A Many-Objective Evolutionary Algorithm with Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning
Posted on30/01/2019
Researches have shown difficulties in obtaining proximity while maintaining diversity for solving many-objective optimization problems (MaOPs). The complexities of the true Pareto... Read More
RandomOut: Using a convolutional gradient norm to rescue convolutional filters
Posted on19/02/2016
Filters in convolutional neural networks are sensitive to their initialization. The random numbers used to initialize filters are a bias and determine... Read More
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Posted on23/11/2015
Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision.... Read More
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning
Posted on23/06/2013
Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier.... Read More
The Toronto Paper Matching System: An automated paper-reviewer assignment system
Posted on23/06/2013
One of the most important tasks of conference organizers is the assignment of papers to reviewers. Reviewers’ assessments of papers is a... Read More
A Framework for Optimizing Paper Matching
Posted on23/07/2011
At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment... Read More