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Found 6 papers, 0 papers with code
Large-scale variational Gaussian state-space models
We introduce an amortized variational inference algorithm and structured variational approximation for state-space models with nonlinear dynamics driven by Gaussian noise.
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
In this work, we propose cvHM, a general inference framework for latent GP models leveraging Hida-Mat\'ern kernels and conjugate computation variational inference (CVI).
Real-Time Variational Method for Learning Neural Trajectory and its Dynamics
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation.
Hida-Matérn Kernel
We present the class of Hida-Mat\'ern kernels, which is the canonical family of covariance functions over the entire space of stationary Gauss-Markov Processes.
Non-parametric generalized linear model
However, obtaining a satisfactory fit often requires burdensome model selection and fine tuning the form of the basis functions and their temporal span.
QCD corrections to vector boson pair production in gluon fusion including interference effects with off-shell Higgs at the LHC
We explore the NLO QCD effects in $gg \to ZZ$ focusing, in particular, on the interference between prompt and Higgs-mediated processes.
High Energy Physics - Phenomenology High Energy Physics - Experiment
