Exploiting Generative AI to Scale up Intelligent Tutoring Systems

An effective WSSENet-based similarity retrieval method of large lung CT image databases

(2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.07.013 · Cited by 79

Distilling Reasoning Capabilities into Smaller Language Models

Shridhar, K., Stolfo, A., & Sachan, M. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.441 · Cited by 79

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Papadopoulos, A., Kyritsis, K., Klingelhoefer, L., Bostanjopoulou, S., Chaudhuri, K. R., & Delopoulos, A. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2961748 · Cited by 79

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

Miao, Q., Zheng, W., Lv, Y., Huang, M., Ding, W., & Wang, F.-Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123561 · Cited by 78

Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries

Heinzerling, B., & Inui, K. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.153 · Cited by 78

Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases

Chalkidis, I., Fergadiotis, M., Tsarapatsanis, D., Aletras, N., Androutsopoulos, I., & Malakasiotis, 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.22 · Cited by 78

Revisiting Temporal Modeling for CLIP-Based Image-to-Video Knowledge Transferring

Liu, R., Huang, J., Li, G., Feng, J., Wu, X., & Li, T. H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00634 · Cited by 78

Cooperative Deep Reinforcement Learning Enabled Power Allocation for Packet Duplication URLLC in Multi-Connectivity Vehicular Networks

Xue, J., Yu, K., Zhang, T., Zhou, H., Zhao, L., & Shen, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3347580 · Cited by 78

On hallucination and predictive uncertainty in conditional language generation

Xiao, Y., & Wang, W. Y. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.236 · Cited by 79

A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports

Li, Y., Wang, H., & Luo, Y. (2020) · 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313289 · Cited by 81

AgentAI: A comprehensive survey on autonomous agents in distributed AI for industry 4.0

Piccialli, F., Chiaro, D., Sarwar, S., Cerciello, D., Qi, P., & Mele, V. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128404 · Cited by 77

Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey

Zhang, J., Liu, L., Silvén, O., Pietikäinen, M., & Hu, D. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3529038 · Cited by 84

Dilated Residual Network with Multi-head Self-attention for Speech Emotion Recognition

Li, R., Wu, Z., Jia, J., Zhao, S., & Meng, H. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682154 · Cited by 82

Transforming education with generative AI (GAI): key insights and future prospects

Lang, Q., Wang, M., Yin, M., Liang, S., & Song, W. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3537618 · Cited by 76

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

Al-Tam, R. M., Al-Hejri, A. M., Narangale, S. M., Samee, N. A., Mahmoud, N. F., Al-masni, M. A., & Al-antari, M. A. (2022) · Biomedicines · doi:10.3390/biomedicines10112971 · Cited by 74

Unreflected Acceptance – Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education

Krupp, L., Steinert, S., Kiefer-Emmanouilidis, M., Avila, K. E., Lukowicz, P., Kuhn, J., Küchemann, S., & Karolus, J. (2024) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia240195 · Cited by 82

The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation

Qi, Y., Pan, Z., Hong, Y., Yang, M.-H., van den Hengel, A., & Wu, Q. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00168 · Cited by 80

A Survey on Efficient Training of Transformers

Zhuang, B., Liu, J., Pan, Z., He, H., Weng, Y., & Shen, C. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/764 · Cited by 78

Unsupervised Paraphrasing by Simulated Annealing

Liu, X., Mou, L., Meng, F., Zhou, H., Zhou, J., & Song, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.28 · Cited by 75

Imitation Attacks and Defenses for Black-box Machine Translation Systems

Wallace, E., Stern, M., & Song, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.446 · Cited by 75

CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

Yao, J., Li, H., Liu, Y., Ray, S., Cheng, Y., Zhang, Q., Du, K., Lu, S., & Jiang, J. (2025) · Proceedings of the Twentieth European Conference on Computer Systems · doi:10.1145/3689031.3696098 · Cited by 75

