Vladimir Kramnik

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Vladimir Kramnik trusts

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

  1. Tommi S. Jaakkola (in Artificial Intelligence)
  2. David Alvarez-Melis (in Artificial Intelligence)

Papers from Vladimir Kramnik's perspective

Ordered by how strongly the researchers Vladimir Kramnik 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. Visualizing Data using t-SNE
  3. Axiomatic Attribution for Deep Networks
  4. The (Un)reliability of saliency methods
  5. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
  6. A Unified Approach to Interpreting Model Predictions
  7. Understanding intermediate layers using linear classifier probes
  8. Learning the parts of objects by non-negative matrix factorization
  9. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
  10. Sanity Checks for Saliency Maps

Their papers