AutoFormer: Searching Transformers for Visual Recognition

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

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

ImageNet: A large-scale hierarchical image database

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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

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472

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MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Neural Prompt Search

Zhang, Y., Zhou, K., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435939 · Cited by 127

DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and TransformersPreprint

Li, C., Wang, G., Wang, B., Liang, X., Li, Z., & Chang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10060 · Cited by 3

When Training-Free Nas Meets Vision Transformers: A Neural Tangent Kernel Perspective

Zhou, Q., & Zhu, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447479 · Cited by 2

ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data

Lin, S., Zhang, Y., Fei, X., Liu, X., & Mei, Q. (2024) · Toxics · doi:10.3390/toxics12080554 · Cited by 2

Lightweight Vision Transformer for damaged wheat detection and classification using spectrograms

Lin, H., Guo, M., & Ma, M. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.5.053063 · Cited by 2

AGGA-MVFLN: Multivariate Time Series Forecasting via Adaptive Generalized Graph Accompanied with Multi-View Learning in Frequency Domain

Lei, J., Chen, F. Y., & Tang, H. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733272 · Cited by 2

AV-NAS: Audio-Visual Multi-Level Semantic Neural Architecture Search for Video Hashing

Chen, Y., Zhou, Y., Dong, H., Liu, R., Lin, Z., & Zhang, D. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729899 · Cited by 2

Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images

Su, X. Q., Mao, Q., Wu, Z., Lin, X., You, S., Liao, Y., & Xu, C. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02042-x · Cited by 2

Edge Temporal Digital Twin Network for Sensor-Driven Fault Detection in Nuclear Power Systems

Liu, S., Ye, G., & Zhao, X. (2025) · Sensors · doi:10.3390/s25247510 · Cited by 2

LatentTAS: Constrained Transformer Architecture Search Via Surrogate-Driven Latent Evolution

Liu, L., Yen, G. G., & He, Z. (2025) · International Conference on Machine Intelligence and Nature-Inspired Computing (MIND) · doi:10.1109/mind67540.2025.11351542 · Cited by 2

Vision Transformer Compression and Architecture Exploration with Efficient Embedding Space Search

Kim, D., & Kim, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26313-2_32 · Cited by 2

NPformer based static FDIAs detection for state-of-charge estimation of battery energy storage systems in smart distribution networks

Liu, Z., Li, Y., Xu, S., Wang, Q., & Li, J. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112225 · Cited by 2

Murmuration: On-the-fly DNN Adaptation for SLO-Aware Distributed Inference in Dynamic Edge Environments

Lin, J., Li, M., Zhang, S. Q., & Leon‐Garcia, A. (2024) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3673038.3673154 · Cited by 2

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

Peng, F., Ji, X., Zhang, L., Wang, J., Zhang, K., & Wu, W. (2024) · Electronics · doi:10.3390/electronics13204116 · Cited by 2

HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis

Zhou, F., Kilickaya, M., Vanschoren, J., & Piao, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_14 · Cited by 2

Temporal decomposition and attribute correlation differentiation at multiple scales: A graph imputation network for incomplete multivariate time series

Chen, D., Zhang, L., Lai, X., Lu, W., & Li, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113636 · Cited by 2

Neural Architecture Selection as a Nash Equilibrium With Batch Entanglement

Li, Q., Xue, C., Li, M., Li, C.-G., Ma, C., & Yang, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3283239 · Cited by 2

GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training

Deng, X., Shi, H., Huang, R., Li, C., Xu, H., Han, J., Kwok, J. T.-Y., Zhao, S., Zhang, W., & Liang, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02027 · Cited by 2

Towards Reliable and Efficient Vegetation Segmentation for Australian Wheat Data Analysis

Yuan, B., Wang, Z., & Yu, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47843-7_9 · Cited by 1

STFformer: An Effective PM2.5 Prediction Model

Zhang, G., Niu, D., Wang, H., Tang, Y., Zang, Z., & Huang, Q. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10587392 · Cited by 1

Data-Driven Power Time Series Forcasting with Spatio-Temporal Neural Networks

Kang, L., Zhu, P., Guo, Y., Liao, S., & Xu, Y. (2024) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise62164.2024.10674635 · Cited by 1

