2 citations · 2 across the 6 of their papers we have counts for
6 papers
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
Runzhong Wang, Yang Li, Junchi Yan +1
Combinatorial optimization (CO) is the fundamental problem at the intersection of computer science, applied mathematics, etc. The inherent hardness in CO problems brings up challen…
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach
Beichen Zhang, Xiaoxing Wang, Xiaohan Qin +1
Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the fin…
Boundary Matters: A Bi-Level Active Finetuning Framework
Han Lu, Yichen Xie, Xiaokang Yang +1
The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields, yet it faces the significant challenge of high sample annotation costs. To miti…
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
Han Lu, Xiaosong Jia, Yichen Xie +3
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm. One main bottleneck lies in its voracious appetite for high-quality labeled…
Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach
Han Lu, Siyu Sun, Yichen Xie +3
In the long-tailed recognition field, the Decoupled Training paradigm has demonstrated remarkable capabilities among various methods. This paradigm decouples the training process i…
Poisson Process for Bayesian Optimization
Xiaoxing Wang, Jiaxing Li, Chao Xue +5
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function throu…