Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Iterative Pseudo-Labeling for Speech Recognition

Xu, Q., Likhomanenko, T., Kahn, J., Hannun, A., Synnaeve, G., & Collobert, R. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1800 · Cited by 112

Parallel Context Windows for Large Language Models

Ratner, N., Levine, Y., Belinkov, Y., Ram, O., Magar, I., Abend, O., Karpas, E., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.352 · Cited by 111

Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions

Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101356 · Cited by 111

Segmenting Transparent Objects in the Wild with Transformer

Xie, E., Wang, W., Wang, W., Sun, P., Xu, H., Liang, D., & Luo, P. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/165 · Cited by 111

LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models

Almodovar, C., Sabrina, F., Karimi, S., & Azad, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3358730 · Cited by 111

What Artificial Neural Networks Can Tell Us about Human Language Acquisition

Warstadt, A., & Bowman, S. R. (2022) · Algebraic Structures in Natural Language · doi:10.1201/9781003205388-2 · Cited by 111

MuTual: A Dataset for Multi-Turn Dialogue Reasoning

Cui, L., Wu, Y., Liu, S., Zhang, Y., & Zhou, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.130 · Cited by 110

Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions

Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3520707 · Cited by 110

The benefits and dangers of anthropomorphic conversational agents

Peter, S., Riemer, K., & West, J. D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415898122 · Cited by 110

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M.-H., Zi, Y., Anderson, C. J., Feldman, M. Q., et al. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3267446 · Cited by 394

Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert

Wang, J., Qian, X., Zhang, M., Tan, R. T., & Li, H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01408 · Cited by 116

Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings

Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109

CLIP-Event: Connecting Text and Images with Event Structures

Li, M., Xu, R., Wang, S., Zhou, L., Lin, X., Zhu, C., Zeng, M., Ji, H., & Chang, S.-F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01593 · Cited by 117

A lithium-ion battery SOH estimation method based on temporal pattern attention mechanism and CNN-LSTM model

Huang, J., He, T., Zhu, W., Liao, Y., Zeng, J., Xu, Q., & Niu, Y. (2025) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109930 · Cited by 113

Long-term Mortality Risk Stratification of Liver Transplant Recipients: Real-Time Application of Deep Learning Algorithms on Longitudinal Data

Nitski, O., Azhie, A., Qazi-Arisar, F. A., Wang, X., Ma, S., Lilly, L., Watt, K. D., Levitsky, J., Asrani, S. K., Lee, D. S., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00040-6 · Cited by 110

ChatGPT for digital forensic investigation: The good, the bad, and the unknown

Scanlon, M., Breitinger, F., Hargreaves, C., Hilgert, J.-N., & Sheppard, J. (2023) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2023.301609 · Cited by 118

Gait Recognition Based on Deep Learning: A Survey

Filipi Gonçalves dos Santos, C., Oliveira, D. D. S., A. Passos, L., Gonçalves Pires, R., Felipe Silva Santos, D., Pascotti Valem, L., P. Moreira, T., Cleison S. Santana, M., Roder, M., Paulo Papa, J., et al. (2023) · ACM Computing Surveys · doi:10.1145/3490235 · Cited by 108

The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance

Zhang, L., Wang, X., Cooper, E., Evans, N., & Yamagishi, J. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2022.3233236 · Cited by 100

Large-Scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification

Chen, Z., Chen, S., Wu, Y., Qian, Y., Wang, C., Liu, S., Qian, Y., & Zeng, M. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747814 · Cited by 112

Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers

Kosar, T., Ostojić, D., Liu, Y. D., & Mernik, M. (2024) · Mathematics · doi:10.3390/math12050629 · Cited by 107

TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation

Suh, S., Rey, V. F., & Lukowicz, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110143 · Cited by 107

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation

Hong, S., Moon, S., Kim, J., Lee, S., Kim, M., Lee, D., & Kim, J.-Y. (2022) · 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro56248.2022.00051 · Cited by 107

On NMT Search Errors and Model Errors: Cat Got Your Tongue?

Stahlberg, F., & Byrne, B. (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/d19-1331 · Cited by 107

Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic review

Hua, Y., Siddals, S., Ma, Z., Galatzer‐Levy, I., Xia, W., Hau, C., Na, H., Flathers, M., Linardon, J., Ayubcha, C., et al. (2025) · World Psychiatry · doi:10.1002/wps.21352 · Cited by 117

Improved RawNet with Feature Map Scaling for Text-Independent Speaker Verification Using Raw Waveforms

Jung, J.-W., Kim, S.-B., Shim, H.-J., Kim, J.-H., & Yu, H.-J. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1011 · Cited by 109

U-BERT: Pre-training User Representations for Improved Recommendation

Qiu, Z., Wu, X., Gao, J., & Fan, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16557 · Cited by 112

The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers

Buschek, D., Zürn, M., & Eiband, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445372 · Cited by 108

Perils and opportunities in using large language models in psychological research

Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae245 · Cited by 111

The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers

Csordás, R., Irie, K., & Schmidhuber, J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.49 · Cited by 106

Robust Small Object Detection on the Water Surface through Fusion of Camera and Millimeter Wave Radar

Cheng, Y., Xu, H., & Liu, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01498 · Cited by 109

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

Wang, P.-Y., Chen, C.-T., Su, J.-W., Wang, T.-Y., & Huang, S.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3071306 · Cited by 105

Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning

Peng, H., Huang, S., Geng, T., Li, A., Jiang, W., Liu, H., Wang, S., & Ding, C. (2021) · 2021 22nd International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed51717.2021.9424344 · Cited by 105

Forecasting extreme labor displacement: A survey of AI practitioners

Gruetzemacher, R., Paradice, D., & Lee, K. B. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120323 · Cited by 105

A Mathematical Investigation of Hallucination and Creativity in GPT Models

Lee, M. (2023) · Mathematics · doi:10.3390/math11102320 · Cited by 105

Few-Shot Text Generation with Natural Language Instructions

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.32 · Cited by 107

Glancing Transformer for Non-Autoregressive Neural Machine Translation

Qian, L., Zhou, H., Bao, Y., Wang, M., Qiu, L., Zhang, W., Yu, Y., & Li, 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.155 · Cited by 108

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) · doi:10.2139/ssrn.4352931 · Cited by 708

Delving Deep into the Generalization of Vision Transformers under Distribution Shifts

Zhang, C., Zhang, M., Zhang, S., Jin, D., Zhou, Q., Cai, Z., Zhao, H., Liu, X., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00713 · Cited by 107

HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

Shabani, M. A., Hosseini, S., & Furukawa, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00529 · Cited by 124

CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow

Weinzaepfel, P., Lucas, T., Leroy, V., Cabon, Y., Arora, V., Brégier, R., Csurka, G., Antsfeld, L., Chidlovskii, B., & Revaud, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01647 · Cited by 110

M3Care: Learning with Missing Modalities in Multimodal Healthcare Data

Zhang, C., Chu, X., Ma, L., Zhu, Y., Wang, Y., Wang, J., & Zhao, J. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539388 · Cited by 102

Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

Zhong, W., Fang, C., Cai, Y., Wei, P., Zhao, G., Lin, L., & Li, G. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00938 · Cited by 102

The Self-Perception and Political Biases of ChatGPT

Rutinowski, J., Franke, S., Endendyk, J., Dormuth, I., Roidl, M., & Pauly, M. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/7115633 · Cited by 98

SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques

Siddiq, M. L., & Santos, J. C. S. (2022) · Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security · doi:10.1145/3549035.3561184 · Cited by 101

Self-Supervised Video Forensics by Audio-Visual Anomaly Detection

Feng, C., Chen, Z., & Owens, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01011 · Cited by 101

What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?

Gao, S., Wen, X.-C., Gao, C., Wang, W., Zhang, H., & Lyu, M. R. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00109 · Cited by 106

Reducing Quantity Hallucinations in Abstractive Summarization

Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99

GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

Zhang, W., Zhang, C., & Tsung, F. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/332 · Cited by 99

Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Yun, Z., Chen, Y., Olshausen, B., & LeCun, Y. (2021) · Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures · doi:10.18653/v1/2021.deelio-1.1 · Cited by 98

The feasibility of artificial consciousness through the lens of neuroscience

Aru, J., Larkum, M. E., & Shine, J. M. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.09.009 · Cited by 98

On the Robustness of Self-Attentive Models

Hsieh, Y.-L., Cheng, M., Juan, D.-C., Wei, W., Hsu, W.-L., & Hsieh, C.-J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1147 · Cited by 96

Semantic Audio-Visual Navigation

Chen, C., Al-Halah, Z., & Grauman, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01526 · Cited by 96

Embers of autoregression show how large language models are shaped by the problem they are trained to solve

McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322420121 · Cited by 96

Width & Depth Pruning for Vision Transformers

Yu, F., Huang, K., Wang, M., Cheng, Y., Chu, W., & Cui, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20222 · Cited by 95

Multi-Modal Attention for Speech Emotion Recognition

Pan, Z., Luo, Z., Yang, J., & Li, H. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1653 · Cited by 95

ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments

An, D., Wang, H., Wang, W., Wang, Z., Huang, Y., He, K., & Wang, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3386695 · Cited by 103

From General to Specific: Informative Scene Graph Generation via Balance Adjustment

Guo, Y., Gao, L., Wang, X., Hu, Y., Xu, X., Lu, X., Shen, H. T., & Song, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01607 · Cited by 97

An Algorithm–Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers

Fang, C., Zhou, A., & Wang, Z. (2022) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2022.3197282 · Cited by 94

Investigating the informativeness of technical indicators and news sentiment in financial market price prediction

Anbaee Farimani, S., Vafaei Jahan, M., Milani Fard, A., & Tabbakh, S. R. K. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108742 · Cited by 93

Vector-Quantized Autoregressive Predictive Coding

Chung, Y.-A., Tang, H., & Glass, J. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1228 · Cited by 97

Contrastive Learning Based Modality-Invariant Feature Acquisition for Robust Multimodal Emotion Recognition With Missing Modalities

Liu, R., Zuo, H., Lian, Z., Schuller, B. W., & Li, H. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3378570 · Cited by 96

Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video Detection

Yin, Q., Lu, W., Li, B., & Huang, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3290752 · Cited by 92

Biomedical Named Entity Recognition with Multilingual BERT

Hakala, K., & Pyysalo, S. (2019) · Proceedings of The 5th Workshop on BioNLP Open Shared Tasks · doi:10.18653/v1/d19-5709 · Cited by 96

A review of deep learning-based deformable medical image registration

Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91

Multimodal End-to-End Sparse Model for Emotion Recognition

Dai, W., Cahyawijaya, S., Liu, Z., & Fung, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.417 · Cited by 91

FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

Huang, R., Lam, M. W. Y., Wang, J., Su, D., Yu, D., Ren, Y., & Zhao, Z. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/577 · Cited by 96

DCR-net: a deep co-interactive relation network for joint dialog act recognition and sentiment classification

Qin, L., Che, W., Li, Y., Ni, M., & Liu, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6391 · Cited by 90

Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition

Demir, K. C., Schieber, H., Weise, T., Roth, D., May, M., Maier, A., & Yang, S. H. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3311628 · Cited by 90

Ditto: Building Digital Twins of Articulated Objects from Interaction

Jiang, Z., Hsu, C.-C., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00553 · Cited by 92

Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

Yang, W., Li, Q., Liu, W., Yu, Y., Ma, Y., He, S., & Pan, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01528 · Cited by 89

Analyzing Multimodal Sentiment Via Acoustic- and Visual-LSTM With Channel-Aware Temporal Convolution Network

Mai, S., Xing, S., & Hu, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3068598 · Cited by 89

A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning

Kotei, E., & Thirunavukarasu, R. (2023) · Information · doi:10.3390/info14030187 · Cited by 89

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Khormali, A., & Yuan, J.-S. (2022) · Applied Sciences · doi:10.3390/app12062953 · Cited by 88

Bad Characters: Imperceptible NLP Attacks

Boucher, N., Shumailov, I., Anderson, R., & Papernot, N. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833641 · Cited by 102

Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation

Yan, B., & Pei, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20204 · Cited by 94

Generate & Rank: A Multi-task Framework for Math Word Problems

Shen, J., Yin, Y., Li, L., Shang, L., Jiang, X., Zhang, M., & Liu, Q. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.195 · Cited by 88

Untitled work

(2019) · doi:10.18653/v1/d19-1432 · Cited by 88

Benchmarking and scaling of deep learning models for land cover image classification

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.012 · Cited by 86

Segment Anything in Medical Images

Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2023) · arXiv · doi:10.48550/arxiv.2304.12306 · Cited by 67

CodeFill: multi-token code completion by jointly learning from structure and naming sequences

Izadi, M., Gismondi, R., & Gousios, G. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510172 · Cited by 86

Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions

Jiang, S., Hu, J., Wood, K. L., & Luo, J. (2022) · Journal of Mechanical Design · doi:10.1115/1.4051681 · Cited by 86

Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization

Li, Z., Sun, M., Lu, A., Ma, H., Yuan, G., Xie, Y., Tang, H., Li, Y., Leeser, M., Wang, Z., et al. (2022) · 2022 32nd International Conference on Field-Programmable Logic and Applications (FPL) · doi:10.1109/fpl57034.2022.00027 · Cited by 85

EquiPNAS: improved protein–nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

Roche, R., Moussad, B., Shuvo, M. H., Tarafder, S., & Bhattacharya, D. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae039 · Cited by 85

A cross-modal generative adversarial network for scenarios generation of renewable energy

Kang, M., Zhu, R., Chen, D., Li, C., Gu, W., Qian, X., & Yu, W. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3277698 · Cited by 90

E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning

Xu, H., Yan, M., Li, C., Bi, B., Huang, S., Xiao, W., & Huang, F. (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.42 · Cited by 84

Neural reverse engineering of stripped binaries using augmented control flow graphs

David, Y., Alon, U., & Yahav, E. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428293 · Cited by 84

Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion

Liang, T., Lin, G., Feng, L., Zhang, Y., & Lv, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00804 · Cited by 93

X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

Cho, J., Lu, J., Schwenk, D., Hajishirzi, H., & Kembhavi, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.707 · Cited by 99

The grass is not always greener: Teacher vs. GPT-assisted written corrective feedback

Lin, S., & Crosthwaite, P. (2024) · System · doi:10.1016/j.system.2024.103529 · Cited by 86

Perceiver CPI: a nested cross-attention network for compound–protein interaction prediction

Nguyen, N.-Q., Jang, G., Kim, H., & Kang, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac731 · Cited by 83

A/B testing: A systematic literature review

Quin, F., Weyns, D., Galster, M., & Silva, C. C. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112011 · Cited by 83

Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

Chen, S., Zhang, F., Sone, K., & Roth, 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.475 · Cited by 83

Envedit: Environment Editing for Vision-and-Language Navigation

Li, J., Tan, H., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01497 · Cited by 87

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Li, Z., Cong, Y., Chen, X., Qi, J., Sun, J., Yan, T., Yang, H., Liu, J., Lu, E., Wang, L., et al. (2023) · iScience · doi:10.1016/j.isci.2022.105872 · Cited by 82

RRSIS: Referring Remote Sensing Image Segmentation

Yuan, Z., Mou, L., Hua, Y., & Zhu, X. X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3369720 · Cited by 81

A Surprisingly Robust Trick for the Winograd Schema Challenge

Kocijan, V., Cretu, A.-M., Camburu, O.-M., Yordanov, Y., & Lukasiewicz, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1478 · Cited by 81

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81

TableFormer: Robust Transformer Modeling for Table-Text Encoding

Yang, J., Gupta, A., Upadhyay, S., He, L., Goel, R., & Paul, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.40 · Cited by 85

Compressing Context to Enhance Inference Efficiency of Large Language Models

Li, Y., Dong, B., Guerin, F., & Lin, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.391 · Cited by 253

Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification

Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac022 · Cited by 80