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  1. ; (, Information and Inference: A Journal of the IMA)
    Abstract In this paper, we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed deep tensor decomposition (DeepTD) is based on a low-rank tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is sample efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network. 
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  2. ; ; ; ; ; (, Proceedings of the 42nd International Conference on Machine Learning)
    Free, publicly-accessible full text available July 15, 2026
  3. ; ; ; ; ; ; ; (, The IEEE/CVF Conference on Computer Vision and Pattern Recognition)
    Free, publicly-accessible full text available June 1, 2026
  4. ; ; ; ; (, The International Conference on Learning Representations)
    Free, publicly-accessible full text available May 15, 2026
  5. ; ; ; ; (, Proceedings of the 28th International Conference on Artificial Intelligence and Statistics)
    Free, publicly-accessible full text available May 1, 2026
  6. ; ; (, AAAI Association for the Advancement of Artificial Intelligence)
    Free, publicly-accessible full text available February 25, 2026
  7. ; ; ; (, AAAI Association for the Advancement of Artificial Intelligence)
    Free, publicly-accessible full text available February 25, 2026
  8. ; ; ; ; ; (, Thirty-Ninth Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 31, 2025
  9. ; ; (, Thirty-Ninth Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 31, 2025
  10. ; ; ; ; ; (, Thirty-Ninth Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 31, 2025