Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Large Language Model guided Protocol Fuzzing

Meng, R., Mirchev, M., Böhme, M., & Roychoudhury, A. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24556 · Cited by 246

A Review of Deep Learning-Based Information Fusion Techniques for Multimodal Medical Image Classification

Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274

Deep learning for human affect recognition: Insights and new developments

Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

Fare Garnot, V. S., & Landrieu, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00483 · Cited by 242

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

Khraisha, Q., Put, S., Kappenberg, J., Warraitch, A., & Hadfield, K. (2024) · Research Synthesis Methods · doi:10.1002/jrsm.1715 · Cited by 269

Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models

Maddigan, P., & Susnjak, T. (2023) · IEEE Access · doi:10.1109/access.2023.3274199 · Cited by 242

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242

Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review

Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103671 · Cited by 235

Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image

Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., & Zhang, J. J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00013 · Cited by 238

Give us the Facts: Enhancing Large Language Models With Knowledge Graphs for Fact-Aware Language Modeling

Yang, L., Chen, H., Li, Z., Ding, X., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3360454 · Cited by 238

La nueva realidad de la educación ante los avances de la inteligencia artificial generativa

García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238

Does Audio Deepfake Detection Generalize?

Müller, N., Czempin, P., Diekmann, F., Froghyar, A., & Böttinger, K. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-108 · Cited by 258

BEVT: BERT Pretraining of Video Transformers

Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.-G., Zhou, L., & Yuan, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01432 · Cited by 234

ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning

Lai, V., Ngo, N., Pouran Ben Veyseh, A., Man, H., Dernoncourt, F., Bui, T., & Nguyen, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.878 · Cited by 233

LongT5: Efficient Text-To-Text Transformer for Long Sequences

Guo, M., Ainslie, J., Uthus, D., Ontanon, S., Ni, J., Sung, Y.-H., & Yang, Y. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.55 · Cited by 230

Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01442 · Cited by 227

HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., & Han, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.686 · Cited by 227

Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models

Liu, Z., Rodriguez-Opazo, C., Teney, D., & Gould, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00213 · Cited by 238

Deep Learning in Protein Structural Modeling and Design

Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227

Vision Transformers for Classification of Breast Ultrasound Images

Gheflati, B., & Rivaz, H. (2022) · 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871809 · Cited by 225

From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference

Samsi, S., Zhao, D., McDonald, J., Li, B., Michaleas, A., Jones, M., Bergeron, W., Kepner, J., Tiwari, D., & Gadepally, V. (2023) · 2023 IEEE High Performance Extreme Computing Conference (HPEC) · doi:10.1109/hpec58863.2023.10363447 · Cited by 225

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C.-K., Li, X., & Guan, C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3308189 · Cited by 224

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks

Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1259 · Cited by 222

Review of multimodal machine learning approaches in healthcare

Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235

GeDi: Generative Discriminator Guided Sequence Generation

Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.424 · Cited by 220

Navigating the Complexity of Generative AI Adoption in Software Engineering

Russo, D. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3652154 · Cited by 217

Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions

Rahate, A., Walambe, R., Ramanna, S., & Kotecha, K. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.12.003 · Cited by 214

Multiple Object Tracking with Correlation Learning

Wang, Q., Zheng, Y., Pan, P., & Xu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00387 · Cited by 215

Battery Safety: Machine Learning-Based Prognostics

Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216

Vision Transformers Are Robust Learners

Paul, S., & Chen, P.-Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20103 · Cited by 244

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611477 · Cited by 210

Can GPT-3 pass a writer’s turing test?

Elkins, K., & Chun, J. (2020) · Journal of Cultural Analytics · doi:10.22148/001c.17212 · Cited by 202

Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation

Li, M., Lin, B., Chen, Z., Lin, H., Liang, X., & Chang, X. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00325 · Cited by 206

GSum: A General Framework for Guided Neural Abstractive Summarization

Dou, Z.-Y., Liu, P., Hayashi, H., Jiang, Z., & Neubig, G. (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.384 · Cited by 210

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204

EPlus-LLM: A large language model-based computing platform for automated building energy modeling

Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123431 · Cited by 204

Beyond semantic distance: Automated scoring of divergent thinking greatly improves with large language models

Organisciak, P., Acar, S., Dumas, D., & Berthiaume, K. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101356 · Cited by 202

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Quilty-Dunn, J., Porot, N., & Mandelbaum, E. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002849 · Cited by 205

Dense Convolutional Network and Its Application in Medical Image Analysis

Zhou, T., Ye, X., Lu, H., Zheng, X., Qiu, S., & Liu, Y. (2022) · BioMed Research International · doi:10.1155/2022/2384830 · Cited by 201

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1613 · Cited by 197

A review of some techniques for inclusion of domain-knowledge into deep neural networks

Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022) · Scientific Reports · doi:10.1038/s41598-021-04590-0 · Cited by 196

Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196

Generative AI in Higher Education: The ChatGPT Effect

Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203

Unleashing the power of generative AI in drug discovery

Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195

Compositionality Decomposed: How do Neural Networks Generalise?

Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11674 · Cited by 194

Multi-Modal Graph Learning for Disease Prediction

Zheng, S., Zhu, Z., Liu, Z., Guo, Z., Liu, Y., Yang, Y., & Zhao, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3159264 · Cited by 196

ELSA: Hardware-Software co-design for efficient, lightweight self-attention mechanism in neural networks

Ham, T. J., Lee, Y., Seo, S. H., Kim, S., Choi, H., Jung, S. J., & Lee, J. W. (2021) · 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca52012.2021.00060 · Cited by 192

Diffusion-based Generation, Optimization, and Planning in 3D Scenes

Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., & Zhu, S.-C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01607 · Cited by 192

Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models

Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., & Rush, A. M. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209479 · Cited by 204

The clinicians’ guide to large language models: a general perspective with a focus on hallucinations

Roustan, D., & Bastardot, F. (2025) · Interactive Journal of Medical Research · doi:10.2196/59823 · Cited by 191

Privacy Risks of General-Purpose Language Models

Pan, X., Zhang, M., Ji, S., & Yang, M. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00095 · Cited by 188

AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake

Yang, W., Zhou, X., Chen, Z., Guo, B., Ba, Z., Xia, Z., Cao, X., & Ren, K. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3262148 · Cited by 198

ScanQA: 3D Question Answering for Spatial Scene Understanding

Azuma, D., Miyanishi, T., Kurita, S., & Kawanabe, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01854 · Cited by 197

Knowledge Neurons in Pretrained Transformers

Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.581 · Cited by 187

TweepFake: about detecting deepfake tweets

Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251415 · Cited by 185

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188

CoLAKE: Contextualized Language and Knowledge Embedding

Sun, T., Shao, Y., Qiu, X., Guo, Q., Hu, Y., Huang, X., & Zhang, Z. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.327 · Cited by 185

Are Multimodal Transformers Robust to Missing Modality?

Ma, M., Ren, J., Zhao, L., Testuggine, D., & Peng, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01764 · Cited by 198

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks

Fang, K., Toshev, A., Fei-Fei, L., & Savarese, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00063 · Cited by 178

Personalizing Dialogue Agents via Meta-Learning

Madotto, A., Lin, Z., Wu, C.-S., & Fung, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1542 · Cited by 183

METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01112 · Cited by 184

V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

Yu, H., Yang, W., Ruan, H., Yang, Z., Tang, Y., Gao, X., Hao, X., Shi, Y., Pan, Y., Sun, N., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00531 · Cited by 182

The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence

Gebru, T., & Torres, É. P. (2024) · First Monday · doi:10.5210/fm.v29i4.13636 · Cited by 181

Protecting Celebrities from DeepFake with Identity Consistency Transformer

Dong, X., Bao, J., Chen, D., Zhang, T., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00925 · Cited by 181

