Exploiting Generative AI to Scale up Intelligent Tutoring Systems

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation

Kim, N., & Linzen, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.731 · Cited by 153

Generative Pre-Training for Speech with Autoregressive Predictive Coding

Chung, Y.-A., & Glass, J. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054438 · Cited by 157

Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer

Lu, S., Wang, M., Liang, S., Lin, J., & Wang, Z. (2020) · 2020 IEEE 33rd International System-on-Chip Conference (SOCC) · doi:10.1109/socc49529.2020.9524802 · Cited by 154

exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models

Hoover, B., Strobelt, H., & Gehrmann, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.22 · Cited by 151

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

Yang, A., Wang, Q., Liu, J., Liu, K., Lyu, Y., Wu, H., She, Q., & Li, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1226 · Cited by 155

Multimodal Transformer for Multimodal Machine Translation

Yao, S., & Wan, X. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.400 · Cited by 152

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

Wang, Y., Shen, X., Hu, S. X., Yuan, Y., Crowley, J. L., & Vaufreydaz, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01414 · Cited by 155

Reasoning Over Semantic-Level Graph for Fact Checking

Zhong, W., Xu, J., Tang, D., Xu, Z., Duan, N., Zhou, M., Wang, J., & Yin, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.549 · Cited by 151

WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research

Mei, X., Meng, C., Liu, H., Kong, Q., Ko, T., Zhao, C., Plumbley, M. D., Zou, Y., & Wang, W. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2024.3419446 · Cited by 150

Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers

Dai, D., Sun, Y., Dong, L., Hao, Y., Ma, S., Sui, Z., & Wei, F. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.247 · Cited by 154

Machine learning for perturbational single-cell omics

Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137

Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models

Ahmed, T., Ghosh, S., Bansal, C., Zimmermann, T., Zhang, X., & Rajmohan, S. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00149 · Cited by 148

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148

Putting the Object Back into Video Object Segmentation

Cheng, H. K., Oh, S. W., Price, B., Lee, J.-Y., & Schwing, A. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00304 · Cited by 148

Neural Machine Translation with Byte-Level Subwords

Wang, C., Cho, K., & Gu, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6451 · Cited by 147

ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry literature search

McGowan, A., Gui, Y., Dobbs, M., Shuster, S., Cotter, M., Selloni, A., Goodman, M., Srivastava, A., Cecchi, G. A., & Corcoran, C. M. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115334 · Cited by 140

Efficient Attentions for Long Document Summarization

Huang, L., Cao, S., Parulian, N., Ji, H., & Wang, L. (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.112 · Cited by 146

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.676 · Cited by 146

Notice of Removal: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., & Tan, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00984 · Cited by 144

Can language models learn from explanations in context?

Lampinen, A., Dasgupta, I., Chan, S., Mathewson, K., Tessler, M., Creswell, A., McClelland, J., Wang, J., & Hill, F. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.38 · Cited by 142

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

Sajjadi, M. S. M., Meyer, H., Pot, E., Bergmann, U., Greff, K., Radwan, N., Vora, S., Lucic, M., Duckworth, D., Dosovitskiy, A., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00613 · Cited by 143

Recent advances in biomedical literature mining

Zhao, S., Su, C., Lu, Z., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa057 · Cited by 134

Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses

Savelka, J., Agarwal, A., An, M., Bogart, C., & Sakr, M. (2023) · Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 · doi:10.1145/3568813.3600142 · Cited by 141

Saint+: Integrating temporal features for ednet correctness prediction

Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448188 · Cited by 141

Generative AI and jobs

Gmyrek, P., Berg, J., Bescond, D., & International Labour Organization. Research Department (2023) · doi:10.54394/fhem8239 · Cited by 140

Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data

Lee, C., Yang, T., Chen, Z., Su, Y., & Lyu, M. R. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00150 · Cited by 149

Multimodal Prompting with Missing Modalities for Visual Recognition

Lee, Y.-L., Tsai, Y.-H., Chiu, W.-C., & Lee, C.-Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01435 · Cited by 156

Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

(2022) · doi:10.1016/j.str.2022.05.001 · Cited by 139

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Li, Z., Zhu, H., Lu, Z., & Yin, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.647 · Cited by 139

Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (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.416 · Cited by 149

Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles

Cui, J., Qiu, H., Chen, D., Stone, P., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01674 · Cited by 138

ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design

You, H., Sun, Z., Shi, H., Yu, Z., Zhao, Y., Zhang, Y., Li, C., Li, B., & Lin, Y. (2023) · 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10071027 · Cited by 138

