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Found 10 papers, 3 papers with code
A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.
Image Classification with Hierarchical Multigraph Networks
Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data.
Understanding Attention and Generalization in Graph Neural Networks
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness.
Ranked #25 on
Graph Classification
on D&D
Data-Efficient Mutual Information Neural Estimator
We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Ranked #12 on
Graph Classification
on NCI109
SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment
We demonstrate an intelligent conversational agent system designed for advancing human-machine collaborative tasks.
Human Motion Modeling using DVGANs
We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs).
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images.
Action-Affect Classification and Morphing using Multi-Task Representation Learning
We propose a new model that enhances the CRBM model with a factored multi-task component to become Multi-Task Conditional Restricted Boltzmann Machines (MTCRBMs).
Human Social Interaction Modeling Using Temporal Deep Networks
We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment.
