Henry, A., Dachapally, P. R., Pawar, S. S., & Chen, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.379 · Cited by 45

PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition

Ngwe, J. L., Lim, K. M., Lee, C. P., Ong, T. S., & Alqahtani, A. (2024) · IEEE Access · doi:10.1109/access.2024.3407108 · Cited by 45

The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing

Xin, J., Tang, R., Yu, Y., & Lin, J. (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.84 · Cited by 45

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Das, S., Islam, M. S., & Amin, M. R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00018 · Cited by 45

Generative AI and Its Educational Implications

Łodzikowski, K., Foltz, P. W., & Behrens, J. T. (2024) · Postdigital Science and Education · doi:10.1007/978-3-031-64487-0_2 · Cited by 45

Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules

Kim, J., Moon, S., Rohrbach, A., Darrell, T., & Canny, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00968 · Cited by 45

SeqXGPT: Sentence-Level AI-Generated Text Detection

Wang, P., Li, L., Ren, K., Jiang, B., Zhang, D., & Qiu, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.73 · Cited by 53

Fully Autonomous Programming with Large Language Models

Liventsev, V., Grishina, A., Härmä, A., & Moonen, L. (2023) · Proceedings of the Genetic and Evolutionary Computation Conference · doi:10.1145/3583131.3590481 · Cited by 45

Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training

Hosseini, E. A., Schrimpf, M., Zhang, Y., Bowman, S., Zaslavsky, N., & Fedorenko, E. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00137 · Cited by 63

Impacts of generative artificial intelligence on the future of labor market: a systematic review

Salari, N., Beiromvand, M., Hosseinian-Far, A., Habibi, J., Babajani, F., & Mohammadi, M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100652 · Cited by 49

Not All Attention Is Needed: Gated Attention Network for Sequence Data

Xue, L., Li, X., & Zhang, N. L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6129 · Cited by 43

ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT

Lee, H., Yoon, S., Dernoncourt, F., Kim, D. S., Bui, T., & Jung, K. (2020) · Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems · doi:10.18653/v1/2020.eval4nlp-1.4 · Cited by 43

Do Long-Range Language Models Actually Use Long-Range Context?

Sun, S., Krishna, K., Mattarella-Micke, A., & Iyyer, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.62 · Cited by 43

Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning

Biten, A. F., Gomez, L., & Karatzas, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00253 · Cited by 43

Generative power of a protein language model trained on multiple sequence alignments

(2023) · doi:10.7554/elife.79854 · Cited by 43

Contextualized medication information extraction using Transformer-based deep learning architectures

Chen, A., Yu, Z., Yang, X., Guo, Y., Bian, J., & Wu, Y. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104370 · Cited by 40

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Zheng, Y., Conrad, R. D., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., & Kolachalama, V. B. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3386108 · Cited by 44

Enhanced automated code vulnerability repair using large language models

de-Fitero-Dominguez, D., Garcia-Lopez, E., Garcia-Cabot, A., & Martinez-Herraiz, J.-J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109291 · Cited by 43

Comprehensive Examination of Instruction-Based Language Models: A Comparative Analysis of Mistral-7B and Llama-2-7B

Thakkar, H., & Manimaran, A. (2023) · 2023 International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434081 · Cited by 42

MindMiner:Uncovering Linguistic Markers of Mind Perception as a New Lens to Understand Consumer‐Smart Object Relationships

Hartmann, J., Bergner, A., & Hildebrand, C. (2023) · Journal of Consumer Psychology · doi:10.1002/jcpy.1381 · Cited by 42

A Lightweight 1-D Convolution Augmented Transformer with Metric Learning for Hyperspectral Image Classification

Hu, X., Yang, W., Wen, H., Liu, Y., & Peng, Y. (2021) · Sensors · doi:10.3390/s21051751 · Cited by 42

UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization

Zhang, R., Wang, H., Du, M., Liu, H., Zhou, Y., & Zeng, Q. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3613767 · Cited by 42

BinaryBERT: Pushing the Limit of BERT Quantization

Bai, H., Zhang, W., Hou, L., Shang, L., Jin, J., Jiang, X., Liu, Q., Lyu, M., & King, I. (2020) · arXiv · doi:10.48550/arxiv.2012.15701 · Cited by 42

RACE: Retrieval-augmented Commit Message Generation

Shi, E., Wang, Y., Tao, W., Du, L., Zhang, H., Han, S., Zhang, D., & Sun, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.372 · Cited by 42

Multimodal fusion and vision–language models: A survey for robot vision

Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 105

PMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction

van Hilten, N., Verwei, N., Methorst, J., Nase, C., Bernatavicius, A., & Risselada, H. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae069 · Cited by 41

Diet code is healthy: simplifying programs for pre-trained models of code

Zhang, Z., Zhang, H., Shen, B., & Gu, X. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549094 · Cited by 41

Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions Over Knowledge Graphs

Chen, Y., Li, H., Qi, G., Wu, T., & Wang, T. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3207477 · Cited by 41

Untitled work

(2020) · doi:10.18653/v1/2020.conll-1.17 · Cited by 41

A conformer-based classifier for variable-length utterance processing in anti-spoofing

Rosello, E., Gomez-Alanis, A., Gomez, A. M., & Peinado, A. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-1820 · Cited by 47

Large language model-based agent Schema and library for automated building energy analysis and modeling

Zhang, L., Fu, X., Li, Y., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106244 · Cited by 40

Joint Audio-Visual Attention with Contrastive Learning for More General Deepfake Detection

Zhang, Y., Lin, W., & Xu, J. (2024) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3625100 · Cited by 40

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

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Multimodal Parameter-Efficient Few-Shot Class Incremental Learning

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