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Enhanced sampling techniques are essential for exploring biomolecular conformational dynamics that occur on time scales inaccessible to conventional molecular dynamics (MD) simulations. This study ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

In their study, Diana et al. introduce a novel method for spike inference from calcium imaging data using a Monte Carlo-based approach, emphasizing the quantification of uncertainties in spike time ...

Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the ...

As the AI infrastructure market evolves, we’ve been hearing a lot more about AI inference—the last step in the AI technology infrastructure chain to deliver fine-tuned answers to the prompts given to ...

Futurum Group research director Ray Wang explains what AI inference is and more on 'Making Money.' Trump says he's removing Federal Reserve Gov. Lisa Cook, citing his administration’s allegations of ...

Abstract: In Monte Carlo-based Bayesian inference, it is important to generate samples from a target distribution, which are then used, e.g., to compute expectations with respect to the target ...

Right now in pymc the sample method on Approximation store the model that was used for training. This makes it harder to use variational inference with the posterior predictive sampling and other ...