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Found 4 papers, 0 papers with code
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens.
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.
Convolutional Dictionary Learning in Hierarchical Networks
Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across scales: the low pass scale coefficients at one layer are obtained by filtering the scale coefficients at the next layer, and adding a high pass detail innovation obtained by filtering a sparse vector.
Learning Parametric Closed-Loop Policies for Markov Potential Games
This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.
