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Found 17 papers, 7 papers with code
Spectral Convolutional Conditional Neural Processes
Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes.
Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery
These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification.
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.
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.
Temporal Network Sampling
In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted.
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 DBLP Temporal
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.
Adaptive Shrinkage Estimation for Streaming Graphs
We propose a novel adaptive, single-pass sampling framework and unbiased estimators for higher-order network analysis of large streaming networks.
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking.
Streaming Network Embedding through Local Actions
Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges.
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
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.
Graphlet Decomposition: Framework, Algorithms, and Applications
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level.
