Deep Fried Convnets

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 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

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 · see citing works

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

How transferable are features in deep neural networks?

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works

Approximate nearest neighbors: towards removing the curse of dimensionality

Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188 · see citing works

Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811 · see citing works

FitNets: Hints for Thin Deep Nets

Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.6550 · Cited by 2,859 · see citing works

Random Features for Large-Scale Kernel Machines

Rahimi, A., & Recht, B. (2007) · Cited by 2,783 · see citing works

Probability and computing: randomized algorithms and probabilistic analysis

Mitzenmacher, M., & Upfal, E. (2005) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,892 · see citing works

Probability and Computing

Mitzenmacher, M., & Upfal, E. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511813603 · Cited by 1,739 · see citing works

Learning both Weights and Connections for Efficient Neural Networks

Han, S., Pool, J., Tran, J., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1506.02626 · Cited by 1,481 · see citing works

One weird trick for parallelizing convolutional neural networksPreprint

Krizhevsky, A. (2014) · arXiv.org · doi:10.48550/arxiv.1404.5997 · Cited by 1,435 · see citing works

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Denton, R., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1404.0736 · Cited by 1,414 · see citing works

Database-friendly random projections: Johnson-Lindenstrauss with binary coins

Achlioptas, D. (2003) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(03)00025-4 · Cited by 1,374 · see citing works

Weight Uncertainty in Neural Networks

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1505.05424 · Cited by 1,342 · see citing works

Finding frequent items in data streams

Charikar, M., Chen, K., & Farach-Colton, M. (2003) · Theoretical Computer Science · doi:10.1016/s0304-3975(03)00400-6 · Cited by 1,325 · see citing works

Weight Uncertainty in Neural Network

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · International Conference on Machine Learning · Cited by 1,280 · see citing works

Speeding up Convolutional Neural Networks with Low Rank Expansions

Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.88 · Cited by 1,164 · see citing works

Compressing Neural Networks with the Hashing Trick

Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv · doi:10.48550/arxiv.1504.04788 · Cited by 974 · see citing works

Untitled work

(2014) · arXiv (Cornell University) · Cited by 942 · see citing works

Feature hashing for large scale multitask learning

Weinberger, K. Q., Dasgupta, A., Langford, J. C., Smola, A. J., & Attenberg, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553516 · Cited by 928 · see citing works

Predicting Parameters in Deep Learning

Denil, M., Shakibi, B., Dinh, L., Ranzato, M., & de Freitas, N. (2013) · arXiv · doi:10.48550/arxiv.1306.0543 · Cited by 853 · see citing works

Sparse Convolutional Neural Networks

Liu, B., Wang, M., Foroosh, H., Tappen, M. F., & Penksy, M. (2015) · 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/cvpr.2015.7298681 · Cited by 750 · see citing works

Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., & Ramabhadran, B. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638949 · Cited by 593 · see citing works

Kernel Methods for Deep Learning

Cho, Y., & Saul, L. K. (2009) · eScholarship (California Digital Library) · Cited by 533 · see citing works

The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors

Ailon, N., & Chazelle, B. (2009) · SIAM Journal on Computing · doi:10.1137/060673096 · Cited by 473 · see citing works

Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches

Cormode, G., Garofalakis, M., Haas, P. J., & Jermaine, C. (2011) · Foundations and Trends in Databases · doi:10.1561/1900000004 · Cited by 472 · see citing works

Predicting Parameters in Deep Learning

Shakibi, B. (2014) · University of British Columbia · doi:10.14288/1.0165555 · Cited by 462 · see citing works

Restructuring of deep neural network acoustic models with singular value decomposition

Xue, J., Li, J., & Gong, Y. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-552 · Cited by 433 · see citing works

On Random Weights and Unsupervised Feature Learning

Saxe, A., Koh, P. W., Chen, Z., Bhand, M., Suresh, B., & Ng, A. Y. (2011) · Cited by 353 · see citing works

Fastfood: approximating kernel expansions in loglinear time

Le, Q. V., Sarlós, T., & Smola, A. J. (2013) · Cited by 337 · see citing works

Convolutional Kernel NetworksPreprint

Mairal, J., Koniusz, P., Harchaoui, Z., & Schmid, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.3332 · Cited by 258 · see citing works

Hardware accelerated convolutional neural networks for synthetic vision systems

Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., & Culurciello, E. (2010) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2010.5537908 · Cited by 233 · see citing works

Memory Bounded Deep Convolutional NetworksPreprint

Collins, M. D., & Kohli, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.1442 · Cited by 137 · see citing works

Kernel methods match Deep Neural Networks on TIMIT

Huang, P.-S., Avron, H., Sainath, T. N., Sindhwani, V., & Ramabhadran, B. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853587 · Cited by 112 · see citing works

