Results 71 to 80 of about 227,027 (269)
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
Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion
Advanced Robotics Research, EarlyView.A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza +3 more
wiley +1 more source
Emulating Human Developmental Stages with Bayesian Neural Networks [PDF]
, 2019 We compare the acquisition of knowledge in humans and machines. Research from the field of developmental psychology indicates, that human-employed hypothesis are initially guided by simple rules, before evolving into more complex theories.
Binz, Marcel, Endres, Dominik
core +1 more source
Consensus Formation and Change are Enhanced by Neutrality
Advanced Science, EarlyView.Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag +3 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
Hierarchical Summary Statistics Encoding Across Primary Visual and Posterior Parietal Cortices
Advanced Science, EarlyView.This study shows that mouse V1 simultaneously encodes the ensemble mean and variance of motion, providing a robust summary‐statistic representation that persists despite single‐neuron variability. These signals propagate to PPC, where they are transformed into abstract category representations during decision making.
Young‐Beom Lee +4 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 Science, EarlyView.Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka +3 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