Results 91 to 100 of about 43,983 (273)
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
Advanced Electronic Materials, EarlyView.This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Stochastic Variational Inference
, 2013 We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet ...
Chong Wang +4 more
core +2 more sources
Bayesian K-SVD Using Fast Variational Inference
IEEE Transactions on Image Processing, 2017 Recent work in signal processing in general and image processing in particular deals with sparse representation related problems. Two such problems are of paramount importance: an overriding need for designing a well-suited overcomplete dictionary containing a redundant set of atoms-i.e., basis signals-and how to find a sparse representation of a given
Juan G. Serra +3 more
openaire +2 more sources
Advanced Energy Materials, EarlyView.We identify two decisive levers for SAM interfaces: molecular design (carboxylic acid‐based, phosphonic acid, other anchoring chemistries, and polymeric SAMs) and mixing routes (co‐assembly, in situ assembly, pre‐ and post‐treatment). Coordinated tuning of headgroups and assembly pathways optimises energy alignment and film formation, suppresses ...
Jiaxu Zhang, Bochun Kang, Feng Yan
wiley +1 more source
Boosting Variational Inference: an Optimization Perspective
, 2018 Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of ...
Ghosh, Joydeep +3 more
core
Advanced Energy Materials, EarlyView.Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion
International Journal of Advanced Robotic Systems, 2016 In this paper, we propose a novel simultaneous registration and fusion approach for tracking. This method is based on a recursive Variational Bayesian (RVB) algorithm, which is the online variant of the Variational Bayesian (VB) approach.
Hao Zhu +3 more
doaj +1 more source
Bayesian and variational inference for reinforcement learning
, 2021 This thesis explores Bayesian and variational inference in the context of solving the reinforcement learning (RL) problem. Recent advances in developing state-ofthe-art algorithms suitable for continuous control introduce regularisation into the reinforcement learning objective.
openaire +2 more sources
Variational Bayesian mixed-effects inference for classification studies
NeuroImage, 2013 NeuroImage, 76 (1)
Kay Henning Brodersen +5 more
openaire +5 more sources