Scalable Kernel Methods via Doubly Stochastic Gradients

Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M.-F., & Song, L. (2018) · doi:10.1184/r1/6476315.v1 · Cited by 89 · see citing works

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise ClassificationPreprint

Li, H., Zhao, R., & Wang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4526 · Cited by 66 · see citing works

Learning by Stretching Deep Networks

Pandey, G. S., & Dukkipati, A. (2014) · Cited by 20 · see citing works

Speeding Up Neural Networks for Large Scale Classification using WTA HashingPreprint

Bakhtiary, A. H., Lapedriza, À., & Masip, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07488 · Cited by 8 · see citing works

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces

Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · see citing works

Quantized Convolutional Neural Networks for Mobile Devices

Wu, J., Leng, C., Wang, Y., Hu, Q., & Cheng, J. (2016) · 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/cvpr.2016.521 · Cited by 1,244 · see citing works

Compact Bilinear Pooling

Gao, Y., Beijbom, O., Zhang, N., & Darrell, T. (2016) · 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/cvpr.2016.41 · Cited by 912 · see citing works

A Survey of Model Compression and Acceleration for Deep Neural Networks

Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2017) · arXiv · doi:10.48550/arxiv.1710.09282 · Cited by 857 · see citing works

NISP: Pruning Networks Using Neuron Importance Score Propagation

Yu, R., Li, A., Chen, C.-F., Lai, J.-H., Morariu, V. I., Han, X., Gao, M., Lin, C., & Davis, L. S. (2018) · 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/cvpr.2018.00958 · Cited by 825 · see citing works

Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges

Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765695 · Cited by 817 · see citing works

Tensorizing Neural NetworksPreprint

Novikov, A., Podoprikhin, D., Osokin, A., & Vetrov, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.06569 · Cited by 656 · see citing works

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Yang, T.-J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_18 · Cited by 572 · see citing works

A comprehensive survey on model compression and acceleration

Choudhary, T., Mishra, V. K., Goswami, A., & Jagannathan, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09816-7 · Cited by 519 · see citing works

Axiom: A Householder-Parameterized Pure Unitary RNN for Long-Range Sequence ModelingPreprint

Arjovsky, M., Shah, A., & Bengio, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06464 · Cited by 510 · see citing works

Communication-efficient edge ai: algorithms and systems

Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3007787 · Cited by 488 · see citing works

Recent advances in convolutional neural network acceleration

Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., & Yu, B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.038 · Cited by 440 · see citing works

Large-Scale Study of Curiosity-Driven Learning

Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., & Efros, A. A. (2018) · arXiv · doi:10.48550/arxiv.1808.04355 · Cited by 401 · see citing works

Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear UnitsPreprint

Shang, W., Sohn, K., Almeida, D., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05201 · Cited by 397 · see citing works

An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

Cheng, Y., Yu, F. X., Feris, R., Kumar, S., Choudhary, A., & Chang, S.-F. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.327 · Cited by 302 · see citing works

More is Less: A More Complicated Network with Less Inference Complexity

Dong, X., Huang, J., Yang, Y., & Yan, S. (2017) · 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/cvpr.2017.205 · Cited by 305 · see citing works

A Survey of Handwritten Character Recognition with MNIST and EMNIST

Baldominos, A., Saez, Y., & Isasi, P. (2019) · Applied Sciences · doi:10.3390/app9153169 · Cited by 279 · see citing works

Deconstructing Lottery Tickets: Zeros, Signs, and the SupermaskPreprint

Zhou, H., Lan, J., Liu, R., & Yosinski, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01067 · Cited by 242 · see citing works

CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization

Tung, F., & Mori, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00821 · Cited by 220 · see citing works

NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

Dai, X., Yin, H., & Jha, N. K. (2019) · IEEE Transactions on Computers · doi:10.1109/tc.2019.2914438 · Cited by 233 · see citing works

Clockwork Convnets for Video Semantic Segmentation

Shelhamer, E., Rakelly, K., Hoffman, J., & Darrell, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_69 · Cited by 204 · see citing works

Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization

Cai, S., Zuo, W., & Lei Zhang, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.63 · Cited by 207 · see citing works

Structured Transforms for Small-Footprint Deep LearningPreprint

Sindhwani, V., Sainath, T. N., & Kumar, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.01722 · Cited by 200 · see citing works

Training Sparse Neural Networks

Srinivas, S., Subramanya, A., & Babu, R. V. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.61 · Cited by 179 · see citing works

Evolutionary convolutional neural networks: An application to handwriting recognition

