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09/20/2023
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
Generative Large Language Models (LLMs) have achieved remarkable advance...
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08/16/2023
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Large Language Models (LLMs) have achieved state-of-the-art performance ...
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02/18/2023
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
Generative Pre-trained Transformer (GPT) models have shown remarkable ca...
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11/18/2022
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production
Mixture of Experts (MoE) models with conditional execution of sparsely a...
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10/14/2022
AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers
Neural architecture search (NAS) has demonstrated promising results on i...
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08/14/2022
Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU
Multilingual Neural Machine Translation has been showing great success u...
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05/28/2022
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Sparsely activated transformers, such as Mixture of Experts (MoE), have ...
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10/08/2021
Taming Sparsely Activated Transformer with Stochastic Experts
Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can ...
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09/22/2021
Scalable and Efficient MoE Training for Multitask Multilingual Models
The Mixture of Experts (MoE) models are an emerging class of sparsely ac...
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10/26/2020
FastFormers: Highly Efficient Transformer Models for Natural Language Understanding
Transformer-based models are the state-of-the-art for Natural Language U...
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01/27/2019
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Background: The trend towards large-scale studies including population i...
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06/16/2018
Real-time Prediction of Segmentation Quality
Recent advances in deep learning based image segmentation methods have e...
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10/25/2017
