1 citations · 1 across the 7 of their papers we have counts for
7 papers
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam
Weiqi Zhai, Zhihai Wang, Jinghang Wang +33
Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions. However, community-led analyses…
Can MLLMs Absorb Math Reasoning Abilities from LLMs as Free Lunch?
Yijie Hu, Zihao Zhou, Kaizhu Huang +2
Math reasoning has been one crucial ability of large language models (LLMs), where significant advancements have been achieved in recent years. However, most efforts focus on LLMs…
Saving for the future: Enhancing generalization via partial logic regularization
Zhaorui Tan, Yijie Hu, Xi Yang +3
Generalization remains a significant challenge in visual classification tasks, particularly in handling unknown classes in real-world applications. Existing research focuses on the…
Disentangling Tabular Data Towards Better One-Class Anomaly Detection
Jianan Ye, Zhaorui Tan, Yijie Hu +3
Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a si…
Covariance-based Space Regularization for Few-shot Class Incremental Learning
Yijie Hu, Guanyu Yang, Zhaorui Tan +3
Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e.…
Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa +1
\texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClus…
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection
Jianan Ye, Yijie Hu, Xi Yang +3
Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during trainin…