Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks

Peter L. Bartlett, David P. Helmbold, Philip M. Long. Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks. Neural Computation, 31(3), 2019. [doi]

@article{BartlettHL19,
  title = {Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks},
  author = {Peter L. Bartlett and David P. Helmbold and Philip M. Long},
  year = {2019},
  doi = {10.1162/neco_a_01164},
  url = {https://doi.org/10.1162/neco_a_01164},
  researchr = {https://researchr.org/publication/BartlettHL19},
  cites = {0},
  citedby = {0},
  journal = {Neural Computation},
  volume = {31},
  number = {3},
}