MultiSpeech: Multi-Speaker Text to Speech with Transformer

Chen, M., Tan, X., Ren, Y., Xu, J., Sun, H., Zhao, S., & Qin, T. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-3139 · Cited by 92

Hybrid graph convolutional network with online masked autoencoder for robust multimodal cancer survival prediction

Hou, W., Lin, C., Yu, L., Qin, J., Yu, R., & Wang, L. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3253760 · Cited by 74

Learning to Generate Scene Graph from Natural Language Supervision

Zhong, Y., Shi, J., Yang, J., Xu, C., & Li, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00184 · Cited by 73

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Wang, Y., Agarwal, S., Mukherjee, S., Liu, X., Gao, J., Awadallah, A. H., & Gao, J. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.388 · Cited by 77

Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation

Gan, Y., Yang, Z., Yue, X., Sun, L., & Yang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02069 · Cited by 72

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

Chavan, A., Shen, Z., Liu, Z., Liu, Z., Cheng, K.-T., & Xing, E. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00488 · Cited by 72

Safety of Large Language Models in Addressing Depression

Heston, T. F. (2023) · Cureus · doi:10.7759/cureus.50729 · Cited by 61

Target Adaptive Context Aggregation for Video Scene Graph Generation

Teng, Y., Wang, L., Li, Z., & Wu, G. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01343 · Cited by 74

Generative Artificial Intelligence for Software Engineering - a Research Agenda

NguyenDuc, A., Cabrero-Daniel, B., Arora, C., Przybylek, A., Khanna, D., Herda, T., Rafiq, U., Melegati, J., Guerra, E., Kemell, K.-K., et al. (2023) · doi:10.2139/ssrn.4622517 · Cited by 71

SKILL: Structured Knowledge Infusion for Large Language Models

Moiseev, F., Dong, Z., Alfonseca, E., & Jaggi, M. (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.113 · Cited by 71

Quantity doesn’t buy quality syntax with neural language models

van Schijndel, M., Mueller, A., & Linzen, T. (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/d19-1592 · Cited by 65

Automating alloy design and discovery with physics-aware multimodal multiagent AI

Ghafarollahi, A., & Buehler, M. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2414074122 · Cited by 72

Importance of Self-Attention for Sentiment Analysis

Letarte, G., Paradis, F., Giguère, P., & Laviolette, F. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5429 · Cited by 70

Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation

Pang, B., Nijkamp, E., Han, W., Zhou, L., Liu, Y., & Tu, K. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.333 · Cited by 70

VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

Tang, Y., Dong, P., Tang, Z., Chu, X., & Liang, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00575 · Cited by 70

Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes

Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105401 · Cited by 69

Learning Multi-View Interactional Skeleton Graph for Action Recognition

Wang, M., Ni, B., & Yang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032738 · Cited by 67

The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice

Shoja, M. M., Van de Ridder, J. M. M., & Rajput, V. (2023) · Cureus · doi:10.7759/cureus.40883 · Cited by 69

Large-Scale Differentially Private BERT

Anil, R., Ghazi, B., Gupta, V., Kumar, R., & Manurangsi, P. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.484 · Cited by 68

Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Lee, C., Yang, T., Chen, Z., Su, Y., Yang, Y., & Lyu, M. R. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00148 · Cited by 68

LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia

Goodman, S. M., Buehler, E., Clary, P., Coenen, A., Donsbach, A., Horne, T. N., Lahav, M., MacDonald, R., Michaels, R. B., Narayanan, A., et al. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544819 · Cited by 71

Progressive Generation of Long Text with Pretrained Language Models

Tan, B., Yang, Z., Al-Shedivat, M., Xing, E., & Hu, Z. (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.341 · Cited by 69

Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction

Amer, F., Jung, Y., & Golparvar-Fard, M. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103929 · Cited by 68

SciRepEval: A Multi-Format Benchmark for Scientific Document Representations

Singh, A., D’Arcy, M., Cohan, A., Downey, D., & Feldman, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.338 · Cited by 67

The Plastic Surgery Hypothesis in the Era of Large Language Models

Xia, C. S., Ding, Y., & Zhang, L. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00047 · Cited by 67

