1 citations · 2 across the 9 of their papers we have counts for
10 papers
Rethinking Attention Locality in Spiking Transformers
Zeqi Zheng, Zizheng Zhu, Yuping Yan +3
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation, yet their Softmax-free Spiking Self-Attention (SSA) struggles to es…
Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction
Hangxuan Li, Renjun Jia, Xuezhang Wu +3
Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engi…
OmniMER: Auxiliary-Enhanced LLM Adaptation for Indonesian Multimodal Emotion Recognition
Xueming Yan, Boyan Xu, Yaochu Jin +7
Indonesian, spoken by over 200 million people, remains underserved in multimodal emotion recognition research despite its dominant presence on Southeast Asian social media platform…
Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs
Yuanshuai Li, Yuping Yan, Junfeng Tang +3
Multimodal Large Language Models (MLLMs) have significantly improved the performance of various tasks, but continue to suffer from visual hallucinations, a critical issue where gen…
STF: Shallow-Level Temporal Feedback to Enhance Spiking Transformers
Zeqi Zheng, Zizheng Zhu, Yingchao Yu +5
Transformer-based Spiking Neural Networks (SNNs) suffer from a great performance gap compared to floating-point \mbox{Artificial} Neural Networks (ANNs) due to the binary nature of…
Sparse Autoencoders Bridge The Deep Learning Model and The Brain
Ziming Mao, Jia Xu, Zeqi Zheng +4
We present SAE-BrainMap, a novel framework that directly aligns deep learning visual model representations with voxel-level fMRI responses using sparse autoencoders (SAEs). First,…