Yasunobu Nohara

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Yasunobu Nohara trusts overall

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

  1. Scott M. Lundberg
  2. Jerome H. Friedman
  3. Tianqi Chen
  4. Su-In Lee
  5. Guolin Ke
  6. Carlos Guestrin
  7. Gabriel Erion-Barner

Who trusts Yasunobu Nohara overall

  1. Shiva Maleki Varnosfaderani
  2. Mohamad Forouzanfar

Papers from Yasunobu Nohara's perspective

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

  1. Greedy function approximation: A gradient boosting machine.
  2. XGBoost: A Scalable Tree Boosting System
  3. A Unified Approach to Interpreting Model Predictions
  4. LightGBM: A Highly Efficient Gradient Boosting Decision Tree
  5. Consistent Individualized Feature Attribution for Tree Ensembles
  6. ImageNet Large Scale Visual Recognition Challenge
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Gradient-based learning applied to document recognition
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Regression Shrinkage and Selection via the Lasso

Their papers