Enhancing Building Information Extraction from Remote Sensing Images through Reinforcement Learning from AI Feedback

Cai, W., He, H., Yao, J., Gao, L.-P., & He, L. (2024) · International Conference on Advances in Computing and Artificial Intelligence · doi:10.1109/acai63924.2024.10899536 · Cited by 1

DCCMamba: A Dual-stream Cross-time and Cross-feature with Mamba for Multivariate Time Series Forecasting

Liang, S., Wang, Z., Zhao, C., Huang, Y., Xu, J., & Yang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888787 · Cited by 1

Intermittent-Friendly Neural Architecture Search: Demystifying Accuracy and Overhead Tradeoffs

Mendis, H. R., Yen, C.-H., Kang, C.-K., & Hsiu, P.-C. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3555963 · Cited by 1

UniCoS: A Unified Neural and Accelerator Co-Search Framework for CNNs and ViTs

FU, W. B., Lou, W., Tang, C., Wen, H., Qin, Y., Gong, L., Wang, C., & Zhou, X. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133418 · Cited by 1

Bit-STED: A lightweight transformer for accurate agave counting with UAV imagery

Villatoro-Geronimo, D., Sanchez‐Ante, G., & Falcón-Morales, L. E. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111047 · Cited by 1

StreamTS: A Streamline Solution Towards Zero-Shot Time Series Forecasting with Large Language Models

Song, W., Fang, Y., Gu, X., Zhang, W., Liu, Z., Cheng, Y. H., & Mauro, M. D. (2025) · Electronics · doi:10.3390/electronics14204088 · Cited by 1

DASViT: Differentiable Architecture Search for Vision Transformer

Wu, P., Neri, F., & Feng, Z. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229286 · Cited by 1

Advancing Global Horizontal Irradiance Prediction with Grey Wolf Optimized Hybrid Transformer Models

Sharma, G., Gupta, R., & Mahapatra, R. P. (2025) · Pure and Applied Geophysics · doi:10.1007/s00024-025-03862-4 · Cited by 1

ST-MambaAD: Spatial-Temporal Mamba for Multivariate Time Series Anomaly Detection

Ma, S., Zheng, H., Guan, S., Zheng, X., Ma, T., Dong, Z., & Gao, M. (2025) · IEEE Interregional NEWCAS Conference (NEWCAS) · doi:10.1109/newcas64648.2025.11107158 · Cited by 1

Automated Neural Architecture Design for Industrial Defect Detection

Liu, Y., Ma, Y., Tang, Y., Liu, M., Jiang, S., & Wang, Y. (2025) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2025.3638345 · Cited by 1

MTS-CR: Contrastive Representation Learning for Real-Time Qos Degradation Detection in Media Cloud Shared Instances

Xiao, Z., Qiu, Y., Liu, P., Cao, W., & Ming, Z. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460796 · Cited by 1

Constraint-aware transformer architecture search via feasibility discriminator-guided latent-space evolution

Liu, L., Yen, G. G., & He, Z. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123858 · Cited by 1

Symmetrical irregular local features for fine-grained visual classification

Yang, M., Xu, Y., Wu, Z., & Wei, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.056 · Cited by 1

Proxyless Neural Architecture Adaptation at Once

Kim, D.-G., & Lee, H.-C. (2022) · IEEE Access · doi:10.1109/access.2022.3206765 · Cited by 1

ScaMP: Scalable Meta-Parallelism for Deep Learning Search

Anthony, Q. G., Xu, L., Shafi, A., Subramoni, H., & Panda, D. K. D. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00044 · Cited by 1

DynaSlim: Dynamic Slimming for Vision Transformers

Shi, D., Gao, J., Liu, T., & Fu, Y. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00251 · Cited by 1

Late Breaking Results: Fast Fair Medical Applications? Hybrid Vision Models Achieve the Fairness on the Edge

Yang, C., Sheng, Y., Dong, P., Kong, Z., Li, Y., Yu, P., Yang, L., & Lin, X. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247761 · Cited by 1

Fast and Fair Medical AI on the Edge Through Neural Architecture Search for Hybrid Vision Models

Yang, C., Sheng, Y., Dong, P., Kong, Z., Li, Y., Yu, P., Yang, L., Lin, X., & Wang, Y. (2023) · doi:10.1109/iccad57390.2023.10323652 · Cited by 1

