1.7k citations · 2.5k across the 48 of their papers we have counts for
4 papers · 1 filter
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
Ioannis Papakis, Abhijit Sarkar, Anuj Karpatne
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching f…
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach
M. Maruf, Anuj Karpatne
The objective of unsupervised graph representation learning (GRL) is to learn a low-dimensional space of node embeddings that reflect the structure of a given unlabeled graph. Exis…
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems
Mohannad Elhamod, Jie Bu, Christopher Singh +5
Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network out…
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitatio…