Results 41 to 50 of about 41,144 (258)

Laplace Redux -- Effortless Bayesian Deep Learning

CoRR, 2021
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep ...
Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer 0001, Philipp Hennig   +5 more
openaire   +5 more sources

Reconstructing enzyme evolution by protein engineering

FEBS Letters, EarlyView.
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler, Markus R. Busch, Reinhard Sterner   +2 more
wiley   +1 more source

Bayesian Deep Neural Network to Compensate for Current Transformer Saturation

IEEE Access, 2021
Current transformer saturation has a negative effect on the operation of IEDs, resulting in their malfunction. Here, we present a technique to compensate for saturated waveforms using Bayesian Deep Neural Network (BDNN) comprising Deep Neural Network ...
Sopheap Key, Sang-Hee Kang, Nam-Ho Lee, Soon-Ryul Nam   +3 more
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

IEEE Access
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj   +1 more source

Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems

IEEE Access, 2022
Deep learning technologies, due to their advanced pattern extraction and recognition of high-dimensional data, have been widely adopted into multisensor-based fire detection systems.
Young-Jin Kim, Won-Tae Kim
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences

Journal of Traffic and Transportation Engineering (English ed. Online), 2021
Short-term taxi demand forecasting is of great importance to incentivize vacant cars moving from over-supply regions to over-demand regions, which can minimize the wait time for passengers and drivers. With the consideration of spatiotemporal dependences,
Huimin Luo, Jianming Cai, Kunpeng Zhang, Ruihang Xie, Liang Zheng   +4 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

Theoretical and Applied Mechanics Letters, 2020
: In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-
Luning Sun, Jian-Xun Wang
doaj   +1 more source