Nisha Bansal

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 4 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Nisha Bansal trusts

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

  1. Lloyd S. Shapley (in Game & Decision Theory)
  2. Scott M. Lundberg (in Artificial Intelligence)
  3. Su-In Lee (in Artificial Intelligence)

Who trusts Nisha Bansal

  1. Su-In Lee (in Artificial Intelligence)
  2. Scott M. Lundberg (in Artificial Intelligence)

Papers from Nisha Bansal's perspective

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

  1. Visualizing and Understanding Convolutional Networks
  2. Et al
  3. Striving for Simplicity: The All Convolutional Net
  4. Random Forests
  5. Axiomatic Attribution for Deep Networks
  6. Scikit-learn: Machine Learning in Python
  7. Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
  8. Greedy function approximation: A gradient boosting machine.
  9. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
  10. A Value for n-person Games

Their papers