88 citations · 204 across the 28 of their papers we have counts for
11 papers · 1 filter
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models
Raphael Olivier, Hadi Abdullah, Bhiksha Raj
A targeted adversarial attack produces audio samples that can force an Automatic Speech Recognition (ASR) system to output attacker-chosen text. To exploit ASR models in real-world…
Training image classifiers using Semi-Weak Label Data
Anxiang Zhang, Ankit Shah, Bhiksha Raj
In Multiple Instance learning (MIL), weak labels are provided at the bag level with only presence/absence information known. However, there is a considerable gap in performance in…
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
Bronya Roni Chernyak, Bhiksha Raj, Tamir Hazan +1
This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy…
Exploiting Non-Linear Redundancy for Neural Model Compression
Muhammad A. Shah, Raphael Olivier, Bhiksha Raj
Deploying deep learning models, comprising of non-linear combination of millions, even billions, of parameters is challenging given the memory, power and compute constraints of the…
Non-Determinism in Neural Networks for Adversarial Robustness
Daanish Ali Khan, Linhong Li, Ninghao Sha +4
Recent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other…
Nonlinear Semi-Parametric Models for Survival Analysis
Chirag Nagpal, Rohan Sangave, Amit Chahar +3
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting…