Search Results for author:
Found 9 papers, 4 papers with code
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
To address this, we propose the first adaptive algorithm selection strategy for deep active learning.
TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook
Since its inception, Facebook has become an integral part of the online social community.
Physics-Guided Deep Neural Networks for Power Flow Analysis
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning.
Ranked #3 on
Graph Classification
on COLLAB
(using extra training data)
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals.
Stability and Generalization of Graph Convolutional Neural Networks
In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting.
Graph Capsule Convolutional Neural Networks
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision.
Ranked #22 on
Graph Classification
on NCI1
Mining Sub-Interval Relationships In Time Series Data
In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time.
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation.
