Results 61 to 70 of about 227,027 (269)
, 2015 We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications involving ...
Korattikara, Anoop +3 more
core +1 more source
Bayesian Neural Networks: Essentials
, 2021 Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural ...
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
Probabilistic classification of quality of service in wireless computer networks
ICT Express, 2019 There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as
Abdussalam Salama, Reza Saatchi
doaj +1 more source
IEEE Access, 2023 The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities.
Devinder Kaur +5 more
doaj +1 more source
Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]
, 2018 We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo +2 more
core +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
Bayesian Perceptron: Towards fully Bayesian Neural Networks [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 Accepted for publication at the 59th IEEE Conference on Decision and Control (CDC) 2020.
openaire +2 more sources
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
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source