Search Results for author:
Found 11 papers, 3 papers with code
The Average Mixing Kernel Signature
We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.
Graph Generation via Spectral Diffusion
In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning.
MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images
Our approach is highly modular and can easily be modified to suit different clinical datasets, as it only requires a patient-level label without annotations and accepts WSI sets of different sizes, as the graphs can be of varying sizes and structures.
Graph Neural Networks in Vision-Language Image Understanding: A Survey
2D image understanding is a complex problem within computer vision, but it holds the key to providing human-level scene comprehension.
Graph Kernel Neural Networks
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
Civil Wars: A New Lotka-Volterra Competitive System and Analysis of Winning Strategies
We introduce a new model in population dynamics that describes two species sharing the same environmental resources in a situation of open hostility.
Analysis of PDEs 92D25, 37N25, 92B05, 34A26
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts.
Ranked #7 on
Semantic Segmentation
on FMB Dataset
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification.
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes.
