29 citations · 40 across the 6 of their papers we have counts for
6 papers
Target-independent XLA optimization using Reinforcement Learning
Milan Ganai, Haichen Li, Theodore Enns +2
An important challenge in Machine Learning compilers like XLA is multi-pass optimization and analysis. There has been recent interest chiefly in XLA target-dependent optimization o…
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training
Hongzheng Chen, Cody Hao Yu, Shuai Zheng +3
Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off…
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud
Zhen Zhang, Shuai Zheng, Yida Wang +5
Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networki…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
Yinhe Zheng, Yida Wang, Pei Ke +2
This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiate…
Diversifying Dialog Generation via Adaptive Label Smoothing
Yida Wang, Yinhe Zheng, Yong Jiang +1
Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in…