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Found 11 papers, 6 papers with code
Adaptive Conditional Quantile Neural Processes
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes.
MoReL: Multi-omics Relational Learning
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems.
Bayesian Graph Contrastive Learning
By learning distributional representations, we provide uncertainty estimates in downstream graph analytics tasks and increase the expressive power of the predictive model.
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale.
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
Network-principled deep generative models for designing drug combinations as graph sets
We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.
Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on
Dynamic Link Prediction
on Enron Emails
Semi-Implicit Graph Variational Auto-Encoders
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
Spatially-Coupled Neural Network Architectures
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network.
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks
Building a prediction model for transportation networks is challenging because spatio-temporal dependencies of traffic data in different roads are complex and the graph constructed from road networks is very large.
