Learning Multiple Layers of Features from Tiny Images

WordNet: a lexical database for English

Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015

Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification

Li, C., Stevens, A., Chen, C., Pu, Y., Gan, Z., & Carin, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.611 · Cited by 39

Slow and Stale Gradients Can Win the Race

Dutta, S., Wang, J., & Joshi, G. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3103770 · Cited by 40

Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network

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Genetic Neural Architecture Search for automatic assessment of human sperm images

Miahi, E., Mirroshandel, S. A., & Nasr, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115937 · Cited by 39

CKConv: Continuous Kernel Convolution For Sequential Data

Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2021) · arXiv · doi:10.48550/arxiv.2102.02611 · Cited by 39

Understanding Self-attention Mechanism via Dynamical System Perspective

Huang, Z., Liang, M., Qin, J., Zhong, S., & Lin, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00136 · Cited by 44

Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack

Wang, Y., Li, J., Liu, H., Wang, Y., Wu, Y., Huang, F., & Ji, R. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20065-6_12 · Cited by 40

A Strategy for an Uncompromising Incremental Learner

Venkatesan, R., Venkateswara, H., Panchanathan, S., & Li, B. (2017) · arXiv · doi:10.48550/arxiv.1705.00744 · Cited by 37

Being Bayesian about Categorical Probability

Joo, T., Chung, U., & Seo, M.-G. (2020) · arXiv · doi:10.48550/arxiv.2002.07965 · Cited by 37

Untitled work

(2021) · doi:10.1145/3474369.3486863 · Cited by 37

Interpretable-through-prototypes deepfake detection for diffusion models

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Extending and Analyzing Self-supervised Learning Across Domains

Wallace, B., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_43 · Cited by 35

Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

Aguirre, F. L., Pazos, S. M., Palumbo, F., Sune, J., & Miranda, E. (2020) · IEEE Access · doi:10.1109/access.2020.3035638 · Cited by 36

CrowdGuard: Federated Backdoor Detection in Federated Learning

Rieger, P., Krauß, T., Miettinen, M., Dmitrienko, A., & Sadeghi, A.-R. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23233 · Cited by 36

Training Binary Neural Networks through Learning with Noisy Supervision

Han, K., Wang, Y., Xu, Y., Xu, C., Wu, E., & Xu, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04871 · Cited by 36

Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

Fan, M., Ji, K., Zhang, Z., Yu, H., & Sun, G. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3267112 · Cited by 37

A Non-Idealities Aware Software–Hardware Co-Design Framework for Edge-AI Deep Neural Network Implemented on Memristive Crossbar

Cao, T., Liu, C., Wang, W., Zhang, T., Lee, H. K., Li, M. H., Song, W., Chen, Z. X., Zhuo, V. Y.-Q., Wang, N., et al. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3214334 · Cited by 34

EQ-ViT: Algorithm-Hardware Co-Design for End-to-End Acceleration of Real-Time Vision Transformer Inference on Versal ACAP Architecture

Dong, P., Zhuang, J., Yang, Z., Ji, S., Li, Y., Xu, D., Huang, H., Hu, J., Jones, A. K., Shi, Y., et al. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443692 · Cited by 35

Layer-Based Communication-Efficient Federated Learning with Privacy Preservation

LIAN, Z., WANG, W., HUANG, H., & SU, C. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021bcp0006 · Cited by 33

Efficient Exact Verification of Binarized Neural Networks

Jia, K., & Rinard, M. (2020) · arXiv · doi:10.48550/arxiv.2005.03597 · Cited by 33

Distribution-aware Adaptive Multi-bit Quantization

Zhao, S., Yue, T., & Hu, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00916 · Cited by 33

On the Outsized Importance of Learning Rates in Local Update Methods

Charles, Z., & Konečný, J. (2020) · arXiv · doi:10.48550/arxiv.2007.00878 · Cited by 32

Natural statistics support a rational account of confidence biases

Webb, T. W., Miyoshi, K., So, T. Y., Rajananda, S., & Lau, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39737-2 · Cited by 31

Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction

Cauchois, M., Gupta, S., & Duchi, J. (2020) · arXiv · doi:10.48550/arxiv.2004.10181 · Cited by 31

On the Decision Boundary of Deep Neural Networks

Li, Y., Ding, L., & Gao, X. (2018) · arXiv · doi:10.48550/arxiv.1808.05385 · Cited by 31

Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?

Xiong, Y., Mehta, R., & Singh, V. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00199 · Cited by 31

NAS4RRAM: neural network architecture search for inference on RRAM-based accelerators

Yuan, Z., Liu, J., Li, X., Yan, L., Chen, H., Wu, B., Yang, Y., & Sun, G. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-3245-7 · Cited by 30

Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

Rajotte, J.-F., Mukherjee, S., Robinson, C., Ortiz, A., West, C., Ferres, J. M. L., & Ng, R. T. (2021) · Proceedings of the Conference on Information Technology for Social Good · doi:10.1145/3462203.3475875 · Cited by 34

DeAR: Debiasing Vision-Language Models with Additive Residuals

Seth, A., Hemani, M., & Agarwal, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00659 · Cited by 34

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

Zhang, Y., Song, Y., Liang, J., Bai, K., & Yang, Q. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403349 · Cited by 29

Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis

Sun, Y., Ochiai, H., & Sakuma, J. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891990 · Cited by 28

Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification

Valdenegro-Toro, M. (2019) · arXiv · doi:10.48550/arxiv.1910.08168 · Cited by 27

Combining Ensembles and Data Augmentation can Harm your Calibration

Wen, Y., Jerfel, G., Muller, R., Dusenberry, M. W., Snoek, J., Lakshminarayanan, B., & Tran, D. (2020) · arXiv · doi:10.48550/arxiv.2010.09875 · Cited by 27

Contrasting Contrastive Self-Supervised Representation Learning Pipelines

Kotar, K., Ilharco, G., Schmidt, L., Ehsani, K., & Mottaghi, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00980 · Cited by 28

Encryption Inspired Adversarial Defense For Visual Classification

Maung, M., Pyone, A., & Kiya, H. (2020) · 2020 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip40778.2020.9190904 · Cited by 26

Learning to Ground Multi-Agent Communication with Autoencoders

Lin, T., Huh, M., Stauffer, C., Lim, S.-N., & Isola, P. (2021) · arXiv · doi:10.48550/arxiv.2110.15349 · Cited by 26

SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells

Kumar, D., Sankar, V., Clausi, D., Taylor, G. W., & Wong, A. (2019) · IEEE Access · doi:10.1109/access.2019.2945524 · Cited by 26

Towards Standardization of Data Licenses: The Montreal Data License

Benjamin, M., Gagnon, P., Rostamzadeh, N., Pal, C., Bengio, Y., & Shee, A. (2019) · arXiv · doi:10.48550/arxiv.1903.12262 · Cited by 26

An adaptive robust defending algorithm against backdoor attacks in federated learning

Wang, Y., Zhai, D.-H., He, Y., & Xia, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.026 · Cited by 25

Transferability-Guided Cross-Domain Cross-Task Transfer Learning

Tan, Y., Zhang, E., Li, Y., Huang, S.-L., & Zhang, X.-P. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3358094 · Cited by 24

CUDA: Convolution-Based Unlearnable Datasets

Sadasivan, V. S., Soltanolkotabi, M., & Feizi, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00376 · Cited by 24

Siamese Networks for Few-Shot Learning on Edge Embedded Devices

Lungu, I. A., Aimar, A., Hu, Y., Delbruck, T., & Liu, S.-C. (2020) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2020.3033155 · Cited by 24

From Data Quality to Model Quality: An Exploratory Study on Deep Learning

He, T., Yu, S., Wang, Z., Li, J., & Chen, Z. (2019) · Proceedings of the 11th Asia-Pacific Symposium on Internetware · doi:10.1145/3361242.3361260 · Cited by 24

Structured Model Pruning of Convolutional Networks on Tensor Processing Units

Chen, K., Franko, K., & Sang, R. (2021) · arXiv · doi:10.48550/arxiv.2107.04191 · Cited by 22

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

Liang, P. P., Wu, P., Ziyin, L., Morency, L.-P., & Salakhutdinov, R. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475247 · Cited by 22

How Well Do Sparse ImageNet Models Transfer?

Iofinova, E., Peste, A., Kurtz, M., & Alistarh, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01195 · Cited by 22

Listen to the Image

Hu, D., Wang, D., Li, X., Nie, F., & Wang, Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00816 · Cited by 21

MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

do Santos, C. F. G., Colombo, D., Roder, M., & Papa, J. P. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412733 · Cited by 20

FTT-NAS: Discovering Fault-tolerant Convolutional Neural Architecture

Ning, X., Ge, G., Li, W., Zhu, Z., Zheng, Y., Chen, X., Gao, Z., Wang, Y., & Yang, H. (2021) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3460288 · Cited by 20

Few-Shot Class-Incremental Learning with Meta-Learned Class Structures

Zheng, G., & Zhang, A. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00058 · Cited by 19

Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data

Liu, X., & Tsaftaris, S. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_10 · Cited by 18

Improving Lightweight Convolutional Neural Network for Facial Expression Recognition via Transfer Learning

Wikanningrum, A., Rachmadi, R. F., & Ogata, K. (2019) · 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) · doi:10.1109/cenim48368.2019.8973312 · Cited by 17

Exploring the Transferability of Visual Prompting for Multimodal Large Language Models

Zhang, Y., Dong, Y., Zhang, S., Min, T., Su, H., & Zhu, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02508 · Cited by 17

Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning

Igl, M., Farquhar, G., Luketina, J., Boehmer, W., & Whiteson, S. (2020) · arXiv · doi:10.48550/arxiv.2006.05826 · Cited by 17

Anomaly Detection Based on Selection and Weighting in Latent Space

Liao, Y., Bartler, A., & Yang, B. (2021) · 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551267 · Cited by 17

On Training Implicit Models

Geng, Z., Zhang, X.-Y., Bai, S., Wang, Y., & Lin, Z. (2021) · arXiv · doi:10.48550/arxiv.2111.05177 · Cited by 17

LT-GAN: Self-Supervised GAN with Latent Transformation Detection

Patel, P., Kumari, N., Singh, M., & Krishnamurthy, B. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00323 · Cited by 18

Semi-Supervised Few-Shot Class-Incremental Learning

Cui, Y., Xiong, W., Tavakolian, M., & Liu, L. (2021) · 2021 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip42928.2021.9506346 · Cited by 16

Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments

Queyrut, S., Schiavoni, V., & Felber, P. (2023) · 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00069 · Cited by 18

Hold me tight! Influence of discriminative features on deep network boundaries

Ortiz-Jimenez, G., Modas, A., Moosavi-Dezfooli, S.-M., & Frossard, P. (2020) · arXiv · doi:10.48550/arxiv.2002.06349 · Cited by 15

Open-world continual learning: Unifying novelty detection and continual learning

Kim, G., Xiao, C., Konishi, T., Ke, Z., & Liu, B. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104237 · Cited by 19

Automating Analogue AI Chip Design with Genetic Search

Krestinskaya, O., Salama, K. N., & James, A. P. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000075 · Cited by 14

Self-Net: Lifelong Learning via Continual Self-Modeling

Mandivarapu, J. K., Camp, B., & Estrada, R. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00019 · Cited by 14

Digest: Fast and communication efficient decentralized learning with local updates

Gholami, P., & Seferoglu, H. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3354236 · Cited by 14

Interference and Generalization in Temporal Difference Learning

Bengio, E., Pineau, J., & Precup, D. (2020) · arXiv · doi:10.48550/arxiv.2003.06350 · Cited by 14

RaScaNet: Learning Tiny Models by Raster-Scanning Images

Yoo, J., Lee, D., Son, C., Jung, S., Yoo, B., Choi, C., Han, J.-J., & Han, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01346 · Cited by 14

RAPA-ConvNets: Modified Convolutional Networks for Accelerated Training on Architectures With Analog Arrays

Rasch, M. J., Gokmen, T., Rigotti, M., & Haensch, W. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00753 · Cited by 14

Deep Learning as a Mixed Convex-Combinatorial Optimization Problem

Friesen, A. L., & Domingos, P. (2017) · arXiv · doi:10.48550/arxiv.1710.11573 · Cited by 14

6G Goal-Oriented Communications: How to Coexist with Legacy Systems?

(2024) · doi:10.3390/telecom5010005 · Cited by 14

Deep Semi-Supervised Learning

Hailat, Z., Komarichev, A., & Chen, X.-W. (2018) · 2018 24th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.2018.8546327 · Cited by 13

LaProp: Separating Momentum and Adaptivity in Adam

Ziyin, L., Wang, Z. T., & Ueda, M. (2020) · arXiv · doi:10.48550/arxiv.2002.04839 · Cited by 13

Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks

Mejia, F. A., Gamble, P., Hampel-Arias, Z., Lomnitz, M., Lopatina, N., Tindall, L., & Barrios, M. A. (2019) · arXiv · doi:10.48550/arxiv.1906.06449 · Cited by 13

Toward Cleansing Backdoored Neural Networks in Federated Learning

Wu, C., Yang, X., Zhu, S., & Mitra, P. (2022) · 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00084 · Cited by 12

Variance Networks: When Expectation Does Not Meet Your Expectations

Neklyudov, K., Molchanov, D., Ashukha, A., & Vetrov, D. (2018) · arXiv · doi:10.48550/arxiv.1803.03764 · Cited by 12

Calibration of Model Uncertainty for Dropout Variational Inference

Laves, M.-H., Ihler, S., Kortmann, K.-P., & Ortmaier, T. (2020) · arXiv · doi:10.48550/arxiv.2006.11584 · Cited by 11

