12 citations · 30 across the 16 of their papers we have counts for
6 papers · 1 filter
Reducing the Adaptation Gap Through Reachable Fisher Geometry
Wasif Jalal, Sachin Deb, Asif Salekin
Parameter-efficient fine-tuning (PEFT) determines not only how many parameters are trained, but also which local directions a model can move in, so similar adapters can affect subg…
UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs
Ashe Neth, Sawinder kaur, Mohammad Nur Hossain Khan +3
Existing pruning methods are typically applied during training or compile time and often rely on structured sparsity. While compatible with low-power microcontrollers (MCUs), struc…
Human Heterogeneity Invariant Stress Sensing
Yi Xiao, Harshit Sharma, Sawinder Kaur +2
Stress affects physical and mental health, and wearable devices have been widely used to detect daily stress through physiological signals. However, these signals vary due to facto…
CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
Harshit Sharma, Shaily Roy, Asif Salekin
Modern human sensing applications often rely on data distributed across users and devices, where privacy concerns prevent centralized training. Federated Learning (FL) addresses th…
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning
Brian Testa, Yi Xiao, Harshit Sharma +2
Smart speaker voice assistants (VAs) such as Amazon Echo and Google Home have been widely adopted due to their seamless integration with smart home devices and the Internet of Thin…
Deadwooding: Robust Global Pruning for Deep Neural Networks
Sawinder Kaur, Ferdinando Fioretto, Asif Salekin
The ability of Deep Neural Networks to approximate highly complex functions is key to their success. This benefit, however, comes at the expense of a large model size, which challe…