Results 141 to 150 of about 541,400 (289)

Visual Ensemble Analysis With Deep Learning Prediction for Studying the Effect of Tissue Properties on Radiofrequency Ablation

Computer Graphics Forum, EarlyView.
We present an interactive visual analysis tool to study how patient‐specific tissue properties influence radiofrequency ablation outcomes. Using a deep‐learning surrogate model, we predict ablation volumes for unseen parameter settings with accuracy superior to interpolation, supporting improved treatment planning. Abstract Radiofrequency (RF) ablation
R. Sabbagh Gol, M. Evers, K. Heimes, T. Gerrits, S. Gyawali, D. Sinden, T. Preusser, L. Linsen   +7 more
wiley   +1 more source

Federated Deep Subspace Clustering

CoRR
8pages,4 figures, 4 ...
Yupei Zhang, Ruojia Feng, Yifei Wang, Xuequn Shang 0001   +3 more
openaire   +3 more sources

Differentially Private Subspace Clustering [PDF]

, 2020
Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple "clusters" so that data points in a single cluster lie approximately on a low-dimensional linear subspace.
Aarti Singh, Yu-Xiang Wang, Yining Wang
core  

Guest Editorial: Computational Intelligence in Dynamic and Uncertain Environments

CAAI Transactions on Intelligence Technology, EarlyView.
Shouyong Jiang
wiley   +1 more source

Scalable Computation of Topological Abstractions for Scalar Data

Computer Graphics Forum, EarlyView.
Abstract Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial ...
M. Will, J. Lukasczyk, J. Tierny, A. Gyulassy, G. H. Weber, H. Carr, C. Garth   +6 more
wiley   +1 more source

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Applied Sciences
Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems.
Wenjiang Shao, Xiaowei Zhang
doaj   +1 more source

Tangent Blow‐Ups for Processing Non‐Manifold Geometry

Computer Graphics Forum, EarlyView.
Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov, Mohammad Sina Nabizadeh, Ana Dodik, Justin Solomon   +3 more
wiley   +1 more source

Optimized and Aligned Anisotropic Monte Carlo Sampling Patterns

Computer Graphics Forum, EarlyView.
Abstract Path tracing uses Monte Carlo integration to solve the rendering equation by evaluating the integrand at random sampling points. The convergence rate of the error can be significantly improved by using correlated instead of random sampling, especially on smooth integrands.
Mirco Werner, Johannes Hanika, Carsten Dachsbacher   +2 more
wiley   +1 more source

An Adaptive Sparse Subspace Clustering for Cell Type Identification. [PDF]

Front Genet, 2020
Zheng R, Liang Z, Chen X, Tian Y, Cao C, Li M.   +5 more
europepmc   +1 more source

Neural collaborative subspace clustering

, 2022
We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of lowdimensional subspaces.
Harandi, Mehrtash, Zhang, Tong, Li, Hongdong, Ji, Pan, Huang, Wenbing   +4 more
core