Airbert: In-domain Pretraining for Vision-and-Language Navigation

Guhur, P.-L., Tapaswi, M., Chen, S., Laptev, I., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00166 · Cited by 139

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (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.365 · Cited by 138

Structured Pruning Learns Compact and Accurate Models

Xia, M., Zhong, Z., & Chen, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.107 · Cited by 136

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks

Yang, X., Feng, S., Zhang, Y., & Wang, D. (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.28 · Cited by 145

Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals

Fan, H., Zhang, X., Xu, Y., Fang, J., Zhang, S., Zhao, X., & Yu, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102161 · Cited by 137

A Systematic Assessment of Syntactic Generalization in Neural Language Models

Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.158 · Cited by 134

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

Wang, Y., Xu, Y., Yang, J., Wu, M., Li, X., Xie, L., & Chen, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29500 · Cited by 134

Siamese transformer network for hyperspectral image target detection

Rao, W., Gao, L., Qu, Y., Sun, X., Zhang, B., & Chanussot, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3163173 · Cited by 134

Automatic Floor Plan Analysis and Recognition

Pizarro, P. N., Hitschfeld, N., Sipiran, I., & Saavedra, J. M. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104348 · Cited by 132

M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer

Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Zhang, D., Zhong, C., Xu, P., & Tian, Y. (2022) · Machines · doi:10.3390/machines10100912 · Cited by 131

Voice Conversion Challenge 2020 –- Intra-lingual semi-parallel and cross-lingual voice conversion –-

Yi, Z., Huang, W.-C., Tian, X., Yamagishi, J., Das, R. K., Kinnunen, T., Ling, Z.-H., & Toda, T. (2020) · Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020 · doi:10.21437/vcc_bc.2020-14 · Cited by 126

A foundation model of transcription across human cell types

(2025) · doi:10.1038/s41586-024-08391-z · Cited by 130

KAT: A Knowledge Augmented Transformer for Vision-and-Language

Gui, L., Wang, B., Huang, Q., Hauptmann, A., Bisk, Y., & Gao, J. (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.70 · Cited by 130

A Lightweight IoT Intrusion Detection Model Based on Improved BERT-of-Theseus

Wang, Z., Li, J., Yang, S., Luo, X., Li, D., & Mahmoodi, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122045 · Cited by 131

Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications

Xiao, C., Xu, S. X., Zhang, K., Wang, Y., & Xia, L. (2023) · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) · doi:10.18653/v1/2023.bea-1.52 · Cited by 138

Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?

Zhang, T., Xu, B., Thung, F., Haryono, S. A., Lo, D., & Jiang, L. (2020) · 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsme46990.2020.00017 · Cited by 129

Factual Error Correction for Abstractive Summarization Models

Cao, M., Dong, Y., Wu, J., & Cheung, J. C. K. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.506 · Cited by 132

Human and artificial cognition

Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., & de Laat, M. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100107 · Cited by 133

No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT

Liu, Z., Tang, Y., Luo, X., Zhou, Y., & Zhang, L. F. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3392499 · Cited by 128

HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

Dong, P., Sun, M., Lu, A., Xie, Y., Liu, K., Kong, Z., Meng, X., Li, Z., Lin, X., Fang, Z., et al. (2023) · 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10071047 · Cited by 128

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02899-9 · Cited by 128

Convolutional Self-Attention Networks

Yang, B., Wang, L., Wong, D. F., Chao, L. S., & Tu, Z. (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/n19-1407 · Cited by 128

Packed Levitated Marker for Entity and Relation Extraction

Ye, D., Lin, Y., Li, P., & Sun, M. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.337 · Cited by 144

ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding

Xue, L., Yu, N., Zhang, S., Panagopoulou, A., Li, J., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J. C., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02558 · Cited by 144

Societal Biases in Language Generation: Progress and Challenges

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (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.330 · Cited by 131

Personalized Transformer for Explainable Recommendation

Li, L., Zhang, Y., & Chen, L. (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.383 · Cited by 124

From sequence to function through structure: Deep learning for protein design

(2022) · doi:10.1016/j.csbj.2022.11.014 · Cited by 124

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.7 · Cited by 124

Enhancing Factual Consistency of Abstractive Summarization

Zhu, C., Hinthorn, W., Xu, R., Zeng, Q., Zeng, M., Huang, X., & Jiang, M. (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.58 · Cited by 127

