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Found 5 papers, 2 papers with code
Thermodynamic Natural Gradient Descent
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.
Exploiting Inductive Biases in Video Modeling through Neural CDEs
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and mask propagation.
A State-Space Perspective on Modelling and Inference for Online Skill Rating
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models.
Bayesian Learning of Parameterised Quantum Circuits
Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits.
Online Particle Smoothing with Application to Map-matching
We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy.
Methodology Applications
