activity
20152025
most citedDriver distraction detection and recognition using RGB-D sensor

76 citations · 312 across the 23 of their papers we have counts for

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Showing 2020Show all

9 papers · 1 filter

stat.ML202031 cited

Locally Linear Embedding and its Variants: Tutorial and Survey

Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1

This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this…

stat.ME202010 cited

Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review

Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh +2

This paper is a tutorial and literature review on sampling algorithms. We have two main types of sampling in statistics. The first type is survey sampling which draws samples from…

stat.ML202022 cited

Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey

Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1

Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, met…

cs.CV2020

Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition

Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of whic…

cs.LG2020

Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3

Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…

cs.LG20202 cited

Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces

Benyamin Ghojogh, Fakhri Karray, Mark Crowley

After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights int…