M. Williams

Computer Vision and Pattern Recognition · with Newby · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom M. Williams trusts

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

  1. Ilya Sutskever (in Computer Vision and Pattern Recognition)
  2. Yann LeCun (in Computer Vision and Pattern Recognition)

Papers from M. Williams's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Stochastic Backpropagation and Approximate Inference in Deep Generative Models
  4. Gradient-based learning applied to document recognition
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Long Short-Term Memory
  8. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  9. Learning Multiple Layers of Features from Tiny Images
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers