Qi Wei - RankMe

23 papers A* 5Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Class-mismatched semi-supervised learning from a new perspective. Pattern Recognit. Rundong He, Zhongyi Han, Yiming Zhu, Xiushan Nie, Qi Wei, Yilong Yin
2026	J	`jnl`	Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks. CoRR Shuo He, Lang Feng, Qi Wei, Xin Cheng, Lei Feng, Bo An
2026	J	`jnl`	Variational Rectification Inference for Learning with Noisy Labels. CoRR Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2025	J	`jnl`	Correction: Variational Rectification Inference for Learning with Noisy Labels. Int. J. Comput. Vis. Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2025	J	`jnl`	Improving Generalizability and Undetectability for Targeted Adversarial Attacks on Multimodal Pre-trained Models. CoRR Zhifang Zhang, Jiahan Zhang, Shengjie Zhou, Qi Wei, Shuo He, Feng Liu, Lei Feng
2025	A*	`conf`	Influence-Based Fair Selection for Sample-Discriminative Backdoor Attack. AAAI Qi Wei, Shuo He, Jiahan Zhang, Lei Feng, Bo An
2025	A*	`conf`	Representation Surgery in Model Merging with Probabilistic Modeling. ICML Qi Wei, Shuo He, Enneng Yang, Tingcong Liu, Haobo Wang, Lei Feng, Bo An
2025	A*	`conf`	Test-Time Multimodal Backdoor Detection by Contrastive Prompting. ICML Yuwei Niu, Shuo He, Qi Wei, Zongyu Wu, Feng Liu, Lei Feng
2025	J	`jnl`	Variational Rectification Inference for Learning with Noisy Labels. Int. J. Comput. Vis. Haoliang Sun, Qi Wei, Lei Feng, Yupeng Hu, Fan Liu, Hehe Fan, Yilong Yin
2024	J	`jnl`	BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection. CoRR Yuwei Niu, Shuo He, Qi Wei, Feng Liu, Lei Feng
2024	A*	`conf`	Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data. ICML Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
2024	J	`jnl`	Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data. CoRR Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
2024	J	`jnl`	Converting Artificial Neural Networks to Ultralow-Latency Spiking Neural Networks for Action Recognition. IEEE Trans. Cogn. Dev. Syst. Hong You, Xian Zhong, Wenxuan Liu, Qi Wei, Wenxin Huang, Zhaofei Yu, Tiejun Huang
2024	J	`jnl`	Correction: Learning sample-aware threshold for semi-supervised learning. Mach. Learn. Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Rundong He, Yilong Yin
2024	J	`jnl`	Debiased Sample Selection for Combating Noisy Labels. CoRR Qi Wei, Lei Feng, Haobo Wang, Bo An
2024	J	`jnl`	Learning sample-aware threshold for semi-supervised learning. Mach. Learn. Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Rundong He, Yilong Yin
2023	A*	`conf`	Fine-Grained Classification with Noisy Labels. CVPR Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Chenhui Guo, Yilong Yin
2023	J	`jnl`	Fine-Grained Classification with Noisy Labels. CoRR Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Chenhui Guo, Yilong Yin
2023	J	`jnl`	Improving Generalization in Meta-Learning via Meta-Gradient Augmentation. CoRR Ren Wang, Haoliang Sun, Qi Wei, Xiushan Nie, Yuling Ma, Yilong Yin
2022	J	`jnl`	Learning to rectify for robust learning with noisy labels. Pattern Recognit. Haoliang Sun, Chenhui Guo, Qi Wei, Zhongyi Han, Yilong Yin
2022	—	`conf`	Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization. ECCV (30) Qi Wei, Haoliang Sun, Xiankai Lu, Yilong Yin
2022	J	`jnl`	Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization. CoRR Qi Wei, Haoliang Sun, Xiankai Lu, Yilong Yin
2021	J	`jnl`	Learning to Rectify for Robust Learning with Noisy Labels. CoRR Haoliang Sun, Chenhui Guo, Qi Wei, Zhongyi Han, Yilong Yin

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.