2 citations · 6 across the 5 of their papers we have counts for
5 papers
Learning to Approximate Particle Smoothing Trajectories via Diffusion Generative Models
Ella Tamir, Arno Solin
Learning dynamical systems from sparse observations is critical in numerous fields, including biology, finance, and physics. Even if tackling such problems is standard in general i…
Function-space Parameterization of Neural Networks for Sequential Learning
Aidan Scannell, Riccardo Mereu, Paul Chang +3
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes el…
Sparse Function-space Representation of Neural Networks
Aidan Scannell, Riccardo Mereu, Paul Chang +3
Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weig…
Transport with Support: Data-Conditional Diffusion Bridges
Ella Tamir, Martin Trapp, Arno Solin
The dynamic Schrödinger bridge problem provides an appealing setting for solving constrained time-series data generation tasks posed as optimal transport problems. It consists of l…
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation
Arno Solin, Ella Tamir, Prakhar Verma
Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. Th…