Bo Jiang

Works (3)

Updated: October 24th, 2023 05:00

2022 article

REFINING SELF-SUPERVISED LEARNING IN IMAGING: BEYOND LINEAR METRIC

2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 76–80.

By: B. Jiang n, H. Krim n , T. Wu n  & D. Cansever*

author keywords: Self-Supervised learning; Contrastive Learning; Jaccard Index; Non-linearity
topics (OpenAlex): Domain Adaptation and Few-Shot Learning; Remote-Sensing Image Classification; Advanced Image and Video Retrieval Techniques
TL;DR: A new statistical perspective is introduced, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: October 23, 2023

2021 article

DYNAMIC GRAPH LEARNING BASED ON GRAPH LAPLACIAN

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), pp. 1090–1094.

By: B. Jiang n, Y. Yu *, H. Krim n  & S. Smith *

author keywords: Dynamic Graph Learning; Graph Signal Processing; Sparse Signal; Convex Optimization
topics (OpenAlex): Advanced Graph Neural Networks; Face and Expression Recognition; Domain Adaptation and Few-Shot Learning
TL;DR: This work forms a quadratic objective functional of observed node signals over short time intervals, subjected to the proper regularization reflecting the graph smoothness and other dynamics involving the underlying graph’s Laplacian, as well as the time evolution smoothness of the underlyinggraph. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: November 29, 2021

2021 journal article

Dynamic Graph Learning: A Structure-Driven Approach

MATHEMATICS, 9(2).

By: B. Jiang n, Y. Huang n, A. Panahi*, Y. Yu *, H. Krim n  & S. Smith *

author keywords: dynamic graph learning; graph signal processing; sparse signal; convex optimization
topics (OpenAlex): Functional Brain Connectivity Studies; Neural dynamics and brain function; Advanced Neuroimaging Techniques and Applications
TL;DR: The purpose of this paper is to infer a dynamic graph as a global model of time-varying measurements at a set of network nodes, which captures both pairwise as well as higher order interactions among the nodes. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 15, 2021

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