56 citations · 223 across the 16 of their papers we have counts for
7 papers · 1 filter
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen +3
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-in…
OpenBox: A Generalized Black-box Optimization Service
Yang Li, Yu Shen, Wentao Zhang +9
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge…
Efficient Automatic CASH via Rising Bandits
Yang Li, Jiawei Jiang, Jinyang Gao +3
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimi…
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Yang Li, Yu Shen, Jiawei Jiang +3
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep lear…
Effective and Efficient Dropout for Deep Convolutional Neural Networks
Shaofeng Cai, Yao Shu, Gang Chen +3
Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitti…
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
Shaofeng Cai, Gang Chen, Beng Chin Ooi +1
Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a hug…
Deep Learning At Scale and At Ease
Wei Wang, Gang Chen, Haibo Chen +5
Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models a…