23 citations · 30 across the 12 of their papers we have counts for
5 papers · 1 filter
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification
Hongjun Choi, Anirudh Som, Pavan Turaga
Standard deep learning models that employ the categorical cross-entropy loss are known to perform well at image classification tasks. However, many standard models thus obtained of…
A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues
Anirudh Som, Sujeong Kim, Bladimir Lopez-Prado +3
K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly asses…
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
Anirudh Som, Narayanan Krishnamurthi, Matthew Buman +1
Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challeng…
Topological Descriptors for Parkinson's Disease Classification and Regression Analysis
Afra Nawar, Farhan Rahman, Narayanan Krishnamurthi +2
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper,…
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
Hongjun Choi, Anirudh Som, Pavan Turaga
Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and tripl…