Method for Expanding Search Space With Hybrid Operations in DynamicNAS

Shin, I., Cho, C.-S., & Kim, S.-T. (2024) · IEEE Access · doi:10.1109/access.2024.3350732 · Cited by 1

Beyond the Limit of Weight-Sharing: Pioneering Space-Evolving NAS with Large Language Models

Su, X. Q., You, S., Xu, H., Li, X., Long, J., Chen, Y., & Xu, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448075 · Cited by 3

To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process

Sun, Z., Si, Z., Zhang, X., Zang, X., Song, Y., Xu, H., & Xu, J. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657732 · Cited by 2

A Novel Multi-Dimensional Joint Search Method for the Compression of Medical Image Segmentation Models

Zheng, Y., Wu, Z., Ji, F., Du, L., & Yang, Z. (2024) · Journal of Imaging · doi:10.3390/jimaging10090206 · Cited by 1

Pareto-Informed Multi-objective Neural Architecture Search

Luo, G., Li, H., Chen, Z., & Zhou, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70071-2_23

SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model

Li, Z., Yan, K., Liu, Y., Liu, D., Hinz, T., Liu, F., & Wang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00827 · Cited by 1

A Crowdsourcing-Driven AI Model Design Framework to Public Health Policy-Adherence Assessment

Zhang, Y., Zong, R., Shang, L., & Wang, D. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3496835 · Cited by 1

A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data

Tan, Y. H., Wu, J., Liu, Y., Shen, S., Xu, X., & Pan, B. (2024) · Remote Sensing · doi:10.3390/rs16234545 · Cited by 1

Evolutionary Neural Architecture Search Framework With Masked Encoding Mechanism for Point Cloud Registration

Wu, Y., Gong, P., Yuan, Y., Gong, M., Ma, W., & Miao, Q. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3526279 · Cited by 1

MS-GATformer:Multi-Scale Hybrid Prediction Based on GAT-iTransformer

Ren, Y., Xiong, Q., Qu, C., Xiang, G., Nie, Y., & Yang, F. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865793 · Cited by 1

The future of action recognition: are multi-modal visual language models the key?

Gümüşkaynak, E., & Eken, S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03951-w · Cited by 3

SiMBA-TS: Simplified Channel Mixing and Mamba for Long-term Time Series Forecasting

Patro, B. N., & Agneeswaran, V. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890315 · Cited by 1

TDFormer: Top-Down Token Generation for 3D Medical Image Segmentation

Du, H., Dong, Q., Xu, Y. L., & Liao, J. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3567590 · Cited by 1

An Interactive Attention Mechanism Network Integrating the C¹ Activation Function for Time Series Forecasting

Yuan, L., Wang, H., & Zhang, F. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3585257 · Cited by 1

Accelerating Zero-Shot NAS With Feature Map-Based Proxy and Operation Scoring Function

Jiang, T., Wang, H., Bie, R., & Yuan, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3590342 · Cited by 1

Efficient Training of Large Vision Models via Advanced Automated Progressive Learning

Li, C., Zhang, J., Lin, S., Yang, Z., Liang, J., Liang, X., & Chang, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3673336 · Cited by 5

ElegansNet : a brief scientific report and initial experimentsPreprint

Bardozzo, F., Terlizzi, A., Lió, P., & Tagliaferri, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.23.537987 · Cited by 1

ElegansAI: how a biological neural network would compare with artificial networks?Preprint

Bardozzo, F., Terlizzi, A., Lió, P., & Tagliaferri, R. (2023) · Research Square · doi:10.21203/rs.3.rs-3005708/v1 · Cited by 1

Exploring Complicated Search Spaces With Interleaving-Free Sampling

Tian, Y., Xie, L., Fang, J., Jiao, J., Ye, Q., & Tian, Q. C. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3408329

A survey: researches advanced in weakly supervised fine-grained visual classification

Ran, N. (2022) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1117/12.2641933

Weight-Sharing Supernet for Searching Specialized Acoustic Event Classification Networks Across Device Constraints

Lin, G.-T., Tang, Q., Kao, C.-C., Rozgić, V., & Wang, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096117

ElegansAI: how a biological neural network would compare with artificial networks?Preprint

