Vincent Miele

Statistical and Nonlinear Physics · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Vincent Miele trusts overall

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

  1. Petter Holme
  2. Santo Fortunato
  3. Jean-Jacques Daudin
  4. Mark Newman
  5. Aditya Grover
  6. Mikail Rubinov
  7. Bryan Perozzi
  8. Jure Leskovec
  9. Michael I. Jordan
  10. Vincent D. Blondel

Papers from Vincent Miele's perspective

Ordered by how strongly the researchers Vincent Miele 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Long Short-Term Memory
  5. Attention is All you Need (Neural Information Processing Systems)
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. Learning Multiple Layers of Features from Tiny Images
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers