1 code implementation • 2 May 2018 • Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
1 code implementation • 13 Jul 2018 • Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
no code implementations • 22 Mar 2018 • Ashkan Panahi, Hamid Krim, Liyi Dai
Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e. g. number of layers in DL).
no code implementations • 11 Mar 2018 • Shahin Mahdizadehaghdam, Ashkan Panahi, Hamid Krim, Liyi Dai
Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics.
no code implementations • 24 Jul 2016 • Saba Emrani, Hamid Krim
We propose a geometric model-free causality measurebased on multivariate delay embedding that can efficiently detect linear and nonlinear causal interactions between time series with no prior information.
no code implementations • 31 Mar 2014 • Xiao Bian, Hamid Krim
Successful applications of sparse models in computer vision and machine learning imply that in many real-world applications, high dimensional data is distributed in a union of low dimensional subspaces.
no code implementations • 20 Jul 2018 • Siddharth Roheda, Benjamin S. Riggan, Hamid Krim, Liyi Dai
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i. e. transferring) knowledge from sensor data and enhancing low-resolution target detection.
no code implementations • 2 Oct 2014 • Saba Emrani, Hamid Krim
In this paper, we present a new approach for analyzing gene expression data that builds on topological characteristics of time series.
Quantitative Methods Algebraic Topology Genomics
no code implementations • 7 Mar 2019 • Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
no code implementations • 16 May 2013 • Saba Emrani, Thanos Gentimis, Hamid Krim
The objective of this study is to detect and quantify the periodic behavior of the signals using topological methods.
Algebraic Topology 55N35, 55N99, 55U99
no code implementations • 9 Mar 2019 • Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt Dubhashi, Hamid Krim
We develop a novel theoretical framework for understating OT schemes respecting a class structure.
no code implementations • 10 Jun 2019 • Siddharth Roheda, Hamid Krim, Benjamin S. Riggan
Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase.
no code implementations • 13 Jun 2019 • Wen Tang, Ashkan Panahi, Hamid Krim
Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes.
no code implementations • 20 Aug 2019 • Shahin Mahdizadehaghdam, Ashkan Panahi, Hamid Krim
To that end we start by dividing an image into multiple patches and modifying the role of the generative network from producing an entire image, at once, to creating a sparse representation vector for each image patch.
no code implementations • 13 Sep 2019 • Yuming Huang, Ashkan Panahi, Hamid Krim, Yiyi Yu, Spencer L. Smith
We present a novel adversarial framework for training deep belief networks (DBNs), which includes replacing the generator network in the methodology of generative adversarial networks (GANs) with a DBN and developing a highly parallelizable numerical algorithm for training the resulting architecture in a stochastic manner.
no code implementations • 23 Sep 2019 • Yuming Huang, Ashkan Panahi, Hamid Krim, Liyi Dai
We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer.
1 code implementation • 21 Oct 2019 • Siddharth Roheda, Hamid Krim
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning.
no code implementations • 18 Feb 2020 • Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
no code implementations • 18 Jun 2020 • Sally Ghanem, Ashkan Panahi, Hamid Krim, Ryan A. Kerekes
Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data.
no code implementations • 18 Jan 2017 • Hui Guan, Thanos Gentimis, Hamid Krim, James Keiser
We introduce the idea of Data Readiness Level (DRL) to measure the relative richness of data to answer specific questions often encountered by data scientists.
no code implementations • 10 Apr 2021 • Sally Ghanem, Siddharth Roheda, Hamid Krim
We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces.
no code implementations • 21 Apr 2021 • Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
no code implementations • 25 Feb 2022 • Bo Jiang, Hamid Krim, Tianfu Wu, Derya Cansever
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.
no code implementations • 26 Apr 2022 • Sally Ghanem, Hamid Krim
In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Network (CNN) formalism.
no code implementations • 2 Oct 2022 • Siddharth Roheda, Ashkan Panahi, Hamid Krim
This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
no code implementations • 17 Apr 2023 • Bo Jiang, Hamid Krim, Tianfu Wu, Derya Cansever
We integrate a metric correction term as well as a prior cluster structure in the source data of the OT-driven adaptation.
1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2020 • Siddharth Roheda, Hamid Krim
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning.