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Found 24 papers, 8 papers with code
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Spectral Gap Regularization of Neural Networks
We propose to use the Fiedler value of the neural network's underlying graph as a tool for regularization.
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
A widely-used emulator is the Gaussian process (GP), which provides a flexible framework for efficient prediction and uncertainty quantification.
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition.
Interpretable AI for relating brain structural and functional connectomes
One of the central problems in neuroscience is understanding how brain structure relates to function.
Multiscale Graph Comparison via the Embedded Laplacian Discrepancy
For comparing graphs that do not have any ambiguities due to basis symmetries (i. e. the spectrums are simple), we show that the ELD becomes a natural pseudo-metric that enjoys nice properties such as invariance under graph isomorphism.
Gaussian Process Subspace Regression for Model Reduction
In PROM, each parameter point can be associated with a subspace, which is used for Petrov-Galerkin projections of large system matrices.
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
More recently, transformation-based models have been used in variational inference (VI) to construct flexible implicit families of variational distributions.
Bayesian neural networks and dimensionality reduction
We attempt to solve these problems by deploying Markov chain Monte Carlo sampling algorithms (MCMC) for Bayesian inference in ANN models with latent variables.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Targeted stimulation of the brain has the potential to treat mental illnesses.
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
We propose to use the Fiedler value of the neural network's underlying graph as a tool for regularization.
Bayesian joint modeling of chemical structure and dose response curves
Our model also enables the prediction of chemical dose-response profiles based on chemical structure (that is, without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs.
Applications
Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data
In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations.
Applications
Stochastic Lipschitz Q-Learning
To the best of our knowledge, this is the first analysis in the model-free setting whose established regret matches the lower bound up to a logarithmic factor.
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e. g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data.
Reducing over-clustering via the powered Chinese restaurant process
Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets.
Intrinsic Gaussian processes on complex constrained domains
in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.
Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation.
Computation
Adaptive posterior convergence rates in non-linear latent variable models
In this article, we study rates of posterior contraction in univariate density estimation for a class of non-linear latent variable models where unobserved U(0, 1) latent variables are related to the response variables via a random non-linear regression with an additive error.
Statistics Theory Statistics Theory
DECOrrelated feature space partitioning for distributed sparse regression
The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space).
No penalty no tears: Least squares in high-dimensional linear models
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.
Median Selection Subset Aggregation for Parallel Inference
For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs.
Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance.
