Search Results for author:
Found 11 papers, 5 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.
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Understanding the effects of these system inputs on system outputs is crucial to have any meaningful model of a dynamical system.
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for many experimental sciences.
Deterministic Variational Inference for Robust Bayesian Neural Networks
We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances.
Soft Weight-Sharing for Neural Network Compression
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices.
Automatic Variational ABC
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models.
Hamiltonian ABC
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models.
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
We propose a post optimization posterior analysis that computes and visualizes all the models that can generate equally good or better simulation results, subject to constraints.
MLitB: Machine Learning in the Browser
Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large.
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Scientists often express their understanding of the world through a computationally demanding simulation program.
