Results 71 to 80 of about 224,776 (267)
Bayesian Neural Network Ensembles
, 2018 Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset. The variance of the ensemble's predictions is interpreted as its epistemic uncertainty.
Pearce, Tim, Zaki, Mohamed, Neely, Andy
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
Deep learning solutions for smart city challenges in urban development
Scientific ReportsIn the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized.
Pengjun Wu +3 more
doaj +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
Deep Learning and Bayesian Methods
EPJ Web of Conferences, 2017 A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding.
Prosper Harrison B.
doaj +1 more source
Deep-learning jets with uncertainties and more
SciPost Physics, 2020 Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers.
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson
doaj +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
Stochastic Bayesian Neural Networks
, 2020 Bayesian neural networks perform variational inference over the weights however calculation of the posterior distribution remains a challenge. Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound.
openaire +2 more sources
Probabilistic Safety for Bayesian Neural Networks
, 2020 We study probabilistic safety for BayesianNeural Networks (BNNs) under adversarial in-put perturbations. Given a compact set of input points,T⊆Rm, we study the probability w.r.t. the BNN posterior that all the pointsinTare mapped to the same region S in theoutput space.
Wicker, M +3 more
openaire +3 more sources
Advanced Robotics Research, EarlyView.A miniaturized soft optical sensor that uses thin film color tuning enables real‐time 3D shape‐sensing from a single red–green–blue (RGB) signal. When integrated into a soft robot, it enables closed‐loop control and autonomous navigation in a phantom lung environment without the need for onboard electronics, achieving sub‐millimeter accuracy through ...
Frank Juliá Wise +6 more
wiley +1 more source