CRONOS introduces scalable convex optimization for two-layer neural networks reaching ImageNet scale, with CRONOS-AM extending to arbitrary multi-layer architectures while matching tuned deep learning performance.
Model selection and estimation in regression with grouped variables
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SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.
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CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
CRONOS introduces scalable convex optimization for two-layer neural networks reaching ImageNet scale, with CRONOS-AM extending to arbitrary multi-layer architectures while matching tuned deep learning performance.
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Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.