387 citations · 1.4k across the 16 of their papers we have counts for
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A Study on Regularization-Based Continual Learning Methods for Indic ASR
Gokul Adethya T, S. Jaya Nirmala
Indias linguistic diversity poses significant challenges for developing inclusive Automatic Speech Recognition (ASR) systems. Traditional multilingual models, which require simulta…
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar +9
Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program…
Theory and Experiments on Vector Quantized Autoencoders
Aurko Roy, Ashish Vaswani, Arvind Neelakantan +1
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a su…
Tensor2Tensor for Neural Machine Translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo +10
Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer…
Fast Decoding in Sequence Models using Discrete Latent Variables
Łukasz Kaiser, Aurko Roy, Ashish Vaswani +4
Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult t…
One Model To Learn Them All
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer +4
Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involv…