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Found 18 papers, 12 papers with code
Towards More Accurate Uncertainty Estimation In Text Classification
The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy.
Graph Bayesian Optimization for Multiplex Influence Maximization
In this problem, the seed set is a combination of influential users and information.
Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
A surrogate function models uncertainty from the limited information.
XFlow: Benchmarking Flow Behaviors over Graphs
One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios.
Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges
Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades.
Memetic algorithms for Spatial Partitioning problems
However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems.
Demystifying Graph Convolution with a Simple Concatenation
We quantify the information overlap between graph topology, node features, and labels in order to determine graph convolution's representation power in the node classification task.
Understanding Influence Maximization via Higher-Order Decomposition
Given its vast application on online social networks, Influence Maximization (IM) has garnered considerable attention over the last couple of decades.
Sampling-based techniques for designing school boundaries
Motivated by these recent developments, we develop a set of similar sampling techniques for designing school boundaries based on the flip proposal.
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information
The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the real-time data while learning from multiple related time series.
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Deep learning's performance has been extensively recognized recently.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.
Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates.
Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator
RatioanlNet is proposed to integrate rational function and neural networks.
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers
One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset.
Multimodal Storytelling via Generative Adversarial Imitation Learning
Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload.
Learning to Fuse Music Genres with Generative Adversarial Dual Learning
FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning.
