Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Skeleton-based action recognition via spatial and temporal transformer networks

Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409

LiT: Zero-Shot Transfer with Locked-image text Tuning

Zhai, X., Wang, X., Mustafa, B., Steiner, A., Keysers, D., Kolesnikov, A., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01759 · Cited by 402

Massively Multilingual Neural Machine Translation

Aharoni, R., Johnson, M., & Firat, O. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1388 · Cited by 402

Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement

Lee, J., Mansimov, E., & Cho, K. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1149 · Cited by 386

HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.161 · Cited by 408

How to build the virtual cell with artificial intelligence: Priorities and opportunities

Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393

Graph-to-Sequence Learning using Gated Graph Neural Networks

Beck, D., Haffari, G., & Cohn, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1026 · Cited by 380

RWKV: Reinventing RNNs for the Transformer Era

Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Derczynski, L., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.936 · Cited by 390

A review of deep learning techniques for speech processing

Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389

Few-Shot Incremental Learning with Continually Evolved Classifiers

Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., & Xu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01227 · Cited by 382

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382

Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention

Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., & Singh, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17664 · Cited by 376

Class-Incremental Learning: A Survey

Zhou, D.-W., Wang, Q.-W., Qi, Z.-H., Ye, H.-J., Zhan, D.-C., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3429383 · Cited by 376

From Show to Tell: A Survey on Deep Learning-Based Image Captioning

Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

Chen, R. J., Lu, M. Y., Weng, W.-H., Chen, T. Y., Williamson, D. F., Manz, T., Shady, M., & Mahmood, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00398 · Cited by 398

Large language models in health care: Development, applications, and challenges

Yang, R., Tan, T. F., Lu, W., Thirunavukarasu, A. J., Ting, D. S. W., & Liu, N. (2023) · Health Care Science · doi:10.1002/hcs2.61 · Cited by 366

ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions

Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., et al. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100027 · Cited by 369

ChatGPT in healthcare: A taxonomy and systematic review

Li, J., Dada, A., Puladi, B., Kleesiek, J., & Egger, J. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108013 · Cited by 452

DocFormer: End-to-End Transformer for Document Understanding

Appalaraju, S., Jasani, B., Kota, B. U., Xie, Y., & Manmatha, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00103 · Cited by 363

MLS: A Large-Scale Multilingual Dataset for Speech Research

Pratap, V., Xu, Q., Sriram, A., Synnaeve, G., & Collobert, R. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2826 · Cited by 361

A-ViT: Adaptive Tokens for Efficient Vision Transformer

Yin, H., Vahdat, A., Alvarez, J. M., Mallya, A., Kautz, J., & Molchanov, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01054 · Cited by 360

Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad493 · Cited by 359

All in One Bad Weather Removal Using Architectural Search

Li, R., Tan, R. T., & Cheong, L.-F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00324 · Cited by 377

Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

Alhichri, H., Alswayed, A. S., Bazi, Y., Ammour, N., & Alajlan, N. A. (2021) · IEEE Access · doi:10.1109/access.2021.3051085 · Cited by 357

Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

Meng, H., Yan, T., Yuan, F., & Wei, H. (2019) · IEEE Access · doi:10.1109/access.2019.2938007 · Cited by 356

Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming

Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580919 · Cited by 366

SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Gliwa, B., Mochol, I., Biesek, M., & Wawer, A. (2019) · Proceedings of the 2nd Workshop on New Frontiers in Summarization · doi:10.18653/v1/d19-5409 · Cited by 424

Compute Trends Across Three Eras of Machine Learning

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363

Dissociating language and thought in large language models

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345

Template-Based Named Entity Recognition Using BART

Cui, L., Wu, Y., Liu, J., Yang, S., & Zhang, Y. (2021) · arXiv · doi:10.48550/arxiv.2106.01760 · Cited by 342

Human heuristics for AI-generated language are flawed

Jakesch, M., Hancock, J. T., & Naaman, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2208839120 · Cited by 342

Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

Shao, Z., Zhang, Z., Wang, F., & Xu, Y. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539396 · Cited by 338

An Analysis of the Automatic Bug Fixing Performance of ChatGPT

Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023) · 2023 IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1109/apr59189.2023.00012 · Cited by 338

WhisperX: Time-Accurate Speech Transcription of Long-Form Audio

Bain, M., Huh, J., Han, T., & Zisserman, A. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-78 · Cited by 337

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335

Cross-Layer Distillation with Semantic Calibration

Chen, D., Mei, J.-P., Zhang, Y., Wang, C., Wang, Z., Feng, Y., & Chen, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16865 · Cited by 335

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F. X., Taylor, R. H., & Unberath, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00614 · Cited by 335

Targeted Syntactic Evaluation of Language Models

Marvin, R., & Linzen, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1151 · Cited by 334

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Wen, B., Yang, W., Kautz, J., & Birchfield, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01692 · Cited by 353

Pixel-Adaptive Convolutional Neural Networks

Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., & Kautz, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01142 · Cited by 348

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.3004294 · Cited by 337

Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Shen, D., Wang, G., Wang, W., Min, M. R., Su, Q., Zhang, Y., Li, C., Henao, R., & Carin, L. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1041 · Cited by 328

SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network

Li, M., Hsu, W., Xie, X., Cong, J., & Gao, W. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2968472 · Cited by 321

Online Knowledge Distillation with Diverse Peers

Chen, D., Mei, J.-P., Wang, C., Feng, Y., & Chen, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5746 · Cited by 324

AutoFormer: Searching Transformers for Visual Recognition

Chen, M., Peng, H., Fu, J., & Ling, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01205 · Cited by 323

EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction

Cai, H., Li, J., Hu, M., Gan, C., & Han, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01587 · Cited by 356

Dual-level Collaborative Transformer for Image Captioning

Luo, Y., Ji, J., Sun, X., Cao, L., Wu, Y., Huang, F., Lin, C.-W., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16328 · Cited by 343

A Survey on Multimodal Large Language Models for Autonomous Driving

Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369

REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & van den Hengel, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01000 · Cited by 331

ELI5: long form question answering

Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1346 · Cited by 319

Ethical challenges and solutions of generative AI: an interdisciplinary perspective

Al-kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., & Alfandi, O. (2024) · Informatics · doi:10.3390/informatics11030058 · Cited by 328

Generating Human Motion from Textual Descriptions with Discrete Representations

Zhang, J., Zhang, Y., Cun, X., Zhang, Y., Zhao, H., Lu, H., Shen, X., & Ying, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01415 · Cited by 328

Query-Centric Trajectory Prediction

Zhou, Z., Wang, J., Li, Y., & Huang, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01713 · Cited by 333

Pre-training of Graph Augmented Transformers for Medication Recommendation

Shang, J., Ma, T., Xiao, C., & Sun, J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/825 · Cited by 311

ChatGPT: Fundamentals, Applications and Social Impacts

Abdullah, M., Madain, A., & Jararweh, Y. (2022) · 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams58071.2022.10062688 · Cited by 310

SegGPT: Towards Segmenting Everything In Context

Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., & Huang, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00110 · Cited by 310

Deduplicating Training Data Makes Language Models Better

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.577 · Cited by 304

Empowering biomedical discovery with AI agents

Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315

Exploring Temporal Coherence for More General Video Face Forgery Detection

Zheng, Y., Bao, J., Chen, D., Zeng, M., & Wen, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01477 · Cited by 320

Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions

Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100199 · Cited by 311

A Unified Generative Framework for Various NER Subtasks

Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, 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.451 · Cited by 299

Can Generative AI improve social science?

Bail, C. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314021121 · Cited by 369

Can large language models reason about medical questions?

