SLiR parameterizes linear relaxations by slope and uses shifting to compute sound bounds for general activation functions, enabling up to 7.8x more verified properties than prior methods.
On optimizing back-substitution methods for neural network verification,
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A portable single-board-computer AI music platform and five case studies demonstrate that remapping inputs, interleaving fast and slow controls, small artist datasets, and cheap hardware can open new artist-centered design spaces for intelligent instruments.
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Shifting-based Optimizable Linear Relaxations for General Activation Functions
SLiR parameterizes linear relaxations by slope and uses shifting to compute sound bounds for general activation functions, enabling up to 7.8x more verified properties than prior methods.
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Opening the Design Space: Two Years of Performance with Intelligent Musical Instruments
A portable single-board-computer AI music platform and five case studies demonstrate that remapping inputs, interleaving fast and slow controls, small artist datasets, and cheap hardware can open new artist-centered design spaces for intelligent instruments.