TA6a5.1

Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts

Eliabelle Mauduit, Andrea Simonetto, ENSTA Paris, France
Session:
TA6a5: Supervised and Self-supervised Learning Poster
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Fred Farr
Presentation Time:
Tue, 31 Oct, 08:15 - 09:55 PT (UTC -8)
Session Chair:
Eliabelle Mauduit, ENSTA Paris
Presentation
Discussion
Resources
No resources available.
Session TA6a5
TA6a5.1: Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts
Eliabelle Mauduit, Andrea Simonetto, ENSTA Paris, France
TA6a5.2: Enhancing Neural Network Performance for Problems in the Physical Sciences: Applications to Electromagnetic Signal Source Localization
Maria Barger, Worcester Polytechnic Institute, United States; Evan Witz, Wisconsin Lutheran College, United States; Randy Paffenroth, Worcester Polytechnic Institute, United States
TA6a5.3: A Novel Cardiac Arrhythmia Classification Method Using Visibility Graphs and Graph Convolutional Network
Dorsa EPMoghaddam, Ananya Muguli, Behnaam Aazhang, Rice University, United States
TA6a5.4: Knowledge Distillation by Compressive Sampling
Shreyas Chaudhari, Jose Moura, Carnegie Mellon University, United States
Contacts