Literature

Papers

ML-Papers

• Model Distillation: A Survey
• Positive-Unlabeled Learning with Non-Negative Risk Estimator
• Positive-Unlabeled Learning from Imbalanced Data
• Distilling the Knowledge in a Neural Network
• Online Knowledge Distillation with Diverse Peers

Domain Knowledge

• Joint epitope selection and spacer design for string-of-beads vaccines
• Integrated modeling of the major events in the MHC class I antigen processing pathway
• The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage
• Bounding and Counting Linear Regions of Deep Neural Networks (not part of this project)
• Learning from Corrupted Binary Labels via Class-Probability Estimation
• Recovering True Classifier Performance in Positive-Unlabeled Learning

Further Links

• Summary of Model Distillation Survey