SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (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/n19-1423 · Cited by 54,325

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

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

BERT applications in natural language processing: a review

Gardazi, N. M., Daud, A. Y., Malik, M. K., Bukhari, A. A., Alsahfi, T., & Alshemaimri, B. K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11162-5 · Cited by 354

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

Yao, Z., Gholami, A., Shen, S., Mustafa, M., Keutzer, K., & Mahoney, M. W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17275 · Cited by 257

End-to-End Transformer-Based Models in Textual-Based NLP

Rahali, A., & Akhloufi, M. A. (2023) · AI · doi:10.3390/ai4010004 · Cited by 209

Survey of transformers and towards ensemble learning using transformers for natural language processing

Zhang, H., & Shafiq, M. O. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00842-0 · Cited by 180

Learned Token Pruning for Transformers

Kim, S., Shen, S., Thorsley, D., Gholami, A., Kwon, W., Hassoun, J., & Keutzer, K. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539260 · Cited by 139

EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference

Tambe, T., Hooper, C. R. C., Pentecost, L., Jia, T., Yang, E.-Y., Donato, M., Sanh, V., Whatmough, P. N., Rush, A. M., Brooks, D. M., et al. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480095 · Cited by 145

A Survey on Bias in Deep NLP

Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021) · Applied Sciences · doi:10.3390/app11073184 · Cited by 123

Efficient Methods for Natural Language Processing: A Survey

Treviso, M., Lee, J.-U., Ji, T., van Aken, B., Cao, Q., Ciosici, M. R., Hassid, M., Heafield, K., Hooker, S., Raffel, C., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00577 · Cited by 156

Compression of Deep Learning Models for Text: A Survey

Gupta, M., & Agrawal, P. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3487045 · Cited by 128

Performance Study on Extractive Text Summarization Using BERT Models

Abdel-Salam, S., & Rafea, A. (2022) · Information · doi:10.3390/info13020067 · Cited by 106

WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

Huang, J., Tang, D., Zhong, W., Lu, S., Shou, L., Gong, M., Jiang, D., & Duan, N. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.23 · Cited by 99

Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Bondarenko, Y., Nagel, M., & Blankevoort, T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.627 · Cited by 172

Deep Learning-Based Depression Detection from Social Media: Comparative Evaluation of ML and Transformer Techniques

Bokolo, B. G., & Liu, Q. (2023) · Electronics · doi:10.3390/electronics12214396 · Cited by 116

Pre-trained Summarization DistillationPreprint

Shleifer, S., & Rush, A. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13002 · Cited by 121

Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions

Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60

Survey of BERT (Bidirectional Encoder Representation Transformer) types

Mohammed, A. H., & Ali, A. H. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1963/1/012173 · Cited by 62

Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM

Hussain, S., Afzal, H., Saeed, R., Iltaf, N., & Umair, M. Y. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5589829 · Cited by 40

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4

Pelrine, K., Imouza, A., Thibault, C., Reksoprodjo, M., Gupta, C., Christoph, J. N., Godbout, J., & Rabbany, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.395 · Cited by 83

DCT-Former: Efficient Self-Attention with Discrete Cosine Transform

Scribano, C., Franchini, G., Prato, M., & Bertogna, M. (2023) · Journal of Scientific Computing · doi:10.1007/s10915-023-02125-5 · Cited by 41

Your fairness may vary: Pretrained language model fairness in toxic text classification

Baldini, I., Wei, D., Ramamurthy, K. N., Singh, M., & Yurochkin, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.176 · Cited by 51

Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach

Ain, Q. U., Chatti, M. A., Bakar, K. G. C., Joarder, S., & Alatrash, R. (2023) · Information · doi:10.3390/info14100526 · Cited by 43

Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

Fang, T., Wang, C., Qiu, M., Zhang, Y., Li, Y., & Huang, J. (2021) · doi:10.18653/v1/2021.acl-long.236 · Cited by 51

Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System

Guesmi, M., Chatti, M. A., Joarder, S., Ain, Q. U., Alatrash, R., Siepmann, C., & Vahidi, T. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2262797 · Cited by 31

AutoBERT-Zero: Evolving BERT Backbone from Scratch

Gao, J., Xu, H., Han, S., Ren, X., Yu, P. L. H., Liang, X., Jiang, X., & Li, Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21311 · Cited by 32

GhostBERT: Generate More Features with Cheap Operations for BERT

Huang, Z., Hou, L., Shang, L., Jiang, X., Chen, X. D., & Liu, Q. (2021) · doi:10.18653/v1/2021.acl-long.509 · Cited by 23

Emotion Recognition on Edge Devices: Training and Deployment

Pandelea, V., Ragusa, E., Apicella, T., Gastaldo, P., & Cambria, E. (2021) · Sensors · doi:10.3390/s21134496 · Cited by 21

EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation

Dong, C., Wang, G., Xu, H., Peng, J., Ren, X., & Liang, X. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.123 · Cited by 22

Machine learning-based approaches for ubiquitination site prediction in human proteins

Pourmirzaei, M., Ramazi, S., Esmaili, F., Shojaeilangari, S., & Allahvardi, A. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05581-w · Cited by 20

SkipBERT: Efficient Inference with Shallow Layer Skipping

Wang, J., Chen, K., Chen, G., Shou, L., & McAuley, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.503 · Cited by 20

ILDAE: Instance-Level Difficulty Analysis of Evaluation Data

Varshney, N., Mishra, S. R., & Baral, C. R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.240 · Cited by 19

Eye Gaze and Self-attention: How Humans and Transformers Attend Words in Sentences

Bensemann, J., Peng, A. Y., Benavides‐Prado, D., Chen, Y., Tan, N. Ö., Corballis, P. M., Riddle, P., & Witbrock, M. (2022) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2022.cmcl-1.9 · Cited by 17

Exposing the Achilles’ heel of textual hate speech classifiers using indistinguishable adversarial examples

Aggarwal, S., & Vishwakarma, D. K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124278 · Cited by 17

Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

(2020) · doi:10.18653/v1/2020.sustainlp-1 · Cited by 16

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

Liapis, C. M., & Kotsiantis, S. B. (2023) · Information · doi:10.3390/info14110596 · Cited by 19

A survey on moral foundation theory and pre-trained language models: current advances and challenges

Zangari, L., Greco, C. M., Picca, D., & Tagarelli, A. (2025) · AI & Society · doi:10.1007/s00146-025-02225-w · Cited by 21

PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors

Chang, H.-Y., Mozafari, S. H., Chen, C., Clark, J. J., Meyer, B. H., & Gross, W. J. (2023) · Journal of Signal Processing Systems · doi:10.1007/s11265-022-01814-y · Cited by 14

SKDBERT: Compressing BERT via Stochastic Knowledge Distillation

Ding, Z., Guoqing, J., Zhang, S., Guo, L., & Lin, W. T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25902 · Cited by 14

Multi-modal spatial relational attention networks for visual question answering

Yao, H., Wang, L., Cai, C., Sun, Y., Zhang, Z., & Luo, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104840 · Cited by 14

Debiasing pretrained text encoders by paying attention to paying attention

Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.651 · Cited by 14

Distilling Wisdom: A Review on Optimizing Learning From Massive Language Models

Zhang, D., Listiyani, D., Singh, P., & Mohanty, M. (2025) · IEEE Access · doi:10.1109/access.2025.3554586 · Cited by 18

NLP based Deep Learning Approach for Plagiarism Detection

Rosu, R., Stoica, A. Ş., Popescu, P. Ș., & Mihăescu, M. C. (2020) · International Joural of User-System Interaction · doi:10.37789/ijusi.2020.13.1.4 · Cited by 12

KPLLM-STE: Knowledge-enhanced and prompt-aware large language models for short-text expansion

Zhong, H., Zhang, Q., Li, W., Lin, R., & Tang, Y. Z. (2024) · World Wide Web · doi:10.1007/s11280-024-01322-y · Cited by 11

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

Liu, P., Gao, Z.-F., Zhao, W. X., Xie, Z. Y., Lu, Z.-Y., & Wen, J.-R. (2021) · doi:10.18653/v1/2021.acl-long.418 · Cited by 27

