Search Results for author:
Found 11 papers, 5 papers with code
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs.
A Contrastive Variational Graph Auto-Encoder for Node Clustering
Thanks to a newly identified term, our lower bound can escape Posterior Collapse and has more flexibility to account for the difference between the inference and generative models.
Graph Attention Network for Camera Relocalization on Dynamic Scenes
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment.
TopoDetect: Framework for Topological Features Detection in Graph Embeddings
TopoDetect is a Python package that allows the user to investigate if important topological features, such as the Degree of the nodes, their Triangle Count, or their Local Clustering Score, are preserved in the embeddings of graph representation models.
Modeling Regime Shifts in Multiple Time Series
The originality of our approach lies in modeling interactions between time series in regime identification and in modeling time-dependent regime transition probabilities, usually assumed to be static in existing work.
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models, due to the graph convolutional operation and the graph decoding design.
Ranked #1 on
Node Clustering
on Pubmed
Exploring the Representational Power of Graph Autoencoder
Furthermore, we ask if the presence of these structures in the embeddings is necessary for a better performance on the downstream tasks, such as clustering and classification.
Context Matters: Self-Attention for Sign Language Recognition
For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components.
Ranked #15 on
Sign Language Recognition
on RWTH-PHOENIX-Weather 2014
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Clustering using deep autoencoders has been thoroughly investigated in recent years.
Ranked #2 on
Image Clustering
on MNIST-full
Mixing syntagmatic and paradigmatic information for concept detection
In the last decades, philosophers have begun using empirical data for conceptual analysis, but corpus-based conceptual analysis has so far failed to develop, in part because of the absence of reliable methods to automatically detect concepts in textual data.
Detecting Large Concept Extensions for Conceptual Analysis
When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit.
