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Sergey M. Plis
The Mind Research Network
Machine learningInfomaxComputer scienceResting state fMRIUnsupervised learning
6Publications
2H-index
10Citations
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Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). Methods: We start with estimat...
1 CitationsSource
#1Noah Lewis (Georgia Institute of Technology)H-Index: 1
#1Noah Lewis (Georgia Institute of Technology)H-Index: 1
Last. Vince D. Calhoun (Georgia Institute of Technology)H-Index: 93
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Abstract Background In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides securit...
1 CitationsSource
#1Nicolas Gillis (University of Mons)H-Index: 21
#2Riyasat Ohib (Georgia Institute of Technology)
Last. Vamsi K. PotluruH-Index: 8
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As evident from deep learning, very large models bring improvements in training dynamics and representation power. Yet, smaller models have benefits of energy efficiency and interpretability. To get the benefits from both ends of the spectrum we often encourage sparsity in the model. Unfortunately, most existing approaches do not have a controllable way to request a desired value of sparsity in an interpretable parameter. In this paper, we design a new sparse projection method for a set of vecto...
#1Usman Mahmood (GSU: Georgia State University)
#2Mahfuzur Rahman (GSU: Georgia State University)
Last. Sergey M. Plis (GSU: Georgia State University)H-Index: 2
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Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the problem---signal dynamics---can be used to our advantage and noticeably improve classification performance on a range of discrimination tasks when traini...
#1Debbrata Kumar Saha (Georgia Institute of Technology)
#2Vince D. CalhounH-Index: 93
Last. Sergey M. Plis (Georgia Institute of Technology)H-Index: 2
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Visualization of high dimensional large-scale datasets via an embedding into a 2D map is a powerful exploration tool for assessing latent structure in the data and detecting outliers. It plays a vital role in neuroimaging field because sometimes it is the only way to perform quality control of large dataset. There are many methods developed to perform this task but most of them rely on the assumption that all samples are locally available for the computation. Specifically, one needs access to al...
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#1Rogers F. SilvaH-Index: 9
#2Sergey M. PlisH-Index: 2
Last. Vince D. CalhounH-Index: 93
view all 5 authors...
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structur...
1 Citations
#1Hafiz ImtiazH-Index: 9
#2Jafar MohammadiH-Index: 4
Last. Vince D. CalhounH-Index: 93
view all 7 authors...
Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at a single site. A recently proposed algorithm uses message-passing between sites and a central aggregator to perform a decentralize...
Sep 1, 2017 in MLSP (International Workshop on Machine Learning for Signal Processing)
#1Weizheng Yan (CAS: Chinese Academy of Sciences)H-Index: 2
#2Sergey M. Plis (CAS: Chinese Academy of Sciences)H-Index: 2
Last. Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
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Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7...
4 CitationsSource
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