2 citations · 7 across the 6 of their papers we have counts for
6 papers
DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning
Shenghe Zheng, Hongzhi Wang, Tianyu Mu
Neural predictors have shown great potential in the evaluation process of neural architecture search (NAS). However, current predictor-based approaches overlook the fact that train…
Differentiable Self-Adaptive Learning Rate
Bozhou Chen, Hongzhi Wang, Chenmin Ba
Learning rate adaptation is a popular topic in machine learning. Gradient Descent trains neural nerwork with a fixed learning rate. Learning rate adaptation is proposed to accelera…
EEML: Ensemble Embedded Meta-learning
Geng Li, Boyuan Ren, Hongzhi Wang
To accelerate learning process with few samples, meta-learning resorts to prior knowledge from previous tasks. However, the inconsistent task distribution and heterogeneity is hard…
FIND:Explainable Framework for Meta-learning
Xinyue Shao, Hongzhi Wang, Xiao Zhu +1
Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique…
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
Chunnan Wang, Xingyu Chen, Chengyue Wu +1
Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design suitable forecasting model for the given time series data scenarios, is a novel…
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy
Chunnan Wang, Hongzhi Wang, Xiangyu Shi
Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource…