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Found 34 papers, 9 papers with code
Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm
Motivated by recent progress in the problem of numerical K\"ahler metrics, we survey machine learning techniques in this area, discussing both advantages and drawbacks.
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Learning complex robot behavior through interactions with the environment necessitates principled exploration.
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data.
Linear combinations of latents in generative models: subspaces and beyond
Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation.
Reparameterization invariance in approximate Bayesian inference
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i. e. BNNs assign different posterior densities to different parametrizations of identical functions.
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
We present a nonparametric dynamic model which learns the mode constraint alongside the dynamic modes.
Identifying latent distances with Finslerian geometry
Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well.
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.
Shooting Schrödinger’s Cat
We present a variational inference scheme to learn a model that solves the Schrödinger Bridge Problem (SBP).
Aligned Multi-Task Gaussian Process
Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
In this paper, we present a Bayesian view on model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+3
Modulating Surrogates for Bayesian Optimization
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
Monotonic Gaussian Process Flow
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).
Data Association with Gaussian Processes
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.
Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner.
Nonparametric Inference for Auto-Encoding Variational Bayes
We would like to learn latent representations that are low-dimensional and highly interpretable.
Bayesian Alignments of Warped Multi-Output Gaussian Processes
We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.
Neural Translation of Musical Style
Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough".
Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.
Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.
Diagnostic Prediction Using Discomfort Drawings with IBTM
The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
Inter-Battery Topic Representation Learning
The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data.
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Inter-battery factor analysis extends this notion to multiple views of the data.
On a Family of Decomposable Kernels on Sequences
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.
Factorized Topic Models
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.
