15 citations · 35 across the 10 of their papers we have counts for
10 papers
Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment
Zhiyuan Li, Hailong Li, Anca L. Ralescu +5
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising ou…
On the ability of CNNs to extract color invariant intensity based features for image classification
Pradyumna Elavarthi, James Lee, Anca Ralescu
Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training di…
Semantic Segmentation of Porosity in 4D Spatio-Temporal X-ray μCT of Titanium Coated Ni wires using Deep Learning
Pradyumna Elavarthi, Arun Bhattacharjee, Ashley Paz y Puente +1
A fully convolutional neural network was used to measure the evolution of the volume fraction of two different Kirkendall pores during the homogenization of Ti coated Ni wires. Tra…
Learning Empirical Bregman Divergence for Uncertain Distance Representation
Zhiyuan Li, Ziru Liu, Anna Zou +1
Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep netw…
A Novel Collaborative Self-Supervised Learning Method for Radiomic Data
Zhiyuan Li, Hailong Li, Anca L. Ralescu +3
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, wh…
Learning Generalized Hybrid Proximity Representation for Image Recognition
Zhiyuan Li, Anca Ralescu
Recently, deep metric learning techniques received attention, as the learned distance representations are useful to capture the similarity relationship among samples and further im…
Latent Semantic Structure in Malicious Programs
John Musgrave, Temesguen Messay-Kebede, David Kapp +1
Latent Semantic Analysis is a method of matrix decomposition used for discovering topics and topic weights in natural language documents. This study uses Latent Semantic Analysis t…
A Novel Feature Representation for Malware Classification
John Musgrave, Temesguen Messay-Kebede, David Kapp +1
In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a…
Empirical Network Structure of Malicious Programs
John Musgrave, Alina Campan, Temesguen Messay-Kebede +2
A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classificatio…
On the Combinality of Evidence in the Dempster-Shafer Theory
Lotfi Zadeh, Anca Ralescu
In the current versions of the Dempster-Shafer theory, the only essential restriction on the validity of the rule of combination is that the sources of evidence must be statistical…