Liévin, V., Hother, C. E., Motzfeldt, A. G., & Winther, O. (2024) · Patterns · doi:10.1016/j.patter.2024.100943 · Cited by 343

DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

Aminabadi, R. Y., Rajbhandari, S., Awan, A. A., Li, C., Li, D., Zheng, E., Ruwase, O., Smith, S., Zhang, M., Rasley, J., et al. (2022) · SC22: International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1109/sc41404.2022.00051 · Cited by 297

Pose Recognition with Cascade Transformers

Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., & Tu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00198 · Cited by 301

Multiview Transformers for Video Recognition

Yan, S., Xiong, X., Arnab, A., Lu, Z., Zhang, M., Sun, C., & Schmid, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00333 · Cited by 312

Generative AI for Software Practitioners

Ebert, C., & Louridas, P. (2023) · IEEE Software · doi:10.1109/ms.2023.3265877 · Cited by 293

Vision-Language Pre-Training with Triple Contrastive Learning

Yang, J., Duan, J., Tran, S., Xu, Y., Chanda, S., Chen, L., Zeng, B., Chilimbi, T., & Huang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01522 · Cited by 309

Splitwise: Efficient Generative LLM Inference Using Phase Splitting

Patel, P., Choukse, E., Zhang, C., Shah, A., Goiri, Í., Maleki, S., & Bianchini, R. (2024) · 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca59077.2024.00019 · Cited by 298

Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection

Wang, S., Liu, Y., Wang, T., Li, Y., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00335 · Cited by 292

Prediction of chemical reaction yields using deep learning

Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J.-L. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc81d · Cited by 291

Visual Commonsense R-CNN

Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01077 · Cited by 291

Unsupervised Domain Adaptation of Object Detectors: A Survey

Oza, P., Sindagi, V. A., VS, V., & Patel, V. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217046 · Cited by 313

Log-based Anomaly Detection Without Log Parsing

Le, V.-H., & Zhang, H. (2021) · 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678773 · Cited by 361

UniT: Multimodal Multitask Learning with a Unified Transformer

Hu, R., & Singh, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00147 · Cited by 303

Ssast: Self-supervised audio spectrogram transformer

Gong, Y., Lai, C.-I., Chung, Y.-A., & Glass, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21315 · Cited by 294

w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

Chung, Y.-A., Zhang, Y., Han, W., Chiu, C.-C., Qin, J., Pang, R., & Wu, Y. (2021) · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru51503.2021.9688253 · Cited by 293

Semantic segmentation using Vision Transformers: A survey

Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288

An Analysis of Encoder Representations in Transformer-Based Machine Translation

Raganato, A., & Tiedemann, J. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5431 · Cited by 277

Foundation Models Defining a New Era in Vision: A Survey and Outlook

Awais, M., Naseer, M., Khan, S., Anwer, R. M., Cholakkal, H., Shah, M., Yang, M.-H., & Khan, F. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3506283 · Cited by 304

Controllable Abstractive Summarization

Fan, A., Grangier, D., & Auli, M. (2018) · Proceedings of the 2nd Workshop on Neural Machine Translation and Generation · doi:10.18653/v1/w18-2706 · Cited by 281

Impact of word embedding models on text analytics in deep learning environment: a review

Asudani, D. S., Nagwani, N. K., & Singh, P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10419-1 · Cited by 274

The potential of generative AI for personalized persuasion at scale

Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-53755-0 · Cited by 315

HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection

Chen, K., Du, X., Zhu, B., Ma, Z., Berg-Kirkpatrick, T., & Dubnov, S. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746312 · Cited by 294

Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2023.100857 · Cited by 269

ChatGPT and Software Testing Education: Promises & Perils

Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023) · 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw58534.2023.00078 · Cited by 270

Spatio-Temporal Graph for Video Captioning With Knowledge Distillation

Pan, B., Cai, H., Huang, D.-A., Lee, K.-H., Gaidon, A., Adeli, E., & Niebles, J. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01088 · Cited by 281

Single cortical neurons as deep artificial neural networks

Beniaguev, D., Segev, I., & London, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.002 · Cited by 254

The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291

A review of large language models and autonomous agents in chemistry

Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274

Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

Garnelo, M., & Shanahan, M. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.12.010 · Cited by 255

A Survey of Knowledge-enhanced Text Generation

Yu, W., Zhu, C., Li, Z., Hu, Z., Wang, Q., Ji, H., & Jiang, M. (2022) · ACM Computing Surveys · doi:10.1145/3512467 · Cited by 257

Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

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SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery

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