filmov
tv
Certifying Robustness of Graph Laplacian-Based Semi-Supervised Learning
Показать описание
Matthew Thorpe (University of Manchester); Bao Wang (University of Utah)
MSML21
Рекомендации по теме
0:04:35
Certifying Robustness of Graph Laplacian-Based Semi-Supervised Learning
1:00:15
Prof. Luca Trevisan: Graph Partitioning Algorithms and Laplacian Eigenvalues
1:25:56
Shape Analysis (Lecture 15): Applications of the Laplacian in graphics, vision, and learning
1:05:17
Robert Ghrist (5/1/21): Laplacians and Network Sheaves
0:05:04
Extensions and Limitations of Randomized Smoothing for Robustness Guarantees
0:48:03
AGT: Graphs, curvature, and local discrepancy
1:09:20
Graph Neural Networks for Learning Nonlinear Power System Operations
0:21:33
Challenges of Guaranteeing DNN Robustness - Reza Samavi - Vector's ML & Privacy Workshop
0:48:05
Robert Ghrist (8/29/21): Laplacians and Network Sheaves
0:17:42
MSML2020 Paper Presentation - Bao Wang
1:13:17
Laura Mancinska (QMATH, Copenhagen): Fixed-size schemes for certification of large quantum systems
0:54:26
Certified Robustness: Fundamentals and Challenges. Aleksandr Petiushko, Nuro, USA
0:53:02
Seminario: Schrödinger Bridges and Sinkhorn algorithm through the lens of Optimal Transport Theory
0:42:33
On Using Graph Distances to Estimate Euclidean and Related Distances
0:04:35
Transformers Meet Directed Graphs (ICML 2023)
1:01:19
Lower bounds on the size of semidefinite programming relaxations (1)
0:23:20
Inference Risks for Machine Learning (ICLR Workshop on Distributed and Private Machine Learning)
0:48:07
Houman Owhadi: On learning adapted kernels. One World Numerical Analysis Series.
0:37:01
Jean-Marc Bardet : Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of...
0:40:39
EUSIPCO 2020 Tutorial 1-1: Nonconvex Optimization for High-Dimensional Signal Estimation
0:57:39
Extended Formulations 1
0:46:48
Interpreting Deep Neural Networks (DNNs)
0:46:02
IEEE Controls System Society Distinguished Lecture: Murat Arcak, March 2, 2018
0:49:58
Session 4: High-dimensional Statistics