7 citations · 11 across the 7 of their papers we have counts for
7 papers
FLIRT: Feedback Loop In-context Red Teaming
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy +6
Warning: this paper contains content that may be inappropriate or offensive. As generative models become available for public use in various applications, testing and analyzing vul…
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Vladislav Lialin, Stephen Rawls, David Chan +3
Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multi…
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition
David M. Chan, Shalini Ghosh, Ariya Rastrow +1
Despite improvements to the generalization performance of automated speech recognition (ASR) models, specializing ASR models for downstream tasks remains a challenging task, primar…
Disentangled Action Recognition with Knowledge Bases
Zhekun Luo, Shalini Ghosh, Devin Guillory +3
Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have trainin…
Content-Context Factorized Representations for Automated Speech Recognition
David M. Chan, Shalini Ghosh
Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features,…
Unified Modeling of Multi-Domain Multi-Device ASR Systems
Soumyajit Mitra, Swayambhu Nath Ray, Bharat Padi +6
Modern Automatic Speech Recognition (ASR) systems often use a portfolio of domain-specific models in order to get high accuracy for distinct user utterance types across different d…
Multi-Modal Pre-Training for Automated Speech Recognition
David M. Chan, Shalini Ghosh, Debmalya Chakrabarty +1
Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance. Unfortunately,…