In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.
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Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
A multiresolution complex Gabor dictionary with orthogonal matching pursuit produces consistent time-frequency features from multiple heart-sound segments that a vision transformer classifies into four systolic murmur types at 95.96% accuracy on the CirCor DigiScope dataset.
citing papers explorer
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In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.
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Improving Dictionary Learning with Gated Sparse Autoencoders
Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
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Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer
A multiresolution complex Gabor dictionary with orthogonal matching pursuit produces consistent time-frequency features from multiple heart-sound segments that a vision transformer classifies into four systolic murmur types at 95.96% accuracy on the CirCor DigiScope dataset.