Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond

Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839

ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

Touvron, H., Bojanowski, P., Caron, M., Cord, M., El-Nouby, A., Grave, E., Izacard, G., Joulin, A., Synnaeve, G., Verbeek, J., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3206148 · Cited by 807

A survey of human-in-the-loop for machine learning

Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.014 · Cited by 800

Deep Audio-Visual Speech Recognition

Afouras, T., Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889052 · Cited by 801

K-BERT: Enabling Language Representation with Knowledge Graph

Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5681 · Cited by 894

ConViT: improving vision transformers with soft convolutional inductive biases

d’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., & Sagun, L. (2022) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/ac9830 · Cited by 782

Artificial intelligence for multimodal data integration in oncology

Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777

Energy and Policy Considerations for Modern Deep Learning Research

Strubell, E., Ganesh, A., & McCallum, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i09.7123 · Cited by 774

Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things

Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774

Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training

Li, G., Duan, N., Fang, Y., Gong, M., & Jiang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6795 · Cited by 795

Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

Yu, W., Xu, H., Yuan, Z., & Wu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17289 · Cited by 816

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., & Zhang, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11941 · Cited by 779

ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding

Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6428 · Cited by 766

PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

Xu, J., Xiong, Z., & Bhattacharyya, S. P. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01871 · Cited by 817

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben Zaken, E., Goldberg, Y., & Ravfogel, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.1 · Cited by 765

Neural Speech Synthesis with Transformer Network

Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754

Mobile-Former: Bridging MobileNet and Transformer

Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00520 · Cited by 731

A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023) · ACM Computing Surveys · doi:10.1145/3582688 · Cited by 721

End-to-End Human Pose and Mesh Reconstruction with Transformers

Lin, K., Wang, L., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00199 · Cited by 740

CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers

Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731

X-Linear Attention Networks for Image Captioning

Pan, Y., Yao, T., Li, Y., & Mei, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01098 · Cited by 720

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (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/d19-1005 · Cited by 661

Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., & Kreis, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02161 · Cited by 724

Towards Large-Scale Small Object Detection: Survey and Benchmarks

Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., & Han, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3290594 · Cited by 683

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., & Rizzo, C. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1166120 · Cited by 708

Vector Quantized Diffusion Model for Text-to-Image Synthesis

Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01043 · Cited by 720

Multi-Concept Customization of Text-to-Image Diffusion

Kumari, N., Zhang, B., Zhang, R., Shechtman, E., & Zhu, J.-Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00192 · Cited by 674

Learning Deep Transformer Models for Machine Translation

Wang, Q., Li, B., Xiao, T., Zhu, J., Li, C., Wong, D. F., & Chao, L. S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1176 · Cited by 673

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, 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.499 · Cited by 673

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Gudovskiy, D., Ishizaka, S., & Kozuka, K. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00188 · Cited by 669

An Empirical Study of Spatial Attention Mechanisms in Deep Networks

Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00679 · Cited by 659

Large language models for software engineering: survey and open problems

Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650

Unified Pre-training for Program Understanding and Generation

Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (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.211 · Cited by 642

Generating Radiology Reports via Memory-driven Transformer

Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.112 · Cited by 693

Time-Series Representation Learning via Temporal and Contextual Contrasting

Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., & Guan, C. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/324 · Cited by 696

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Zhao, T., Kumar, V., Levine, S., & Finn, C. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.016 · Cited by 674

Incorporating Convolution Designs into Visual Transformers

Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., & Wu, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00062 · Cited by 611

Generalizing Face Forgery Detection with High-frequency Features

Luo, Y., Zhang, Y., Yan, J., & Liu, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01605 · Cited by 609

Machine learning and AI in marketing–Connecting computing power to human insights

Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.

Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597

Masked Feature Prediction for Self-Supervised Visual Pre-Training

Wei, C., Fan, H., Xie, S., Wu, C.-Y., Yuille, A., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01426 · Cited by 587

End-to-End Dense Video Captioning with Masked Transformer

Zhou, L., Zhou, Y., Corso, J. J., Socher, R., & Xiong, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00911 · Cited by 605

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01567 · Cited by 621

Retrieval Augmentation Reduces Hallucination in Conversation

Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.320 · Cited by 575

DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

Kim, G., Kwon, T., & Ye, J. C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00246 · Cited by 570

ChatGPT for Robotics: Design Principles and Model Abilities

Vemprala, S. H., Bonatti, R., Bucker, A., & Kapoor, A. (2024) · IEEE Access · doi:10.1109/access.2024.3387941 · Cited by 570

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567

ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., & Yu, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512217 · Cited by 599

What Makes Good In-Context Examples for GPT-3?

Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022) · Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures · doi:10.18653/v1/2022.deelio-1.10 · Cited by 563

On the Sentence Embeddings from Pre-trained Language Models

Li, B., Zhou, H., He, J., Wang, M., Yang, Y., & Li, L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.733 · Cited by 593

DeepInf: Social Influence Prediction with Deep Learning

Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., & Tang, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220077 · Cited by 587

XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y., Pino, J., et al. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-143 · Cited by 586

Machine Learning for industrial applications: A comprehensive literature review

Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114820 · Cited by 553

Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

Wang, X., Huang, Q., Celikyilmaz, A., Gao, J., Shen, D., Wang, Y.-F., Wang, W. Y., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00679 · Cited by 522

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication

Xu, R., Xiang, H., Xia, X., Han, X., Li, J., & Ma, J. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9812038 · Cited by 586

Deep Multimodal Representation Learning: A Survey

Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535

AI Choreographer: Music Conditioned 3D Dance Generation with AIST++

Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553

Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks

Wang, W., Bao, H., Dong, L., Bjorck, J., Peng, Z., Liu, Q., Aggarwal, K., Mohammed, O. K., Singhal, S., Som, S., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01838 · Cited by 561

VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y., Wang, Y., Wang, Y., & Qiao, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01398 · Cited by 536

Language-agnostic BERT Sentence Embedding

Feng, F., Yang, Y., Cer, D., Arivazhagan, N., & Wang, W. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.62 · Cited by 758

TransFG: A Transformer Architecture for Fine-Grained Recognition

He, J., Chen, J.-N., Liu, S., Kortylewski, A., Yang, C., Bai, Y., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19967 · Cited by 551

Quantum advantage in learning from experiments

Huang, H.-Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., Neven, H., Babbush, R., Kueng, R., Preskill, J., et al. (2022) · Science · doi:10.1126/science.abn7293 · Cited by 519

Proceedings of the ACM Web Conference 2022

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Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29720 · Cited by 556

Using cognitive psychology to understand GPT-3

Binz, M., & Schulz, E. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218523120 · Cited by 559

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., & Song, S. (2023) · Robotics: Science and Systems XIX · doi:10.15607/rss.2023.xix.026 · Cited by 530

Multimodal intelligence: Representation learning, information fusion, and applications

Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511

Global Second-Order Pooling Convolutional Networks

Gao, Z., Xie, J., Wang, Q., & Li, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00314 · Cited by 524

The impact of ChatGPT on higher education

Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1206936 · Cited by 519

Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine

Harrer, S. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104512 · Cited by 480

ProGen2: Exploring the boundaries of protein language models

Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.605132 · Cited by 468

Computational bioacoustics with deep learning: a review and roadmap

Stowell, D. (2022) · PeerJ · doi:10.7717/peerj.13152 · Cited by 471

A Literature Survey of Recent Advances in Chatbots

Caldarini, G., Jaf, S., & McGarry, K. (2022) · Information · doi:10.3390/info13010041 · Cited by 470

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

Song, H., Rajan, D., Thiagarajan, J., & Spanias, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11635 · Cited by 468

Spatial-Spectral Transformer for Hyperspectral Image Classification

He, X., Chen, Y., & Lin, Z. (2021) · Remote Sensing · doi:10.3390/rs13030498 · Cited by 462

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Yin, P., Neubig, G., Yih, W.-T., & Riedel, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.745 · Cited by 487

Deep multimodal data fusion

Zhao, F., Zhang, C., & Geng, B. (2024) · ACM Computing Surveys · doi:10.1145/3649447 · Cited by 480

Detecting Deepfakes with Self-Blended Images

Shiohara, K., & Yamasaki, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01816 · Cited by 503

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., & Ballas, N. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01499 · Cited by 456

Autoregressive Image Generation using Residual Quantization

Lee, D., Kim, C., Kim, S., Cho, M., & Han, W.-S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01123 · Cited by 445

Security and Privacy Challenges of Large Language Models: A Survey

Das, B. C., Amini, M. H., & Wu, Y. (2025) · ACM Computing Surveys · doi:10.1145/3712001 · Cited by 445

MTEB: Massive Text Embedding Benchmark

Muennighoff, N., Tazi, N., Magne, L., & Reimers, N. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.148 · Cited by 548

No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465

AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise

Wyatt, J., Leach, A., Schmon, S. M., & Willcocks, C. G. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00080 · Cited by 442

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1179 · Cited by 434

Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions

Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833571 · Cited by 476

Automated Program Repair in the Era of Large Pre-trained Language Models

Xia, C. S., Wei, Y., & Zhang, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00129 · Cited by 433

Self-Supervised Representation Learning: Introduction, advances, and challenges

Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3134634 · Cited by 433

Audiolm: A language modeling approach to audio generation

Borsos, Z., Marinier, R., Vincent, D., Kharitonov, E., Pietquin, O., Sharifi, M., Roblek, D., Teboul, O., Grangier, D., Tagliasacchi, M., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3288409 · Cited by 432

Dynamic DETR: End-to-End Object Detection with Dynamic Attention

Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., & Zhang, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00298 · Cited by 456

SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning

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