DEL is a new loss for LLM numerical learning that applies supervised digit entropy optimization and extends to floating-point numbers, showing improved accuracy and distance metrics over prior methods on math benchmarks.
Visualizing data using t-sne.Journal of Machine Learning Research, 9(11)
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Complex Diffusion Maps with ω-parameterized complex kernels reveal inherent harmonic representations and improve discrimination over real-valued diffusion methods on synthetic data, fMRI, and EEG.
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DEL: Digit Entropy Loss for Numerical Learning of Large Language Models
DEL is a new loss for LLM numerical learning that applies supervised digit entropy optimization and extends to floating-point numbers, showing improved accuracy and distance metrics over prior methods on math benchmarks.
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Complex Diffusion Maps with $\omega$-Parameterized Kernels Revealing Inherent Harmonic Representations
Complex Diffusion Maps with ω-parameterized complex kernels reveal inherent harmonic representations and improve discrimination over real-valued diffusion methods on synthetic data, fMRI, and EEG.