This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Abstract: Many safety-critical applications of neural networks, such as robotic control, require safety guarantees. This article introduces a method for ensuring the safety of learned models for ...

DDSP is a library of differentiable versions of common DSP functions (such as synthesizers, waveshapers, and filters). This allows these interpretable elements to be used as part of an deep learning ...

Economists have developed different types of models describing the interaction of agents in markets. Early models in general equilibrium theory describe agents taking prices as given and do not ...

The Uncertainty-Aware Fourier Ptychography (UA-FP) framework marks a transformative milestone in computational imaging, revolutionizing the way we address system uncertainties. This innovative ...

This important study introduces a fully differentiable variant of the Gillespie algorithm as an approximate stochastic simulation scheme for complex chemical reaction networks, allowing kinetic ...

Researchers and enthusiasts have been fascinated by the challenge of reverse-engineering complex behaviors that emerge from simple rules in cellular automata for decades. Traditionally, this field ...

The original version of this story appeared in Quanta Magazine. Calculus is a powerful mathematical tool. But for hundreds of years after its invention in the 17th century, it stood on a shaky ...

In the late 19th century, Karl Weierstrass invented a fractal-like function that was decried as nothing less than a “deplorable evil.” In time, it would transform the foundations of mathematics.

According to recent studies, a policy’s depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have ...