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Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization

arXiv 2210.13468 ↗First posted 24 Oct 2022cs.LG eess.SPcited by 0

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Abstract (arXiv)

Despite the outstanding performance of deep neural networks in different applications, they are still computationally extensive and require a great number of memories. This motivates more research on reducing the resources required for implementing such networks. An efficient approach addressed for this purpose is matrix factorization, which has been shown to be effective on different networks. In this paper, we utilize binary matrix factorization and show its great efficiency in reducing the required number of resources in deep neural networks. In effect, this technique can lead to the practical implementation of such networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 24 Oct 2022.

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30 of 42 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  6. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  7. 2017Mixed Precision Trainingcited by 1,156
  8. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  9. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  10. 2016Learning Structured Sparsity in Deep Neural Networkscited by 541
  11. 2014Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluationcited by 306
  12. 2018Model compression via distillation and quantizationcited by 289
18 more
  1. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
  2. 2014Speeding up Convolutional Neural Networks with Low Rank Expansionscited by 284
  3. 2018LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networkscited by 274
  4. 2017Variational Dropout Sparsifies Deep Neural Networkscited by 265
  5. 2018Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithmcited by 235
  6. 2016Dynamic Network Surgery for Efficient DNNscited by 233
  7. 2015Structured Pruning of Deep Convolutional Neural Networkscited by 149
  8. 2017Deep Learning with Low Precision by Half-wave Gaussian Quantizationcited by 117
  9. 2020Training Binary Neural Networks with Real-to-Binary Convolutionscited by 112
  10. 2016Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1cited by 105
  11. 2015Convolutional neural networks with low-rank regularizationcited by 103
  12. 2017Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracycited by 101
  13. 2017Soft Weight-Sharing for Neural Network Compressioncited by 99
  14. 2016Group Sparse Regularization for Deep Neural Networkscited by 89
  15. 2014Efficient and Accurate Approximations of Nonlinear Convolutional Networkscited by 30
  16. 2019Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagationcited by 28
  17. 2019Binary Component Decomposition Part I: The Positive-Semidefinite Casecited by 5
  18. 2019Binary component decomposition Part II: The asymmetric casecited by 3

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