Search Results for author:
Found 7 papers, 3 papers with code
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Using the proposed graph distance, we present two clustering algorithms and show that they achieve state-of-the-art results.
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
This paper focuses on semi-supervised learning on graphs, and explains the above observations through the lens of Neural Tangent Kernels (NTKs).
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
The fundamental principle of Graph Neural Networks (GNNs) is to exploit the structural information of the data by aggregating the neighboring nodes using a `graph convolution' in conjunction with a suitable choice for the network architecture, such as depth and activation functions.
Improved Representation Learning Through Tensorized Autoencoders
The central question in representation learning is what constitutes a good or meaningful representation.
Unveiling the Hessian's Connection to the Decision Boundary
Based on this finding, we provide a new and straightforward approach to studying the complexity of a high-dimensional decision boundary; show that this connection naturally inspires a new generalization measure; and finally, we develop a novel margin estimation technique which, in combination with the generalization measure, precisely identifies minima with simple wide-margin boundaries.
Kernels, Data & Physics
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches.
Fast Adaptive Test-Time Defense with Robust Features
Adaptive test-time defenses are used to improve the robustness of deep neural networks to adversarial examples.
