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20132019
most citedDeep Dictionary Learning: A PARametric NETwork Approach

69 citations · 131 across the 11 of their papers we have counts for

collaborators

11 papers

cs.SI2019★ 23 cited

Community Detection and Improved Detectability in Multiplex Networks

Yuming Huang, Ashkan Panahi, Hamid Krim +1

We investigate the widely encountered problem of detecting communities in multiplex networks, such as social networks, with an unknown arbitrary heterogeneous structure. To improve…

cs.LG2019★ 1 cited

Analysis Dictionary Learning: An Efficient and Discriminative Solution

Wen Tang, Ashkan Panahi, Hamid Krim +1

Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-th…

eess.IV2018

Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks

Siddharth Roheda, Benjamin S. Riggan, Hamid Krim +1

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 targ…

cs.CV2018★ 35 cited

Analysis Dictionary Learning based Classification: Structure for Robustness

Wen Tang, Ashkan Panahi, Hamid Krim +1

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis di…

cs.CV2018★ 2 cited

Structured Analysis Dictionary Learning for Image Classification

Wen Tang, Ashkan Panahi, Hamid Krim +1

We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning. To achieve more…

cs.LG2018

Demystifying Deep Learning: A Geometric Approach to Iterative Projections

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 comp…