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09/02/2023
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could ...
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03/15/2023
A Comprehensive Study on Post-Training Quantization for Large Language Models
Post-training quantization () had been recently shown as a compromising ...
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03/13/2023
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant stri...
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01/14/2022
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
As the training of giant dense models hits the boundary on the availabil...
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07/13/2021
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V L) models rely on pre-trained vis...
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05/30/2021
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
Pruning is an effective method to reduce the memory footprint and comput...
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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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08/26/2020
Benchmarking Semi-supervised Federated Learning
Federated learning promises to use the computational power of edge devic...
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12/16/2019
