18 citations · 34 across the 5 of their papers we have counts for
7 papers
Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing
Matteo Grimaldi, David Klee, Ziling Chen +5
Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of the most challenging cases: de…
SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
Tong Jian, Aditya Thurvas Senthil Kumar, Xinyi Li +7
Slip detection is fundamental to dexterous manipulation, yet existing systems often lack precise characterization of detection latency and cross-platform generalization. We present…
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Tong Jian, Zifeng Wang, Yanzhi Wang +2
Adversarial pruning compresses models while preserving robustness. Current methods require access to adversarial examples during pruning. This significantly hampers training effici…
SparCL: Sparse Continual Learning on the Edge
Zifeng Wang, Zheng Zhan, Yifan Gong +7
Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the t…
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Batool Salehi, Guillem Reus-Muns, Debashri Roy +5
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed with…
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
Zifeng Wang, Tong Jian, Aria Masoomi +2
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier. In addition to the…
Learn-Prune-Share for Lifelong Learning
Zifeng Wang, Tong Jian, Kaushik Chowdhury +3
In lifelong learning, we wish to maintain and update a model (e.g., a neural network classifier) in the presence of new classification tasks that arrive sequentially. In this paper…