Michelle Brachman

Representation & Self-Supervised Learning · 2020

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

Subfields

Whom Michelle Brachman trusts overall

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

  1. Dong-Hyun Lee

Papers from Michelle Brachman's perspective

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

  1. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
  2. Active Learning Literature Survey
  3. Learning from labeled and unlabeled data with label propagation
  4. Learning with Local and Global Consistency
  5. Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction
  6. Semi-Supervised Classification by Low Density Separation
  7. Selective Sampling Using the Query by Committee Algorithm
  8. Power to the People: The Role of Humans in Interactive Machine Learning (The AI Magazine)
  9. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
  10. Universal Sentence Encoder

Their papers