1 code implementation • NeurIPS 2023 • Jifan Zhang, Shuai Shao, Saurabh Verma, Robert Nowak
To address this, we propose the first adaptive algorithm selection strategy for deep active learning.
no code implementations • 18 Feb 2020 • Nima Noorshams, Saurabh Verma, Aude Hofleitner
Since its inception, Facebook has become an integral part of the online social community.
no code implementations • 31 Jan 2020 • Xinyue Hu, Haoji Hu, Saurabh Verma, Zhi-Li Zhang
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.
1 code implementation • 22 Sep 2019 • Saurabh Verma, Zhi-Li Zhang
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)
no code implementations • 3 Jun 2019 • Saurabh Agrawal, Saurabh Verma, Anuj Karpatne, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar
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.
no code implementations • 3 May 2019 • Saurabh Verma, Zhi-Li Zhang
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.
1 code implementation • 21 May 2018 • Saurabh Verma, Zhi-Li Zhang
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
no code implementations • 16 Feb 2018 • Saurabh Agrawal, Saurabh Verma, Gowtham Atluri, Anuj Karpatne, Stefan Liess, Angus Macdonald III, Snigdhansu Chatterjee, Vipin Kumar
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.
2 code implementations • NeurIPS 2017 • Saurabh Verma, Zhi-Li Zhang
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.