Baldominos, A., Sáez, Y., & Isasi, P. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.049 · Cited by 175 · see citing works

Few-Shot Learning With Localization in Realistic Settings

Wertheimer, D., & Hariharan, B. (2019) · 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/cvpr.2019.00672 · Cited by 174 · see citing works

Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer

Lin, S., Ji, R., Chen, C., Tao, D., & Luo, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873305 · Cited by 165 · see citing works

Deep Neural Network Approximation for Custom Hardware

Wang, E., Davis, J. J., Zhao, R., Ng, H.-C., Niu, X., Luk, W. W., Cheung, P. Y. K., & Constantinides, G. A. (2019) · ACM Computing Surveys · doi:10.1145/3309551 · Cited by 166 · see citing works

Deep Learning on Mobile and Embedded Devices: State-of-the-art, Challenges, and Future Directions

Chen, Y., Zheng, B., Zhang, Z., Wang, Q., Shen, C., & Zhang, Q. (2021) · ACM Computing Surveys · doi:10.1145/3398209 · Cited by 159 · see citing works

Sparse low rank factorization for deep neural network compression

Swaminathan, S., Garg, D., Kannan, R., & Andrès, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.035 · Cited by 148 · see citing works

Entropy and mutual information in models of deep neural networks*

Gabrié, M., Manoel, A., Luneau, C., Barbier, J., Macris, N., Krząkała, F., & Zdeborová, L. (2019) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ab3430 · Cited by 143 · see citing works

Deep Neural Network Compression by In-Parallel Pruning-Quantization

Tung, F., & Mori, G. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2886192 · Cited by 140 · see citing works

Resource-efficient machine learning in 2 KB RAM for the internet of things

Kumar, A., Goyal, S., & Varma, M. (2017) · International Conference on Machine Learning · Cited by 132 · see citing works

Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks

Cheng, J., Wu, J., Leng, C., Wang, Y., & Hu, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2774288 · Cited by 125 · see citing works

PerforatedCNNs: Acceleration through Elimination of Redundant ConvolutionsPreprint

Figurnov, M., Ibraimova, A., Vetrov, D., & Kohli, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08362 · Cited by 109 · see citing works

An Architecture to Accelerate Convolution in Deep Neural Networks

Ardakani, A., Condo, C., Ahmadi, M., & Gross, W. J. (2017) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2757036 · Cited by 104 · see citing works

Memristor-Based Design of Sparse Compact Convolutional Neural Network

Wen, S., Wei, H., Yan, Z., Guo, Z., Yang, Y., Huang, T., & Chen, Y. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2934357 · Cited by 99 · see citing works

Universal Deep Neural Network Compression

Choi, Y., El‐Khamy, M., & Lee, J. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2975903 · Cited by 91 · see citing works

Self-Attention Encoding and Pooling for Speaker Recognition

Safari, P., India, M., & Hernando, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1446 · Cited by 85 · see citing works

LCNN: Lookup-Based Convolutional Neural Network

Bagherinezhad, H., Rastegari, M., & Farhadi, A. (2017) · 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/cvpr.2017.98 · Cited by 77 · see citing works

Multigrid Neural Architectures

Ke, T.-W., Maire, M., & Yu, S. X. (2017) · 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/cvpr.2017.433 · Cited by 81 · see citing works

Convolution by Evolution

Fernando, C., Banarse, D., Reynolds, M., Besse, F., Pfau, D., Jaderberg, M., Lanctot, M., & Wierstra, D. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908890 · Cited by 75 · see citing works

Random curiosity-driven exploration in deep reinforcement learning

Li, J., Shi, X., Li, J., Zhang, X., & Wang, J.-Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.024 · Cited by 68 · see citing works

Coreset-Based Neural Network Compression

Dubey, A., Chatterjee, M., & Ahuja, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_28 · Cited by 72 · see citing works

OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization

Hu, P., Peng, X., Zhu, H., Aly, M. M. S., & Lin, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16950 · Cited by 71 · see citing works

Efficient Mobile Implementation of A CNN-based Object Recognition System

Yanai‬, ‪., Tanno, R., & Okamoto, K. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967243 · Cited by 72 · see citing works

Computer Vision – ECCV 2018 Workshops

(2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5 · Cited by 70 · see citing works

Centered Weight Normalization in Accelerating Training of Deep Neural Networks

Huang, L., Liu, X., Liu, Y., Lang, B., & Tao, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.305 · Cited by 70 · see citing works

Adversarial Network Compression

Belagiannis, V., Farshad, A., & Galasso, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5_37 · Cited by 62 · see citing works

Low-Memory Neural Network Training: A Technical ReportPreprint

Sohoni, N. S., Aberger, C. R., Leszczynski, M., Zhang, J., & Ré, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10631 · Cited by 71 · see citing works

