NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
arXiv preprint arXiv:2304.06446 , year=
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NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.
citing papers explorer
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Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
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NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
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HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
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Text-Guided Multi-Scale Frequency Representation Adaptation
FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.