A new approach to risk assessment in failure mode and effect analysis based on engineering textual data

Song, W., & Zheng, J. (2024) · Quality Engineering · doi:10.1080/08982112.2024.2304815 · Cited by 11

Pushing the Accuracy of Thai Food Image Classification with Transfer Learning

Ittisoponpisan, S., Kaipan, C., Ruang-on, S., Thaiphan, R., & Songsri-in, K. (2022) · Engineering Journal · doi:10.4186/ej.2022.26.10.57 · Cited by 10

Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions.Preprint

Tailor, S. A., Opolka, F. L., Lió, P., & Lane, N. D. (2021) · arXiv (Cornell University) · Cited by 10

EdgeTran: Device-Aware Co-Search of Transformers for Efficient Inference on Mobile Edge Platforms

Tuli, S., & Jha, N. K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3328287 · Cited by 16

CS-BERT: a pretrained model for customer service dialogues

Wang, P., Fang, J., & Reinspach, J. (2021) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2021.nlp4convai-1.13 · Cited by 9

A flaw in using pretrained protein language models in protein–protein interaction inference models

Szymborski, J., & Emad, A. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-025-01176-7 · Cited by 13

The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment

Fernandez, J., Kahn, J., Na, C., Bisk, Y., & Strubell, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.98 · Cited by 11

Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions

Do, N., & Pavlick, E. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.181 · Cited by 8

Word embedding factor based multi-head attention

Li, Z., Zhao, Y., Zhang, X., Han, H., & Huang, C. F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11115-y · Cited by 7

Towards SLO-Compliant and Cost-Effective Serverless Computing on Emerging GPU Architectures

Bhasi, V. M., Sharma, A., Jain, R., Gunasekaran, J. R., Pattnaik, A., Kandemir, M., & Das, C. R. (2024) · International Middleware Conference · doi:10.1145/3652892.3700760 · Cited by 10

On the Role of Corpus Ordering in Language Modeling

Agrawal, A., Singh, S., Schneider, L. R., & Samuels, M. (2021) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2021.sustainlp-1.15 · Cited by 7

High Performance Natural Language Processing

Ilharco, G., Magalhaes, C. I., Turc, I., Dettmers, T., Ferreira, F., & Lee, K. (2020) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2020.emnlp-tutorials.4 · Cited by 7

On convex decision regions in deep network representations

Tětková, L., Brüsch, T., Dorszewski, T., Mager, F. M., Aagaard, R. Ø., Foldager, J., Alstrøm, T. S., & Hansen, L. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60809-y · Cited by 10

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Guesmi, M., Chatti, M. A., Kadhim, L., Joarder, S., & Ain, Q. U. (2023) · Multimodal Technologies and Interaction · doi:10.3390/mti7090091 · Cited by 6

Efficient Transformers for on-robot Natural Language Understanding

Greco, A., Roberto, A., Saggese, A., & Vento, M. (2022) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids53995.2022.10000085 · Cited by 6

Optimal Condition Training for Target Source Separation

Tzinis, E., Wichern, G., Smaragdis, P., & Roux, J. L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095128 · Cited by 6

GHOST at SemEval-2021 Task 5: Is explanation all you need?

Pluciński, K., & Klimczak, H. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.114 · Cited by 6

MLPruning: A Multilevel Structured Pruning Framework for Transformer-based ModelsPreprint

Yao, Z., Ma, L., Shen, S., Keutzer, K., & Mahoney, M. W. (2021) · arXiv (Cornell University) · Cited by 6

Stereotype and Categorical Bias Evaluation via Differential Cosine Bias Measure

Sayenju, S., Aygün, R. S., Franks, B., Johnston, S., Lee, G., & Modgil, G. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020924 · Cited by 5

Detection of Morality in Tweets Based on the Moral Foundation Theory

Bulla, L., Giorgis, S. D., Aldo Gangemi, A., Marinucci, L., & Mongiovı̀, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25599-1_1 · Cited by 5

Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network

Zhou, X., Fu, Q., Chen, J., Liu, L., Wang, Y., Lu, Y., & Wu, H. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104445 · Cited by 5

EPAM: A Predictive Energy Model for Mobile AI

Mallik, A., Wang, H., Xie, J. L., Chen, D., & Han, K. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279417 · Cited by 5

E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models

Akbari, M., Banitalebi-Dehkordi, A., & Zhang, Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.359 · Cited by 8

Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative Study

Guesmi, M., Siepmann, C., Chatti, M. A., Joarder, S., Ain, Q. U., & Alatrash, R. (2023) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3563359.3597379 · Cited by 5

NoiER: An Approach for Training More Reliable Fine-Tuned Downstream Task Models

Jang, M., & Lukasiewicz, T. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3193292 · Cited by 4

Explainable AI Discloses Gender Bias in Sexism Detection Algorithm

Muntasir, F., & Noor, J. (2024) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3704522.3704524 · Cited by 5

Revisiting Text Guide, a Truncation Method for Long Text Classification

Fiok, K., Karwowski, W., Gutiérrez-Franco, E., Davahli, M. R., Wilamowski, M., & Ahram, T. (2021) · Applied Sciences · doi:10.3390/app11188554 · Cited by 4

Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense

Cui, W., & Chen, X. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.138 · Cited by 4

Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

Kadiķis, E., Srivastav, V., & Klinger, R. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.441 · Cited by 4

Textual Pre-Trained Models for Age Screening Across Community Question-Answering

Figueroa, A., & Timilsina, M. (2024) · IEEE Access · doi:10.1109/access.2024.3368929 · Cited by 3

A systematic review of toxicity in large language models: definitions, datasets, detectors, detoxification methods and challenges

Villate-Castillo, G., Ser, J. D., & Sanz, B. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11548-z · Cited by 7

Memory-Assistant Collaborative Language Understanding for Artificial Intelligence of Things

Yan, M., Chen, C., Du, J., Peng, X., Zhou, J. T., & Zeng, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3100397 · Cited by 3

A data generation pipeline for cardiac vessel segmentation and motion artifact grading

Sushmit, A. S., Xu, Y., Mariani, O., Lyu, Q., Li, Y., Cao, X., Wiedeman, C., Ma, H., Maltz, J. S., Yu, H., et al. (2022) · Developments in X-Ray Tomography · doi:10.1117/12.2642869 · Cited by 3

Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across DomainsPreprint

Fang, T., Wang, C., Qiu, M., Zhang, Y., Li, Y., & Huang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01266 · Cited by 3

Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

(2022) · doi:10.18653/v1/2022.cmcl-1 · Cited by 3

BOHDL model: a robust framework for fault detection and classification in ring/radial distribution systems

Tiwari, G., Saini, S., & Minaxi (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10847-2 · Cited by 2

Comparative Analysis of Deep Learning Algorithms in Fire Detection

Göçmen, R., Çıbuk, M., & Akin, E. (2024) · Balkan Journal of Electrical and Computer Engineering · doi:10.17694/bajece.1533966 · Cited by 2

Architectures for Machine Learning

Yang, Y., Chen, C., & Wang, Z. (2024) · Springer eBooks · doi:10.1007/978-981-97-9314-3_12 · Cited by 2

GroupBERT: Enhanced Transformer Architecture with Efficient Grouped StructuresPreprint

Chelombiev, I., Justus, D., Orr, D., Dietrich, A., Gressmann, F., Koliousis, A., & Luschi, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05822 · Cited by 6

Multi-Label Emotion Classification Using Physically Explicated Dataset

Sowndarya Spoorthi, B., Harshitha, V., Nagasandesh, N., Pramod, K. S., & Shashank, N. (2023) · Soft Computing Research Society eBooks · doi:10.52458/978-81-955020-5-9-71 · Cited by 1

SAFE Natural Language Processing

Babaei, G., Giudice, O., Giudici, P., & Maggi, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228343 · Cited by 1

Small language models for on-device IoT intelligence: A survey on architectures, optimization, and deployment

Driss, M., Atitallah, S. B., Benjdira, B., AlHalawani, S., Ali, A. M., & Boulila, W. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102020 · Cited by 2

