Results 91 to 100 of about 190,737 (267)
Benchmarking uncertainty quantification for protein engineering.
PLoS Computational BiologyMachine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods
Kevin P Greenman +2 more
doaj +1 more source
A Bayesian Approach toward Active Learning for Collaborative Filtering
, 2012 Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user.
Jin, Rong, Si, Luo
core
Advanced Robotics Research, EarlyView.An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma +10 more
wiley +1 more source
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning [PDF]
, 2010 Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to ...
Coates, Mark +2 more
core +1 more source
Bayesian Batch Active Learning as Sparse Subset Approximation
, 2019 Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled.
Pinsler, R. +3 more
openaire +3 more sources
Disentangled Multi-Fidelity Deep Bayesian Active Learning
, 2023 To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels.
Wu, Dongxia +4 more
openaire +2 more sources
Hard‐Magnetic Soft Millirobots in Underactuated Systems
Advanced Robotics Research, EarlyView.This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang +4 more
wiley +1 more source
3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends
Advanced Robotics Research, EarlyView.Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu +5 more
wiley +1 more source
Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
Bayesian model updating via streamlined Bayesian active learning cubature
This paper proposes a novel Bayesian active learning method for Bayesian model updating, which is termed as "Streamlined Bayesian Active Learning Cubature" (SBALC). The core idea is to approximate the log-likelihood function using Gaussian process (GP) regression in a streamlined Bayesian active learning way.
Li, Pei-Pei +4 more
openaire +2 more sources