Adaptable butterfly accelerator for attention-based NNs via hardware and algorithm co-design

Fan, H., Chau, T., Venieris, S. I., Lee, R., Kouris, A., Luk, W., Lane, N. D., & Abdelfattah, M. S. (2022) · 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro56248.2022.00050 · Cited by 66

FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

Rotstein, N., Bensaïd, D., Brody, S., Ganz, R., & Kimmel, R. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00559 · Cited by 66

YOLOE: Real-time seeing anything

Wang, A., Liu, L., Chen, H., Lin, Z., Han, J., & Ding, G. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02280 · Cited by 65

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Hu, J., Gao, J., Fang, X., Liu, Z., Wang, F., Huang, W., Wu, H., & Zhao, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac302 · Cited by 65

Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers

Gong, Y., Khurana, S., Karlinsky, L., & Glass, J. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-2193 · Cited by 65

Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications

Szwarcman, D., Roy, S., Fraccaro, P., Gislason, T. E., Blumenstiel, B., Ghosal, R., de Oliveira, P. H., de Sousa Almeida, J. L., Sedona, R., Kang, Y., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3642610 · Cited by 100

Hardware acceleration of fully quantized bert for efficient natural language processing

Liu, Z., Li, G., & Cheng, J. (2021) · 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date51398.2021.9474043 · Cited by 64

SkCoder: A Sketch-based Approach for Automatic Code Generation

Li, J., Li, Y., Li, G., Jin, Z., Hao, Y., & Hu, X. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00179 · Cited by 64

Training-free Transformer Architecture Search

Zhou, Q., Sheng, K., Zheng, X., Li, K., Sun, X., Tian, Y., Chen, J., & Ji, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01062 · Cited by 64

Bot-Adversarial Dialogue for Safe Conversational Agents

Xu, J., Ju, D., Li, M., Boureau, Y.-L., Weston, J., & Dinan, E. (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.235 · Cited by 64

An Empirical Study of Code Smells in Transformer-based Code Generation Techniques

Siddiq, M. L., Majumder, S. H., Mim, M. R., Jajodia, S., & Santos, J. C. S. (2022) · 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) · doi:10.1109/scam55253.2022.00014 · Cited by 70

Domain-specific language models pre-trained on construction management systems corpora

Zhong, Y., & Goodfellow, S. D. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105316 · Cited by 65

DeepNet: Scaling Transformers to 1,000 Layers

Wang, H., Ma, S., Dong, L., Huang, S., Zhang, D., & Wei, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3386927 · Cited by 113

Probing Image-Language Transformers for Verb Understanding

Hendricks, L. A., & Nematzadeh, A. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.318 · Cited by 63

Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation

Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63

Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation

Guerreiro, N. M., Voita, E., & Martins, A. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.75 · Cited by 63

Conceptualizing generative AI as style engines: Application archetypes and implications

Riemer, K., & Peter, S. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102824 · Cited by 63

Transformer-Based Spatio-Temporal Unsupervised Traffic Anomaly Detection in Aerial Videos

Tran, T. M., Bui, D. C., Nguyen, T. V., & Nguyen, K. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3376399 · Cited by 63

A review of LLMs and their applications in the architecture, engineering and construction industry

Kampelopoulos, D., Tsanousa, A., Vrochidis, S., & Kompatsiaris, I. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11241-7 · Cited by 69

Assessing Phrasal Representation and Composition in Transformers

Yu, L., & Ettinger, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.397 · Cited by 58

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

Shi, D., Zhao, J., Wang, Z., Zhao, H., Eze, C., Wang, J., Lian, Y., & Burke, A. F. (2023) · Energies · doi:10.3390/en16093855 · Cited by 61

LSA-DDI: learning stereochemistry-aware drug interactions via 3D feature fusion and contrastive cross-attention

Wang, S., Yang, C., & Chen, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146799 · Cited by 65

