15 research outputs found
Quantifying Epistemic Uncertainty in Deep Learning
Uncertainty quantification is at the core of the reliability and robustness
of machine learning. In this paper, we provide a theoretical framework to
dissect the uncertainty, especially the epistemic component, in deep learning
into procedural variability (from the training procedure) and data variability
(from the training data), which is the first such attempt in the literature to
our best knowledge. We then propose two approaches to estimate these
uncertainties, one based on influence function and one on batching. We
demonstrate how our approaches overcome the computational difficulties in
applying classical statistical methods. Experimental evaluations on multiple
problem settings corroborate our theory and illustrate how our framework and
estimation can provide direct guidance on modeling and data collection effort
to improve deep learning performance
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Estimating uncertainty in image-to-image networks is an important task,
particularly as such networks are being increasingly deployed in the biological
and medical imaging realms. In this paper, we introduce a new approach to this
problem based on masking. Given an existing image-to-image network, our
approach computes a mask such that the distance between the masked
reconstructed image and the masked true image is guaranteed to be less than a
specified threshold, with high probability. The mask thus identifies the more
certain regions of the reconstructed image. Our approach is agnostic to the
underlying image-to-image network, and only requires triples of the input
(degraded), reconstructed and true images for training. Furthermore, our method
is agnostic to the distance metric used. As a result, one can use -style
distances or perceptual distances like LPIPS, which contrasts with
interval-based approaches to uncertainty. Our theoretical guarantees derive
from a conformal calibration procedure. We evaluate our mask-based approach to
uncertainty on image colorization, image completion, and super-resolution
tasks, demonstrating high quality performance on each
The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
These proceedings aim to collect the ideas presented, discussed, and disputed at the 40th Workshop on Bayesian Inference and Maximum Entropy, MaxEnt 2021. Skilling and Knuth seek to rebuild the foundations of quantum mechanics from probability theory, and Caticha competes in that endeavour with a very different entropy-based approach. Costa connects entropy with general relativity, Pessoa reports new insights on ecology and Yousefi derives classical density functional theory, both through the maximum entropy principle. Von Toussaint, Preuss, Albert, Rath, Ranftl and Kvas report the latest developments in regression and surrogate-based inference with applications to optimization and inverse problems in plasma physics, biomechanics and geodesy. Van Soom presents new priors for phonetics, Stern et al. propose a new haphazard sampling method, and Kelter uncovers two measure theoretic iss phonetics ues with hypothesis testing
Reassessing the Paradigms of Statistical Model-Building
Statistical model-building is the science of constructing models from data and from information about the data-generation process, with the aim of analysing those data and drawing inference from that analysis. Many statistical tasks are undertaken during this analysis; they include classification, forecasting, prediction and testing. Model-building has assumed substantial importance, as new technologies enable data on highly complex phenomena to be gathered in very large quantities. This creates a demand for more complex models, and requires the model-building process itself to be adaptive. The word “paradigm” refers to philosophies, frameworks and methodologies for developing and interpreting statistical models, in the context of data, and applying them for inference. In order to solve contemporary statistical problems it is often necessary to combine techniques from previously separate paradigms. The workshop addressed model-building paradigms that are at the frontiers of modern statistical research. It tried to create synergies, by delineating the connections and collisions among different paradigms. It also endeavoured to shape the future evolution of paradigms
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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Hidden states, hidden structures: Bayesian learning in time series models
This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration.
For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4).
Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6).
Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7)This work was supported by the Engineering and Physical Sciences Research Council (EPSRC