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Found 14 papers, 7 papers with code
Beyond Intuition, a Framework for Applying GPs to Real-World Data
Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets.
Bayesian inference and neural estimation of acoustic wave propagation
In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals.
Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning
This work examines the characteristic activation values of individual ReLU units in neural networks.
Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation
However, these theoretical tools cannot fully explain finite networks as the empirical kernel changes significantly during gradient-descent-based training in contrast to infinite networks.
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT).
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Since DynamicPPL is a modular, stand-alone library, any probabilistic programming system written in Julia, such as Turing. jl, can use DynamicPPL to specify models and trace their model parameters.
Bijectors.jl: Flexible transformations for probability distributions
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning.
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms
Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm.
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations
Many modern machine learning algorithms, such as automatic differentiation (AD) and versions of approximate Bayesian inference, can be understood as a particular case of message passing on some computation graph.
Bayesian Learning of Sum-Product Networks
While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them are somewhat ad-hoc and based on intuition rather than a clear learning principle.
Particle Gibbs for Infinite Hidden Markov Models
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system.
Dirichlet Fragmentation Processes
In this paper, first we review the theory of random fragmentation processes [Bertoin, 2006], and a number of existing methods for modelling trees, including the popular nested Chinese restaurant process (nCRP).
A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system.
