105 citations · 195 across the 28 of their papers we have counts for
9 papers · 1 filter
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Tooba Imtiaz, Morgan Kohler, Jared Miller +5
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can d…
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zifeng Wang, Zizhao Zhang, Jacob Devlin +6
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We presen…
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…
Boundary-Aware Uncertainty for Feature Attribution Explainers
Davin Hill, Aria Masoomi, Max Torop +2
Post-hoc explanation methods have become a critical tool for understanding black-box classifiers in high-stakes applications. However, high-performing classifiers are often highly…
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…
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Zulqarnain Khan, Davin Hill, Aria Masoomi +2
Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explai…
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi +8
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store pa…
Deep Layer-wise Networks Have Closed-Form Weights
Chieh Wu, Aria Masoomi, Arthur Gretton +1
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP). To better mimic the brain, training a network \texti…
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…