Model Similarity Mitigates Test Set Overuse

Mania, H., Miller, J., Schmidt, L., Hardt, M., & Recht, B. (2019) · arXiv · doi:10.48550/arxiv.1905.12580 · Cited by 11

Untitled work

(2021) · arXiv (Cornell University) · Cited by 11

Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks

Li, X., Xiang, Z., Miller, D. J., & Kesidis, G. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746573 · Cited by 11

Neural networks memorise personal information from one sample

Hartley, J., Sanchez, P. P., Haider, F., & Tsaftaris, S. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-48034-3 · Cited by 10

A Note on Deepfake Detection with Low-Resources

Kawa, P., & Syga, P. (2020) · arXiv · doi:10.48550/arxiv.2006.05183 · Cited by 10

Learning to Defer with Limited Expert Predictions

Hemmer, P., Thede, L., Vössing, M., Jakubik, J., & Kühl, N. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25742 · Cited by 10

Progressive Learning of Low-Precision Networks for Image Classification

Zhou, Z., Zhou, W., Lv, X., Huang, X., Wang, X., & Li, H. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2990087 · Cited by 10

REVEL Framework to Measure Local Linear Explanations for Black‐Box Models: Deep Learning Image Classification Case Study

Sevillano-García, I., Luengo, J., & Herrera, F. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8068569 · Cited by 10

BoolNet: Minimizing The Energy Consumption of Binary Neural Networks

Guo, N., Bethge, J., Yang, H., Zhong, K., Ning, X., Meinel, C., & Wang, Y. (2021) · arXiv · doi:10.48550/arxiv.2106.06991 · Cited by 10

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Dao, T., Chen, B., Liang, K., Yang, J., Song, Z., Rudra, A., & Ré, C. (2021) · arXiv · doi:10.48550/arxiv.2112.00029 · Cited by 10

Defending local poisoning attacks in multi-party learning via immune system

Xie, F., Gao, Y., Wang, J., & Zhao, W. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107850 · Cited by 9

Pruning from Scratch

Wang, Y., Zhang, X., Xie, L., Zhou, J., Su, H., Zhang, B., & Hu, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6910 · Cited by 8

Private Prediction Sets

Angelopoulos, A. N., Bates, S., Zrnic, T., & Jordan, M. I. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.16c71dad · Cited by 8

Learning Versatile Convolution Filters for Efficient Visual Recognition

Han, K., Wang, Y., Xu, C., Xu, C., Wu, E., & Tao, D. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3114368 · Cited by 8

Feature Distribution Distillation-Based Few Shot Class Incremental Learning

Zhu, J., Yao, G., Zhou, W., Zhang, G., Ping, W., & Zhang, W. (2022) · 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904282 · Cited by 6

Untitled work

(2018) · arXiv (Cornell University) · Cited by 6

Few-shot class-incremental learning based on representation enhancement

Yao, G., Zhu, J., Zhou, W., & Li, J. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.4.043027 · Cited by 6

TBAL: Two-stage batch-mode active learning for image classification

Shen, Y., Song, Y., Wu, C.-H., & Kuo, C.-C. J. (2022) · Signal Processing: Image Communication · doi:10.1016/j.image.2022.116731 · Cited by 6

Amnesiac Machine Learning

Graves, L., Nagisetty, V., & Ganesh, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17371 · Cited by 5

A novel connectivity of deep convolutional neural networks

Shen, Z., & Liu, Y. (2017) · 2017 Chinese Automation Congress (CAC) · doi:10.1109/cac.2017.8244187 · Cited by 5

Neural Activity Shaping in Visual Prostheses with Deep Learning

Castro, D., Grayden, D. B., Meffin, H., & Spencer, M. (2023) · doi:10.1101/2023.12.20.572123 · Cited by 4

The potential of vision-language models for content moderation of children’s videos

Ahmed, S. H., Hu, S., & Sukthankar, G. (2023) · 2023 International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla58977.2023.00186 · Cited by 4

Pulmonary disease classification using globally correlated maximum likelihood: An auxiliary attention mechanism for convolutional neural networks

Verenich, E., Martin, T., Velasquez, A., Khan, N., & Hussain, F. (2021) · arXiv · doi:10.48550/arxiv.2109.00573 · Cited by 4

Why do classifier accuracies show linear trends under distribution shift?

Mania, H., & Sra, S. (2020) · arXiv · doi:10.48550/arxiv.2012.15483 · Cited by 3

SimClone: Detecting Tabular Data Clones Using Value Similarity

Yang, X., Rajbahadur, G. K., Lin, D., Wang, S., & Jiang, Z. M. (. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3676961 · Cited by 3