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

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 ...

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 ...

Abstract: Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D ...

The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this source of ...

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 ...

Differentiable Swift is an experimental language feature for the Swift language that is currently in the pitch phase of the Swift Evolution process. The goal of this feature is to provide first-class, ...