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Zixuan Gong

7 accepted papers

2025

MindSimulator: Exploring Brain Concept Localization via Synthetic fMRI

ICLR 2025poster

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanism…

Cited by 0SourcePDFScholar
2025

MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction

AAAI 2025technical

Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and…

Cited by 8SourcePDFScholar
2025

Stability and Generalization of Zeroth-Order Decentralized Stochastic Gradient Descent with Changing Topology

AAAI 2025technical

Zeroth-order (ZO) optimization as the gradient-free method has become a powerful tool when the first-order gradient is unavailable or expensive to obtain, especially in decentralized learning scenarios where data and computational resources are distributed across multiple clients. There have been ma…

Cited by 0SourcePDFScholar
2025

Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization

ICLR 2025poster

Large language models (LLMs) have demonstrated remarkable in-context learning (ICL) abilities. However, existing theoretical analysis of ICL primarily exhibits two limitations: \textbf{(a) Limited \textit{i.i.d.} Setting.} Most studies focus on supervised function learning tasks where prompts are co…

Cited by 0SourcePDFScholar
2025

Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding

AAAI 2025technical

Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailored to each subject. The personalization limits th…

Cited by 0SourcePDFScholar
2024

MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization

ICML 2024poster

Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms of *inefficient data utilization*. It relies on a single contrastive supervision for each image-text pair during repres…

Cited by 3SourcePDFScholar
2024

NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction

NeurIPS 2024oral

Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of conti…