10 citations · 16 across the 7 of their papers we have counts for
8 papers
DFWLayer: Differentiable Frank-Wolfe Optimization Layer
Zixuan Liu, Liu Liu, Xueqian Wang +1
Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks. This paper propo…
Over-training with Mixup May Hurt Generalization
Zixuan Liu, Ziqiao Wang, Hongyu Guo +1
Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of…
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
Mehmet Saygın Seyfioğlu, Zixuan Liu, Pranav Kamath +4
We propose a novel framework for Alzheimer's disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), wh…
Gradient-based Bi-level Optimization for Deep Learning: A Survey
Can Chen, Xi Chen, Chen Ma +2
Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extracti…
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Xin Zhang, Zixuan Liu, Kaiwen Xiao +5
Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes…
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
Weixin Liang, Yanhao Jiang, Zixuan Liu
Images are more than a collection of objects or attributes -- they represent a web of relationships among interconnected objects. Scene Graph has emerged as a new modality for a st…
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Zixuan Liu, Ehsan Adeli, Kilian M. Pohl +1
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision pr…
Longitudinal Self-Supervised Learning
Qingyu Zhao, Zixuan Liu, Ehsan Adeli +1
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As gr…