Government
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich, Bernhard Schรคfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Gรผnter Klambauer
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
OTLDA: AGeometry-AwareOptimalTransport ApproachforTopicModeling
We present an optimal transport framework for learning topics from textual data. While the celebrated Latent Dirichlet allocation (LDA) topic model and its variants have been applied to many disciplines, they mainly focus on wordoccurrences and neglect to incorporate semantic regularities in language.
