TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
Xception: Deep learning with depthwise separable convolutions
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RamanBench unifies 74 datasets into the first large-scale reproducible benchmark for ML on Raman spectra, finding tabular foundation models outperform baselines but no method generalizes across datasets.
Muon optimizer outperforms AdamW in ViT training on two image datasets, with gains that depend on data augmentation strength and are linked to wider singular-value spread in QKV gradients and prevention of late-training mode collapse in MLP blocks.
citing papers explorer
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TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles
TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
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RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy
RamanBench unifies 74 datasets into the first large-scale reproducible benchmark for ML on Raman spectra, finding tabular foundation models outperform baselines but no method generalizes across datasets.
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Muon in Vision Transformers: Optimizer-Recipe Interactions and Gradient Spectra
Muon optimizer outperforms AdamW in ViT training on two image datasets, with gains that depend on data augmentation strength and are linked to wider singular-value spread in QKV gradients and prevention of late-training mode collapse in MLP blocks.