Opportunities and challenges of text mining in materials research

Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182

Code Prediction by Feeding Trees to Transformers

Kim, S., Zhao, J., Tian, Y., & Chandra, S. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) · doi:10.1109/icse43902.2021.00026 · Cited by 209

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Zhong, M., Yin, D., Yu, T., Zaidi, A., Mutuma, M., Jha, R., Awadallah, A. H., Celikyilmaz, A., Liu, Y., Qiu, X., et al. (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.472 · Cited by 178

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178

RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data

Zhan, Y., Xiong, Z., & Yuan, Y. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3250471 · Cited by 179

Untitled work

(2022) · doi:10.18653/v1/2022.emnlp-main.669 · Cited by 177

Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs

Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., & Kao, O. (2020) · 2020 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm50108.2020.00148 · Cited by 176

Replay and Synthetic Speech Detection with Res2Net Architecture

Li, X., Li, N., Weng, C., Liu, X., Su, D., Yu, D., & Meng, H. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413828 · Cited by 186

Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

Mai, S., Zeng, Y., & Hu, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3171679 · Cited by 182

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Hasan, M. K., Rahman, W., Bagher Zadeh, A., Zhong, J., Tanveer, M. I., Morency, L.-P., & Hoque, M. (. (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/d19-1211 · Cited by 174

Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods

Cao, Y., Zhao, H., Cheng, Y., Shu, T., Chen, Y., Liu, G., Liang, G., Zhao, J., Yan, J., & Li, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3497992 · Cited by 174

A comparative atlas of single-cell chromatin accessibility in the human brain

Li, Y. E., Preissl, S., Miller, M., Johnson, N. D., Wang, Z., Jiao, H., Zhu, C., Wang, Z., Xie, Y., Poirion, O., et al. (2023) · Science · doi:10.1126/science.adf7044 · Cited by 173

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

Lei, J., Wang, L., Shen, Y., Yu, D., Berg, T., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.233 · Cited by 179

F 2 Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection

Miao, C., Tan, Z., Chu, Q., Liu, H., Hu, H., & Yu, N. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3233774 · Cited by 172

AI chatbots not yet ready for clinical use.

Au Yeung, J., Kraljevic, Z., Luintel, A., Balston, A., Idowu, E., Dobson, R. J., & Teo, J. T. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1161098 · Cited by 176

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.462 · Cited by 167

A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2907718 · Cited by 167

Augment your batch: Improving generalization through instance repetition

Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., & Soudry, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00815 · Cited by 166

Multimodal Attention-Based Deep Learning for Alzheimer’s Disease Diagnosis

Golovanevsky, M., Eickhoff, C., & Singh, R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac168 · Cited by 165

Theoretical Limitations of Self-Attention in Neural Sequence Models

Hahn, M. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00306 · Cited by 165

The emergence of economic rationality of GPT

Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316205120 · Cited by 164

Zero-Shot Multi-Speaker Text-To-Speech with State-Of-The-Art Neural Speaker Embeddings

Cooper, E., Lai, C.-I., Yasuda, Y., Fang, F., Wang, X., Chen, N., & Yamagishi, J. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054535 · Cited by 164

Lite Vision Transformer with Enhanced Self-Attention

Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z., & Yuille, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01169 · Cited by 164

Human-like problem-solving abilities in large language models using ChatGPT

Orrù, G., Piarulli, A., Conversano, C., & Gemignani, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1199350 · Cited by 170

Using artificial intelligence to document the hidden RNA virosphere

Hou, X., He, Y., Fang, P., Mei, S.-Q., Xu, Z., Wu, W.-C., Tian, J.-H., Zhang, S., Zeng, Z.-Y., Gou, Q.-Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.027 · Cited by 162

A survey on aspect-based sentiment classification

Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165

Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3260040 · Cited by 161

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3182537 · Cited by 161

Limitations of transformers on clinical text classification

Gao, S., Alawad, M., Young, M. T., Gounley, J., Schaefferkoetter, N., Yoon, H. J., Wu, X.-C., Durbin, E. B., Doherty, J., Stroup, A., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3062322 · Cited by 157

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

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