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Found 15 papers, 7 papers with code
Following the Human Thread in Social Navigation
We propose the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
Adaptive Point Transformer
The recent surge in 3D data acquisition has spurred the development of geometric deep learning models for point cloud processing, boosted by the remarkable success of transformers in natural language processing.
GATSY: Graph Attention Network for Music Artist Similarity
The artist similarity quest has become a crucial subject in social and scientific contexts.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it.
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework.
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
One novelty of DEA is that we can use a discrete yet learnable adjacency matrix in our fine-tuning.
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman isomorphism test.
A Meta-Learning Approach for Training Explainable Graph Neural Networks
Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms.
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Furthermore, to better evaluate the gains, we propose a new dyadic group definition to measure the bias of a link prediction task when paired with group-based fairness metrics.
Distributed Training of Graph Convolutional Networks
After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents.
Adaptive Propagation Graph Convolutional Network
Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes.
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention.
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
We also explore a few extensions to the basic architecture involving the use of residual connections between layers, and of global statistics computed from the data set to improve the accuracy.
