@article{jiang_krim_wu_cansever_2022, title={REFINING SELF-SUPERVISED LEARNING IN IMAGING: BEYOND LINEAR METRIC}, ISSN={["1522-4880"]}, DOI={10.1109/ICIP46576.2022.9897745}, abstractNote={We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning. Specifically, our proposed metric may be interpreted as a dependence measure between two adapted projections learned from the so-called latent representations. This is in contrast to the cosine similarity measure in the conventional contrastive learning model, which accounts for correlation information. To the best of our knowledge, this effectively non-linearly fused information embedded in the Jaccard similarity, is novel to self-supervision learning with promising results. The proposed approach is compared to two state-of-the-art self-supervised contrastive learning methods on three image datasets. We not only demonstrate its amenable applicability in current ML problems, but also its improved performance and training efficiency.}, journal={2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP}, author={Jiang, Bo and Krim, Hamid and Wu, Tianfu and Cansever, Derya}, year={2022}, pages={76–80} }
 @article{jiang_yu_krim_smith_2021, title={DYNAMIC GRAPH LEARNING BASED ON GRAPH LAPLACIAN}, DOI={10.1109/ICASSP39728.2021.9413744}, abstractNote={The purpose of this paper is to infer a global (collective) model of time-varying responses of a set of nodes as a dynamic graph, where the individual time series are respectively observed at each of the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality upon observing activities in different regions of the brain and possibly of individual neurons. We formulate the problem as 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 underlying graph. The resulting joint optimization is solved by a continuous relaxation and an introduced novel gradient-projection scheme. We apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to not only be viable but also efficiently computable.}, journal={2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)}, author={Jiang, Bo and Yu, Yiyi and Krim, Hamid and Smith, Spencer L.}, year={2021}, pages={1090–1094} }
 @article{jiang_huang_panahi_yu_krim_smith_2021, title={Dynamic Graph Learning: A Structure-Driven Approach}, volume={9}, ISSN={["2227-7390"]}, DOI={10.3390/math9020168}, abstractNote={The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality across all regions of the brain, and possibly at individual neurons. We formulate it as an optimization problem, a quadratic objective functional and tensor information of observed node signals over short time intervals. The proper regularization constraints reflect the graph smoothness and other dynamics involving the underlying graph’s Laplacian, as well as the time evolution smoothness of the underlying graph. The resulting joint optimization is solved by a continuous relaxation of the weight parameters and an introduced novel gradient-projection scheme. While the work may be applicable to any time-evolving data set (e.g., fMRI), we apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to be not only viable but also efficiently computable.}, number={2}, journal={MATHEMATICS}, author={Jiang, Bo and Huang, Yuming and Panahi, Ashkan and Yu, Yiyi and Krim, Hamid and Smith, Spencer L.}, year={2021}, month={Jan} }