Results 81 to 90 of about 901,615 (325)
Enhancing Low‐Temperature Performance of Sodium‐Ion Batteries via Anion‐Solvent Interactions
Advanced Functional Materials, EarlyView.DOL is introduced into electrolytes as a co‐solvent, increasing slat solubility, ion conductivity, and the de‐solvent process, and forming an anion‐rich solvent shell due to its high interaction with anion. With the above virtues, the batteries using this electrolyte exhibit excellent cycling stability at low temperatures. Abstract Sodium‐ion batteries
Cheng Zheng +7 more
wiley +1 more source
Avoiding barren plateaus via Gaussian mixture model
New Journal of PhysicsVariational quantum algorithms are among the most prominent methods in quantum computing, with applications in quantum machine learning, quantum simulation, and related fields.
Xiao Shi, Yun Shang
doaj +1 more source
IEEE Access, 2022 This study processes an autonomous indoor drone photographer that searches for and selects a heuristic optimal viewpoint to obtain a well-composed photograph of a group of subjects.
Taisei Yokomatsu, Kosuke Sekiyama
doaj +1 more source
Bayesian inference of Gaussian mixture models with noninformative priors [PDF]
, 2014 This paper deals with Bayesian inference of a mixture of Gaussian distributions. A novel formulation of the mixture model is introduced, which includes the prior constraint that each Gaussian component is always assigned a minimal number of data points ...
Stoneking, Colin J.
core
Diffusion model conditioning on Gaussian mixture model and negative Gaussian mixture gradient
NeurocomputingDiffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation.
Weiguo Lu +5 more
openaire +2 more sources
A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing [PDF]
IEEE Transactions on Image Processing, 2017 Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model, where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions.
Yuan Zhou, Anand Rangarajan, P. Gader
semanticscholar +1 more source
Advanced Functional Materials, EarlyView.Zinc(II) coordination complexes with tunable aryloxy‐imine ligands exhibit controllable supramolecular self‐assembly into hierarchical fibrous structures. Coordination‐driven stacking, not π–π interactions, enables gelation, dynamic assembly/disassembly, and enhanced nanomechanical properties.
Merlin R. Stühler +10 more
wiley +1 more source
Bayesian approaches to Gaussian mixture modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998 A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets.
Roberts, S +3 more
openaire +2 more sources
Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning
IEEE transactions on fuzzy systems, 2019 Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-
Hua Zuo +3 more
semanticscholar +1 more source
Sharp optimal recovery in the two component Gaussian mixture model [PDF]
Annals of Statistics, 2018 In this paper, we study the problem of clustering in the Two component Gaussian mixture model when the centers are separated by some $\Delta>0$. We present a non-asymptotic lower bound for the corresponding minimax Hamming risk improving on existing ...
M. Ndaoud
semanticscholar +1 more source