We present a nonparametric dynamic model which learns the mode constraint alongside the dynamic modes.
Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well.
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.
Building on the previous work by Kazlauskaiteet al. , we include a separate monotonic warp of the input data to model temporal misalignment.
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
In this paper, we present a Bayesian view on model-based reinforcement learning.
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.
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).
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.
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.
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.
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.
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 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.
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.
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
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.
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.