research
∙
05/15/2023
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Pre-trained language models (PLMs) have accomplished impressive achievem...
share
research
∙
03/08/2023
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
Data pruning aims to obtain lossless performances as training on the ori...
research
∙
08/08/2022
A Frequency-aware Software Cache for Large Recommendation System Embeddings
Deep learning recommendation models (DLRMs) have been widely applied in ...
research
∙
07/19/2022
Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI
The success of today's AI applications requires not only model training ...
research
∙
03/03/2022
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort throug...
research
∙
03/02/2022
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
Protein structure prediction is an important method for understanding ge...
research
∙
10/28/2021
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
The Transformer architecture has improved the performance of deep learni...
research
∙
08/12/2021
PatrickStar: Parallel Training of Pre-trained Models via a Chunk-based Memory Management
The pre-trained model (PTM) is revolutionizing Artificial intelligence (...
research
∙
05/30/2021
2.5-dimensional distributed model training
Data parallelism does a good job in speeding up the training. However, w...
research
∙
10/30/2020
Training EfficientNets at Supercomputer Scale: 83 Accuracy in One Hour
EfficientNets are a family of state-of-the-art image classification mode...
research
∙
04/01/2019
Reducing BERT Pre-Training Time from 3 Days to 76 Minutes
Large-batch training is key to speeding up deep neural network training ...
research
∙
01/24/2019
Combinational Q-Learning for Dou Di Zhu
Deep reinforcement learning (DRL) has gained a lot of attention in recen...
research
∙
09/14/2017
