Mason McGill

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 196 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

Whom Mason McGill trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Alex Krizhevsky
  2. Yann LeCun
  3. Kaiming He
  4. Sergey Ioffe
  5. Yoshua Bengio
  6. Geoffrey E. Hinton
  7. Shaoqing Ren
  8. Xavier Glorot
  9. Ross B. Girshick
  10. Christian Szegedy

Who trusts Mason McGill overall

  1. Tom Véniat
  2. Clemens Rosenbaum
  3. Rameswar Panda
  4. Xin Wang
  5. Víctor Campos
  6. Ludovic Denoyer
  7. Daniel Crankshaw
  8. Charles Herrmann
  9. Tim Klinger
  10. Zuxuan Wu

Papers from Mason McGill's perspective

Ordered by how strongly the researchers Mason McGill trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. ImageNet classification with deep convolutional neural networks
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Gradient-based learning applied to document recognition
  5. ImageNet: A large-scale hierarchical image database
  6. Long Short-Term Memory
  7. Learning Multiple Layers of Features from Tiny Images
  8. Attention is All you Need (Neural Information Processing Systems)
  9. Going deeper with convolutions
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers