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02/15/2023
Big Little Transformer Decoder
The recent emergence of Large Language Models based on the Transformer a...
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06/02/2022
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
The recently proposed Conformer model has become the de facto backbone m...
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07/02/2021
Learned Token Pruning for Transformers
A major challenge in deploying transformer models is their prohibitive i...
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03/31/2021
Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various...
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01/22/2021
Hessian-Aware Pruning and Optimal Neural Implant
Pruning is an effective method to reduce the memory footprint and FLOPs ...
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01/05/2021
I-BERT: Integer-only BERT Quantization
Transformer based models, like BERT and RoBERTa, have achieved state-of-...
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11/20/2020
HAWQV3: Dyadic Neural Network Quantization
Quantization is one of the key techniques used to make Neural Networks (...
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12/16/2019
PyHessian: Neural Networks Through the Lens of the Hessian
We present PyHessian, a new scalable framework that enables fast computa...
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10/07/2019
