Search Results for author:
Found 6 papers, 1 papers with code
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.
Node Injection Attacks on Graphs via Reinforcement Learning
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes application to three Brain Computer Interface (BCI) applications.
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement
Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks.
Compositional Stochastic Average Gradient for Machine Learning and Related Applications
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).
