6 papers
Dual Certified White-Box Inference for Input Convex Neural Networks
Kang Liu, Jianchen Hu, Wei Peng
Input convex neural networks (ICNNs) are used to learn convex objectives whose minimizers define decisions, making efficient and reliable optimization central to inference. At nons…
Budget-aware Auto Optimizer Configurator
Kang Liu, Wei Peng, Jianchen Hu
Optimizer states occupy massive GPU memory in large-scale model training. However, gradients in different network blocks exhibit distinct behaviors, such as varying directional sta…
OPBO: Order-Preserving Bayesian Optimization
Wei Peng, Jianchen Hu, Kang Liu +1
Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for app…
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language Models
Wei Peng, Kang Liu, Jianchen Hu +1
Prompt learning is one of the most effective paradigms for adapting pre-trained vision-language models (VLMs) to the biomedical image classification tasks in few shot scenarios. Ho…
Learning based convex approximation for constrained parametric optimization
Kang Liu, Wei Peng, Jianchen Hu
We propose an input convex neural network (ICNN)-based self-supervised learning framework to solve continuous constrained optimization problems. By integrating the augmented Lagran…
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
Wei Peng, Kang Liu, Jiaxi Shi +1
The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant…