Input-convex neural networks
PulseAugur coverage of Input-convex neural networks — every cluster mentioning Input-convex neural networks across labs, papers, and developer communities, ranked by signal.
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New research proves W[1]-hardness for ICNN Lipschitz constant computation
Researchers have established the parameterized complexity of computing $L_p$-Lipschitz constants for two-layer input-convex neural networks (ICNNs). This problem is equivalent to maximizing the $L_p$-norm over a zonotop…
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Input Convex Neural Networks Offer Optimization Gains Over FNNs
Researchers have introduced Input Convex Neural Networks (ICNNs) as a superior alternative to traditional Feedforward Neural Networks (FNNs) for use in mathematical optimization problems. ICNNs offer computational advan…
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Tropical circuits with scalar multiplication gates analyzed · 2 sources tracked
Researchers have introduced tropical circuits with scalar multiplication gates, which utilize operations like max, addition, and multiplication by a positive constant. The study establishes exponential size lower bounds…
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AI models learn constitutive laws in mechanics and thermomechanics
Researchers have developed novel physics-informed neural network frameworks for discovering constitutive models in mechanics. One approach focuses on identifying anisotropic yield functions in plasticity by representing…
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New 'Lift' Method Enhances Input-Convex Neural Network Training
Researchers have introduced a novel training technique called "the lift" for input-convex neural networks (ICNNs), which are crucial for tasks like density estimation and Bayesian inference. Traditional methods struggle…