A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education

Doughty, J., Wan, Z., Bompelli, A., Qayum, J., Wang, T., Zhang, J., Zheng, Y., Doyle, A., Sridhar, P., Agarwal, A., et al. (2024) · Proceedings of the 26th Australasian Computing Education Conference · doi:10.1145/3636243.3636256 · Cited by 132

CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer

Kim, Y., Kim, S., Choi, J. W., & Kum, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25198 · Cited by 130

A systematic review of digital transformation technologies in museum exhibition

Li, J., Zheng, X., Watanabe, I., & Ochiai, Y. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108407 · Cited by 136

GSM: Graph Similarity Model for Multi-Object Tracking

Liu, Q., Chu, Q., Liu, B., & Yu, N. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/74 · Cited by 123

Jointly Learning to Align and Translate with Transformer Models

Garg, S., Peitz, S., Nallasamy, U., & Paulik, 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/d19-1453 · Cited by 123

Investigating Self-Supervised Front Ends for Speech Spoofing Countermeasures

Wang, X., & Yamagishi, J. (2022) · The Speaker and Language Recognition Workshop (Odyssey 2022) · doi:10.21437/odyssey.2022-14 · Cited by 138

Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents

Tu, M., Huang, K., Wang, G., Huang, J., He, X., & Zhou, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6441 · Cited by 136

Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports

Zhou, H.-Y., Chen, X., Zhang, Y., Luo, R., Wang, L., & Yu, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00425-9 · Cited by 126

Detecting Hallucinated Content in Conditional Neural Sequence Generation

Zhou, C., Neubig, G., Gu, J., Diab, M., Guzmán, F., Zettlemoyer, L., & Ghazvininejad, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.120 · Cited by 122

Pre-trained language model representations for language generation

Edunov, S., Baevski, A., & Auli, M. (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/n19-1409 · Cited by 122

EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124

Performance of large language models on a neurology board-style examination

Schubert, M. C., Wick, W., & Venkataramani, V. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.46721 · Cited by 115

Typology of Risks of Generative Text-to-Image Models

Bird, C., Ungless, E., & Kasirzadeh, A. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604722 · Cited by 121

IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

Yin, J., Shen, J., Chen, R., Li, W., Yang, R., Frossard, P., & Wang, W. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01412 · Cited by 133

The limitations and ethical considerations of ChatGPT

Hua, S., Jin, S., & Jiang, S. (2024) · Data Intelligence · doi:10.1162/dint_a_00243 · Cited by 120

Abstractive Summarization: A Survey of the State of the Art

Lin, H., & Ng, V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019815 · Cited by 120

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces

Ding, Y., Li, Y., Sun, H., Liu, R., Tong, C., Liu, C., Zhou, X., & Guan, C. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3504604 · Cited by 120

Where to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence

Singh, N., Bernal, G., Savchenko, D., & Glassman, E. L. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3511599 · Cited by 119

On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation

Wang, C., & Sennrich, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.326 · Cited by 118

Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture

Zheng, P., Zhou, H., Liu, J., & Nakanishi, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121607 · Cited by 118

Multi-modal cross-attention network for Alzheimer’s disease diagnosis with multi-modality data

Zhang, J., He, X., Liu, Y., Cai, Q., Chen, H., & Qing, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107050 · Cited by 117

Deep learning and the global workspace theory

VanRullen, R., & Kanai, R. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.005 · Cited by 128

Untitled work

(2021) · doi:10.18653/v1/2021.naacl-main.46 · Cited by 115

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning

Nguyen, K., & Daumé III, H. (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/d19-1063 · Cited by 115

Chatgpt: a meta-analysis after 2.5 months

Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100541 · Cited by 115

Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning

Zhu, D., Lu, S., Wang, M., Lin, J., & Wang, Z. (2020) · IEEE Transactions on Circuits and Systems II: Express Briefs · doi:10.1109/tcsii.2020.3002564 · Cited by 115

Token Merging for Fast Stable Diffusion

Bolya, D., & Hoffman, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00484 · Cited by 115

A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects

Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121

SpecInfer: Accelerating Large Language Model Serving with Tree-based Speculative Inference and Verification

Miao, X., Oliaro, G., Zhang, Z., Cheng, X., Wang, Z., Zhang, Z., Wong, R. Y. Y., Zhu, A., Yang, L., Shi, X., et al. (2024) · Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 · doi:10.1145/3620666.3651335 · Cited by 114

Diffusion Models, Image Super-Resolution, and Everything: A Survey

Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117

A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis

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