Junchi Yan

Transferable AutoML by Model Sharing Over Grouped Datasets

2019

Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure …

M-Mix

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Negative pairs, especially hard negatives as combined with common negatives (easy to discriminate), are essential in contrastive learning, which plays a role of avoiding degenerate solutions in the sense of constant representation across different instances. …

Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond

2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Despite the recent breakthrough of high accuracy deep graph matching (GM) over visual images, the robustness of deep GM models is rarely studied which yet has been revealed an important issue in modern deep nets, …

M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios

2023 · arXiv (Cornell University)

In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i.e., not seen during training), they always predict …

On the Role of Label Noise in the Feature Learning Process

2025 · arXiv (Cornell University)

Deep learning with noisy labels presents significant challenges. In this work, we theoretically characterize the role of label noise from a feature learning perspective. Specifically, we consider a signal-noise data distribution, where each sample comprises …