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Found 16 papers, 11 papers with code
Reasoning about Ambiguous Definite Descriptions
But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language.
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph neural networks (GNNs) learn the representation of nodes in a graph by aggregating the neighborhood information in various ways.
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure.
Relational Graph Convolutional Networks: A Closer Look
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN).
Finding Motifs in Knowledge Graphs using Compression
We introduce a method to find network motifs in knowledge graphs.
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction
Many traffic prediction applications rely on uncertainty estimates instead of the mean prediction.
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain.
Exploiting Temporality for Semi-Supervised Video Segmentation
In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated.
End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks
We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information.
Three Tools for Practical Differential Privacy
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy.
A tutorial on MDL hypothesis testing for graph analysis
This document provides a tutorial description of the use of the MDL principle in complex graph analysis.
Learning sparse transformations through backpropagation
Many transformations in deep learning architectures are sparsely connected.
Deep Learning for Classification Tasks on Geospatial Vector Polygons
In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks.
An Expectation-Maximization Algorithm for the Fractal Inverse Problem
We present an Expectation-Maximization algorithm for the fractal inverse problem: the problem of fitting a fractal model to data.
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
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Large-scale network motif analysis using compression
We introduce a new method for finding network motifs: interesting or informative subgraph patterns in a network.
