Search Results for author:
Found 15 papers, 5 papers with code
Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation
To leverage generative learning for zero-shot cross-modality image segmentation, we propose a novel unsupervised image translation method.
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence
In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries.
EMP: Effective Multidimensional Persistence for Graph Representation Learning
This framework empowers the exploration of data by simultaneously varying multiple scale parameters.
Tensor-view Topological Graph Neural Network
Graph classification is an important learning task for graph-structured data.
Topological Pooling on Graphs
By invoking the machinery of persistent homology and the concept of landmarks, we propose a novel topological pooling layer and witness complex-based topological embedding mechanism that allow us to systematically integrate hidden topological information at both local and global levels.
Seven open problems in applied combinatorics
We present and discuss seven different open problems in applied combinatorics.
Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets
Nowadays, it is broadly recognized in the power system community that to meet the ever expanding energy sector's needs, it is no longer possible to rely solely on physics-based models and that reliable, timely and sustainable operation of energy systems is impossible without systematic integration of artificial intelligence (AI) tools.
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds.
BScNets: Block Simplicial Complex Neural Networks
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs.
Topological Relational Learning on Graphs
As a result, the new framework enables us to harness both the conventional information on the graph structure and information on the graph higher order topological properties.
Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity
As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever depend on the dynamics of environmental factors.
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting
Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex dependencies in multivariate spatio-temporal processes.
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms.
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever increasing interest.
Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds continues to gain an ever increasing interest.
