A compiler for a self-hosting Scheme subset produces a differentiable meta-circular interpreter supporting gradient-based co-optimization of program structure and continuous parameters.
Should we apply bias correction to global and regional climate model data?
4 Pith papers cite this work, alongside 45 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A universal shallow water equations solver integrates neural networks via differentiable programming to enable inverse modeling of flow resistance and physics discovery in river channels.
Hybrid neural-process model derives biokinetic parameters from genomic traits for soil organic matter turnover, with ecological constraints, and outperforms baselines on synthetic and real data.
dCLIMBA learns parametric bias corrections for GCM precipitation using differentiability to match observations, improving extreme distributions and showing partial generalization.
citing papers explorer
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Compile Once, Differentiate Everywhere: A Differentiable Meta-Circular Interpreter
A compiler for a self-hosting Scheme subset produces a differentiable meta-circular interpreter supporting gradient-based co-optimization of program structure and continuous parameters.
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Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming
A universal shallow water equations solver integrates neural networks via differentiable programming to enable inverse modeling of flow resistance and physics discovery in river channels.
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Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Hybrid neural-process model derives biokinetic parameters from genomic traits for soil organic matter turnover, with ecological constraints, and outperforms baselines on synthetic and real data.
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A Differentiable Framework for Global Circulation Model Precipitation Bias Correction
dCLIMBA learns parametric bias corrections for GCM precipitation using differentiability to match observations, improving extreme distributions and showing partial generalization.