Benjamin Aubin

Scaling & Learning Theory · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Benjamin Aubin trusts overall

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

  1. Michel Talagrand
  2. Peter L. Bartlett
  3. Florent Krzakala
  4. Lenka Zdeborov'a
  5. Jean Barbier
  6. M. M'ezard
  7. Martin J. Wainwright
  8. Rémi Monasson
  9. Vladimir Naumovich Vapni
  10. Shahar Mendelson

Who trusts Benjamin Aubin overall

  1. Jean Barbier
  2. Stefano Spigler
  3. Sebastian Goldt
  4. Nicolas Macris
  5. Mario Geiger

Papers from Benjamin Aubin's perspective

Ordered by how strongly the researchers Benjamin Aubin 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. Gradient-based learning applied to document recognition
  5. Understanding deep learning requires rethinking generalization
  6. Long Short-Term Memory
  7. The Nature of Statistical Learning Theory
  8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  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