Jamal Atif

Artificial Intelligence · 2008–2019

A researcher in the Science corpus: 4 papers; trusts 15 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom Jamal Atif trusts overall

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

  1. Boris Motik
  2. Céline Hudelot
  3. Volker Haarslev
  4. Rob Shearer
  5. Alex Krizhevsky
  6. Ian Horrocks

Who trusts Jamal Atif overall

  1. Ivan Donadello
  2. Luciano Serafini

Papers from Jamal Atif's perspective

Ordered by how strongly the researchers Jamal Atif 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: A large-scale hierarchical image database
  3. Learning Multiple Layers of Features from Tiny Images
  4. ImageNet classification with deep convolutional neural networks
  5. Intriguing properties of neural networks
  6. Gradient-based learning applied to document recognition
  7. Explaining and Harnessing Adversarial Examples
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. Wide Residual Networks
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers