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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
A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures.
Supplementary Materials: Interpretable Sequence Learning for COVID-19 Forecasting
Table 1: Covariates selected for model.
STORM +: Fully Adaptive SGD with Momentum for Nonconvex Optimization
The most popular approach to handling such problems is variance reduction techniques, which are also known to obtain tight convergence rates, matching the lower bounds in this case.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.
