19 citations · 41 across the 9 of their papers we have counts for
9 papers
Intra-class Patch Swap for Self-Distillation
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre…
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Matthew P. Buman +1
The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topologi…
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Ankita Shukla +4
Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is…
Leveraging Angular Distributions for Improved Knowledge Distillation
Eun Som Jeon, Hongjun Choi, Ankita Shukla +1
Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher n…
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustne…
PHYSFRAME: Type Checking Physical Frames of Reference for Robotic Systems
Sayali Kate, Michael Chinn, Hongjun Choi +2
A robotic system continuously measures its own motions and the external world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate sy…
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen +3
In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update…
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…
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy +2
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and c…