Bardozzo, F., Terlizzi, A., Lió, P., & Tagliaferri, R. (2023) · Research Square · doi:10.21203/rs.3.rs-3005708/v2

Sparse Gate for Differentiable Architecture Search

Fan, L., & Wang, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191908

TAQ: Top-K Attention-Aware Quantization for Vision Transformers

Shi, L., Huang, H., Song, B., Tan, M., Zhao, W., Xia, T., & Ren, P. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222721

AEformer: Asymmetric Embedding Transformer for Stock Market Prediction based on Investor SentimentPreprint

Jiang, L., Yue, S., Zhang, M., & Zhang, F. (2023) · Research Square · doi:10.21203/rs.3.rs-3344960/v1

Towards Effective Neural Architecture Selection: The Promise of Self-AttentionPreprint

Sun, Z., Hu, Y., Yang, L., Lu, S., Mei, J., & Han, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4634144

Plug n’ Play Channel Shuffle Module for Enhancing Tiny Vision Transformers

Xu, X., Wang, S., Chen, Y., & Liu, J. (2023) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta60407.2023.00066

Deploying a Machine Translation Model on a Mobile Device with Improved Latency Constraints

Chang, W.-C., Liu, A., & Wang, H.-Y. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394343

Updates and Experiences of VenusAI Platform

Wan, M., Cao, R., Li, K., Wang, X., Wang, Z., Wang, J., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9119-8_48

Research on industrial risk early warning algorithms based on edge computing and multimodal data

Su, Q., Wang, H., Zhao, H., Chu, Y., Li, J., Lyu, X., & Li, Z. (2024) · Electronics Letters · doi:10.1049/ell2.13109

TCNAS: Transformer Architecture Evolving in Code Clone Detection

Xu, H., Pei, X., Su, X. Q., You, S., & Xu, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10445958

Advancing palmprint recognition technology through vision transformer integration

he, J., Jia, W., & Yang, J. (2024) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1117/12.3034744

Beyond Words: Adapting NLP Methodologies for Time Series Forecasting Challenges

Dalal, L., Verma, R., & Chahar, R. (2024) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit61985.2024.10627058

Multi-dimensional Search with Strip Convolution and R-Squared Loss for Lane Detection

Lin, Z., Ye, P., Chen, T., & Tang, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650216

NAS-Bench-Compre: A Comprehensive Neural Architecture Search Benchmark with Customizable Components

Wang, D., Jing, K., & Xu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72332-2_19

WaterTS: Integrating Enhanced Transformer, Sliding Block, and Channel Independence for Long-term Water Quality Prediction

Bi, J., Xu, L., Wang, Z., Yuan, H., Chen, S., Gu, M., & Zhou, M. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711632

Graph-Based Modeling for Multi-input Temperature Prediction in Power Plant

Gang, Y., Peng, C., & Cheng, C. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0225-4_30

Cream Strategy and Its Crucial Role In Foreign Language Learning

Mamatkulova, N. (2024) · Jurnal ISO Jurnal Ilmu Sosial Politik dan Humaniora · doi:10.53697/iso.v4i2.2166

RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism

Yu, H., Wang, L., Xu, W., Mi, S., & Zhang, Y. (2025) · Applied Sciences · doi:10.3390/app15031326

WSSNet: A Multivariate Time Series Forecasting Framework Incorporating Serial Correlations

Liu, H., Hu, F., Su, Z., Dai, J., Yu, H. Z., & Zhou, Y. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865509

Novel Hybrid Deep Learning Model for Enhancing Skin Cancer Detection

Smida, J., Azizi, M. K., Bose, A. S. C., & Smida, A. (2025) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas63548.2025.10924546

RetNAS: Reinforce Evolutionary Trend in Lightweight Neural Architecture Search

Xiao, M., Li, S., Zhang, L., & Pan, G. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00107

TAFL: Task-Agnostic Feature Learner for Efficient Adaptation to Unseen Clinical Tasks Based on Whole-Slide Histopathological Images

Zuo, Y., Wang, L., Liu, J., Feng, S., Wang, Y., Zhu, Q., Shao, W., & Zhang, D. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3575801

Beyond Multi-Tower Approaches: Unified Cross-Modal Attention Mechanisms for Multimodal Foundation ModelsPreprint

Kodela, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5360155

Dual Cuts for Efficiency: Streamlining Nas with Train-Time Pruning of Supernet and Search SpacePreprint

NIU, D., Jiang, J., Pan, H., Xu, J., Niu, X., & Lv, Q. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5387391

Hierarchical Patch Based Transformer with Learnable Weighted Loss Optimization for Multivariate Time Series ForecastingPreprint

Chakraborty, S., Delibasoglu, I., & Heintz, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5400868

Efficient Tuning of Vision Foundation Models with Neural Prompt Search

Zhang, Y., Zhou, K., & Liu, Z. (2025) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-031-94969-2_8

Weight-Sharing NAS with Architecture-Agnostic Intermediate Representation

Yu, S., Mazaheri, A., & Jannesari, A. (2025) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3731545.3731587

Outlier detection on lithium battery cells via patched inpainting convolution

Wang, Y., Bai, X., Liu, Z., & Tan, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117766

Efficient Time Series Forecasting with MSPA for Smart Government Big Data Platforms

Song, W., Zuo, E., Yan, Z., & Zhu, J. (2025) · International Conference on Modeling, Natural Language Processing and Machine Learning (CMNM) · doi:10.1145/3757110.3757196

EMC-LSP: A Novel Lightweight Architecture for Edge Multi-Node Long Sequence Prediction

Xiong, C., Yang, J., Zhong, J., He, Z., Pang, P., & Guo, M. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3620441

A Survey of Deep Model Compression and Acceleration

Zhang, C., Liu, H., Wang, H., Wang, J., Zheng, S., Meng, X., & Zhu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-1346-8_6

Efficient Zero-Cost Neural Architecture Search for Personalized AI Systems in Cloud-Edge Networks

Huang, K., Mao, Y., Zhang, B., Chen, Y., Chen, Y., & Wu, J. (2025) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass66014.2025.00063

KUformer: KAN-UKF-Based Transformer for Battery SoH Prediction

Zhang, J., Dai, X., Zhou, R., Zhang, W., & Ma, H. (2025) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon58223.2025.11221982

Transformer-PLM Enhanced Multimodal Time Series Forecasting via Decoupled Dual-Temporal Graph Adaptation

Lei, J., Zhang, W., Yang, Q., Zhang, X., & Tang, H. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3630087

Generalization-Aware Zero-Shot Neural Architecture Search for Self-Supervised Transformers

Ko, J.-H., & Chiueh, T. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229357

Multiscale Attention Knowledge Distillation for Object Detection

Zhang, F. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227248

Neural Architecture Generation via Contrastive Representation Learning

Xie, J., Feng, Y., Zhong, M., & Sun, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229050

An adaptive outlier correction quantization method for vision Transformers

Li, Z., Lan, C., Zhang, K., Tan, W., Ren, Y. T., & Xiao, J. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2400994

Token Compression Meets Compact Vision Transformers: a Survey and Comparative Evaluation for Edge AI

Nguyen, P., & Cheung, N. M. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249396 · Cited by 3

Hybrid CNN-Transformer and YOLOv8-Based Robust Real-Time Object Detection for Autonomous Vehicles

Marri, M. S., & Monica, P. (2025) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact65952.2025.11307825

Vision Transformer Architecture SearchPreprint

Su, X. Q., You, S., Xie, J., Zheng, M., Wang, F., Chen, Q., Zhang, C., Wang, X., & Xu, C. (2021) · arXiv (Cornell University) · Cited by 10

Searching the Search Space of Vision TransformerPreprint

Chen, M., Wu, K., Ni, B., Peng, H., Liu, B., Fu, J., Chao, H., & Ling, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.14725 · Cited by 35

NASformer: Neural architecture search for vision transformer

Ni, B., Meng, G., Xiang, S., & Pan, C. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-02375-0_4 · Cited by 9

AutoTaskFormer: Searching Vision Transformers for Multi-task LearningPreprint

Liu, Y., Yan, S., Zhang, Y., Ren, K., Zhang, Q., Ren, Z., Cai, D., & Zhang, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.08756

ViTAS: Vision Transformer Architecture Search

Su, X. Q., You, S., Xie, J., Zheng, M., Wang, F., Qian, C., Zhang, C., Wang, X., & Xu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_9 · Cited by 94