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

McDonald, J., Li, B., Frey, N., Tiwari, D., Gadepally, V., & Samsi, S. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.151 · Cited by 62

Enhanced deepfake detection with densenet and cross-vit

Siddiqui, F., Yang, J., Xiao, S., & Fahad, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126150 · Cited by 62

Transferable deep generative modeling of intrinsically disordered protein conformations

Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62

Collaborative framework of Transformer and LSTM for enhanced state-of-charge estimation in lithium-ion batteries

Bao, G., Liu, X., Zou, B., Yang, K., Zhao, J., Zhang, L., Chen, M., Qiao, Y., Wang, W., Tan, R., et al. (2025) · Energy · doi:10.1016/j.energy.2025.135548 · Cited by 65

Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation

Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476487 · Cited by 78

Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61

Testing the Ability of Teachers and Students to Differentiate between Essays Generated by ChatGPT and High School Students

Waltzer, T., Cox, R. L., & Heyman, G. D. (2023) · Human Behavior and Emerging Technologies · doi:10.1155/2023/1923981 · Cited by 61

Significant Productivity Gains through Programming with Large Language Models

Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3661145 · Cited by 64

Towards Good Practices for Missing Modality Robust Action Recognition

Woo, S., Lee, S., Park, Y., Nugroho, M. A., & Kim, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25378 · Cited by 67

Generative Artificial Intelligence for Software Engineering—A Research Agenda

Nguyen‐Duc, A., Cabrero‐Daniel, B., Przybylek, A., Arora, C., Khanna, D., Herda, T., Rafiq, U., Melegati, J., Guerra, E., Kemell, K., et al. (2025) · Software: Practice and Experience · doi:10.1002/spe.70005 · Cited by 60

Explainability in transformer models for functional genomics

Clauwaert, J., Menschaert, G., & Waegeman, W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab060 · Cited by 60

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers

Liu, Y., Yang, H., Dong, Z., Keutzer, K., Du, L., & Zhang, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01946 · Cited by 60

LEGO-Net: Learning Regular Rearrangements of Objects in Rooms

Wei, Q. A., Ding, S., Park, J. J., Sajnani, R., Poulenard, A., Sridhar, S., & Guibas, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01825 · Cited by 60

Revisiting Parameter-Efficient Tuning: Are We Really There Yet?

Chen, G., Liu, F., Meng, Z., & Liang, S. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.168 · Cited by 60

Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments

Raychaudhuri, S., Wani, S., Patel, S., Jain, U., & Chang, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.328 · Cited by 60

A survey of transformer-based multimodal pre-trained modals

Han, X., Wang, Y.-T., Feng, J.-L., Deng, C., Chen, Z.-H., Huang, Y.-A., Su, H., Hu, L., & Hu, P.-W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2022.09.136 · Cited by 59

Transformer-Based Feature Compensation and Aggregation for DeepFake Detection

Tan, Z., Yang, Z., Miao, C., & Guo, G. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3214768 · Cited by 59

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

Kim, G., & Cho, K. (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.508 · Cited by 59

Multi-task Learning for Multilingual Neural Machine Translation

Wang, Y., Zhai, C., & Hassan, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.75 · Cited by 59

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions

Giri, N., Roy, R. S., & Cheng, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102536 · Cited by 59

Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation

Li, M., Cai, W., Verspoor, K., Pan, S., Liang, X., & Chang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02000 · Cited by 60

M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training

Ni, M., Huang, H., Su, L., Cui, E., Bharti, T., Wang, L., Zhang, D., & Duan, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00397 · Cited by 57

Behavioral Use Licensing for Responsible AI

Contractor, D., McDuff, D., Haines, J. K., Lee, J., Hines, C., Hecht, B., Vincent, N., & Li, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533143 · Cited by 54

Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning

Erdem, E., Kuyu, M., Yagcioglu, S., Frank, A., Parcalabescu, L., Plank, B., Babii, A., Turuta, O., Erdem, A., Calixto, I., et al. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12918 · Cited by 54

Extracting Symptoms and their Status from Clinical Conversations

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