Amal Asselman

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Amal Asselman trusts overall

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

  1. Leo Breiman
  2. Didrik Nielsen
  3. Albert T. Corbett
  4. Ryan Shaun Joazeiro de Baker
  5. Yoav Freund
  6. Tianqi Chen
  7. K. Koedinger
  8. Mingyu Feng
  9. Jitendra Kumar
  10. Robert E. Schapire

Papers from Amal Asselman's perspective

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

  1. Bagging Predictors
  2. Knowledge tracing: Modeling the acquisition of procedural knowledge (User modeling and user-adapted interaction)
  3. Greedy function approximation: A gradient boosting machine.
  4. Addressing the assessment challenge with an online system that tutors as it assesses (User modeling and user-adapted interaction)
  5. Deep Knowledge Tracing
  6. A decision-theoretic generalization of on-line learning and an application to boosting
  7. XGBoost: A Scalable Tree Boosting System
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Gradient-based learning applied to document recognition

Their papers