Alia Abbara

Scaling & Learning Theory · 2019

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Alia Abbara trusts overall

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

  1. Vladimir Naumovich Vapni
  2. Michel Talagrand
  3. Peter L. Bartlett
  4. Shai Shalev-Shwartz
  5. Rémi Monasson
  6. E. Gardner
  7. Shahar Mendelson
  8. Riccardo Zecchina
  9. Shai Ben-David
  10. C. Broeck

Papers from Alia Abbara's perspective

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

  1. Understanding deep learning requires rethinking generalization
  2. The Nature of Statistical Learning Theory
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. Gradient-based learning applied to document recognition
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Learning Multiple Layers of Features from Tiny Images
  8. Long Short-Term Memory
  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