Nomen est Omen - The Role of Signatures in Ascribing Email Author Identity with Transformer Neural Networks

Srinivasan, S., Begoli, E., Mahbub, M., & Knight, K. (2021) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw53761.2021.00049 · Cited by 2

Student performance prediction based on multiple-choice question test using neural network in the VLab platform

Deshmukh, S. A., Vaidya, R., Kamuni, V. D., & Gaikwad, S. V. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2023.2214792 · Cited by 1

Efficient 1D Grouped Convolution for PyTorch a Case Study: Fast On-Device Fine-Tuning for SqueezeBERT

Mozafari, S. H., Clark, J. J., Gross, W. J., & Meyer, B. H. (2023) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap57973.2023.00024 · Cited by 1

FHSI-GNN: Fusion Hierarchical Structure Information Graph Neural Network for Extractive Long Documents Summarization

Zhang, Z., Yun, W., Jia, X., Lv, Q., Ni, H., Wang, X., & Wu, G. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8138-0_12 · Cited by 1

HMI: hierarchical knowledge management for efficient multi-tenant inference in pretrained language models

Zhang, J., Wang, J., Li, H., Shou, L., Chen, K., Chen, G., Xie, Q., Xie, G., & Gong, X. (2025) · The VLDB Journal · doi:10.1007/s00778-025-00919-7 · Cited by 1

A Follow-the-Leader Strategy Using Hierarchical Deep Neural Networks with Grouped Convolutions

Solomon, J. E., & Charette, F. (2021) · SN Computer Science · doi:10.1007/s42979-021-00572-1 · Cited by 1

On The Connection of Benford's Law and Neural Networks.Preprint

Sahu, S. K., Java, A., & Shaikh, A. (2021) · arXiv (Cornell University) · Cited by 1

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsPreprint

Liu, P., Gao, Z.-F., Zhao, W. X., Xie, Z. Y., Lu, Z.-Y., & Wen, J.-R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02205 · Cited by 1

Your fairness may vary: Group fairness of pretrained language models in toxic text classification.Preprint

Baldini, I., Wei, D., Ramamurthy, K. N., Yurochkin, M., & Singh, M. (2021) · arXiv (Cornell University) · Cited by 1

Data Mining Tool To Help The Scientific Community Develop Answers To Covid-19 Queries

Rajath, S., Kumar, A., Agarwal, M., shekar, S. N. C., & Prasad, V. B. (2021) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds53782.2021.9626771 · Cited by 1

Architectures for Machine Learning

Yang, Y., Chen, C., & Wang, Z. (2022) · Springer eBooks · doi:10.1007/978-981-15-6401-7_12-1

High-Throughput Edge Inference for BERT Models via Neural Architecture Search and Pipeline

Chang, H.-Y., Mozafari, S. H., Clark, J. J., Meyer, B. H., & Gross, W. J. (2023) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3583781.3590302

Hybrid Model Compression for BERT-Based Polyphone Disambiguation in Mandarin ChinesePreprint

Zhao, W., wang, Z., & xu, L. (2023) · doi:10.36227/techrxiv.23983191

Hybrid Model Compression for BERT-Based Polyphone Disambiguation in Mandarin ChinesePreprint

Zhao, W., wang, Z., & xu, L. (2023) · doi:10.36227/techrxiv.23983191.v1

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., & Zhou, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.195 · Cited by 757

SQUEEZEBERT: BRIDGING THE GAP BETWEEN COMPUTER VISION AND NLP FOR ENHANCED EFFICIENCY

(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets45993

EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation

Dong, C., Wang, G., Xu, H., Peng, J., Ren, X., & Liang, X. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.123 · Cited by 22

Elbert: Fast Albert with Confidence-Window Based Early ExitPreprint

Xie, K., Lu, S., Wang, M., & Wang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00175

DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference

Eyzaguirre, C., del Rio, F., Araujo, V., & Soto, Á. (2022) · NLPPOWER · doi:10.18653/v1/2022.nlppower-1.10 · Cited by 9