424 citations · 435 across the 2 of their papers we have counts for
4 papers
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely belie…
Robust Speech Recognition Using Generative Adversarial Networks
Anuroop Sriram, Heewoo Jun, Yashesh Gaur +1
This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained wi…
Cold Fusion: Training Seq2Seq Models Together with Language Models
Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh +1
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and sp…
Reducing Bias in Production Speech Models
Eric Battenberg, Rewon Child, Adam Coates +13
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and…