157 citations · 192 across the 5 of their papers we have counts for
5 papers
Training Large ASR Encoders with Differential Privacy
Geeticka Chauhan, Steve Chien, Om Thakkar +2
Self-supervised learning (SSL) methods for large speech models have proven to be highly effective at ASR. With the interest in public deployment of large pre-trained models, there…
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin +6
ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the pr…
Temporal Multimodal Multivariate Learning
Hyoshin Park, Justice Darko, Niharika Deshpande +7
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observati…
Detecting Unintended Memorization in Language-Model-Fused ASR
W. Ronny Huang, Steve Chien, Om Thakkar +1
End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words. At the same…
Toward Training at ImageNet Scale with Differential Privacy
Alexey Kurakin, Shuang Song, Steve Chien +3
Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the tr…