Enhancing deep neural networks via multiple kernel learning

Lauriola, I., Gallicchio, C., & Aiolli, F. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107194 · Cited by 59 · see citing works

Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Yang, H., Tang, M., Wen, W., Yan, F., Hu, D., Li, A., Li, H., & Chen, Y. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00347 · Cited by 56 · see citing works

ACDC: A Structured Efficient Linear LayerPreprint

Moczulski, M., Denil, M., Appleyard, J., & de Freitas, N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05946 · Cited by 56 · see citing works

Doubly Convolutional Neural NetworksPreprint

Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09716 · Cited by 55 · see citing works

Resource-Efficient Neural Networks for Embedded SystemsPreprint

Roth, W., Schindler, G., Klein, B., Peharz, R., Tschiatschek, S., Fröning, H., Pernkopf, F., & Ghahramani, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03048 · Cited by 57 · see citing works

Online Performance Monitoring and Modeling Paradigm Based on Just-in-Time Learning and Extreme Learning Machine for a Non-Gaussian Chemical Process

Peng, X., Tang, Y. S., Du, W., & Qian, F. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6b04633 · Cited by 51 · see citing works

Low-Complexity Approximate Convolutional Neural Networks

Cintra, R. J., Duffner, S., García, C., & Leite, A. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2815435 · Cited by 50 · see citing works

Efficient mobilenet architecture as image recognition on mobile and embedded devices

Khasoggi, B., Ermatita, E., & Samsuryadi, S. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v16.i1.pp389-394 · Cited by 62 · see citing works

IDK Cascades: Fast Deep Learning by Learning not to OverthinkPreprint

Wang, X., Luo, Y., Crankshaw, D., Tumanov, A., Yu, F., & Gonzalez, J. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00885 · Cited by 51 · see citing works

Long short‐term memory networks for proton dose calculation in highly heterogeneous tissues

Neishabouri, A., Wahl, N., Mairani, A., Köthe, U., & Bangert, M. (2020) · Medical Physics · doi:10.1002/mp.14658 · Cited by 48 · see citing works

EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy

Aujih, A. B., Shapiai, M. I., Mériaudeau, F., & Tang, T. B. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3182907 · Cited by 47 · see citing works

Butterfly Transform: An Efficient FFT Based Neural Architecture Design

Alizadeh, K., Prabhu, A., Farhadi, A., & Rastegari, M. (2020) · 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/cvpr42600.2020.01204 · Cited by 46 · see citing works

Artificial intelligence: a way forward for agricultural sciences

Redhu, N. S., Thakur, Z., Yashveer, S., & Mor, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89778-5.00007-6 · Cited by 47 · see citing works

Compression of recurrent neural networks for efficient language modeling

Grachev, A. M., Ignatov, D. I., & Savchenko, A. V. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.057 · Cited by 42 · see citing works

Review of Algorithms for Artificial Intelligence on Low Memory Devices

Radanliev, P., & Roure, D. D. (2021) · IEEE Access · doi:10.1109/access.2021.3101579 · Cited by 40 · see citing works

Generalized BackPropagation, Étude De Cas: OrthogonalityPreprint

Harandi, M. T., & Fernando, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05927 · Cited by 39 · see citing works

Robust Student Network Learning

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Building Efficient Deep Neural Networks With Unitary Group Convolutions

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Learning Compression from Limited Unlabeled Data

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Fried Binary Embedding for High-Dimensional Visual Features

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Principled Training of Neural Networks with Direct Feedback AlignmentPreprint

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Neuromorphic circuit based on the un-supervised learning of biologically inspired spiking neural network for pattern recognition

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Quantifying Long Range Dependence in Language and User Behavior to improve RNNs

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A novel un-supervised burst time dependent plasticity learning approach for biologically pattern recognition networks

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Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization

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Light-weight 3DCNN for DeepFakes, FaceSwap and Face2Face facial forgery detection

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A Dynamic Pruning Method on Multiple Sparse Structures in Deep Neural Networks

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Constructing Deep Neural Networks by Bayesian Network Structure LearningPreprint

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Orthogonal Over-Parameterized Training

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Learning Compressed Transforms with Low Displacement Rank

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Reliable identification of redundant kernels for convolutional neural network compression

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Rectified Binary Convolutional Networks with Generative Adversarial Learning

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Compressing fully connected layers of deep neural networks using permuted features

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On the building of efficient Fourier Convolutional Neural Networks

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Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingPreprint

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ProdSumNet: reducing model parameters in deep neural networks via product-of-sums matrix decompositionsPreprint

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ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget

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