Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...
The next phase of AI infrastructure will not be defined by a single destination called “the cloud” or “the edge.” ...
Abstract: Variational inference (VI) provides a principled framework for estimating posterior distributions over model parameters, enabling explicit modeling of weight uncertainty during optimization.
It's not just Google's Gemini 3, Nano Banana Pro and Anthropic's Claude Opus 4.5 we have to be thankful around the Thanksgiving holiday (at least here in the U.S.) No, German AI startup Black Forest ...
Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.
Jomo Kenyatta University of Agriculture and Technology, Juja, Kiambu County, Kenya. Where KL denotes the Kullback-Leibler divergence, and p(z) is a prior distribution over the latent space (typically ...
Due to the high-stakes nature of industrial processes, there is an immediate and pressing need on soft sensors for stability and interpretability. In this regard, causality-inspired modeling aims to ...
This GitHub repository contains the code, data, and figures for the Nature Communications paper Seismic Multi-hazard and Impact Estimation via Causal Inference from Satellite Imagery. If you have any ...
Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results