Implicit Deep Learning
Copyright policy )Implicit Deep Learning
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined. The implicit framework greatly simplifies the notation of deep learning, and opens up many new possibilities, in terms of novel architectures and algorithms, robustness analysis and design, interpretability, sparsity, and network architecture optimization.
FOS: Computer and information sciences, Numerical optimization and variational techniques, Computer Science - Machine Learning, deep learning, Machine Learning (stat.ML), robustness, deep equilibrium models, Perron-Frobenius theory, Machine Learning (cs.LG), Neural nets and related approaches to inference from stochastic processes, fixed-point equations, Implicit function theorems, Jacobians, transformations with several variables, adversarial attacks, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control
FOS: Computer and information sciences, Numerical optimization and variational techniques, Computer Science - Machine Learning, deep learning, Machine Learning (stat.ML), robustness, deep equilibrium models, Perron-Frobenius theory, Machine Learning (cs.LG), Neural nets and related approaches to inference from stochastic processes, fixed-point equations, Implicit function theorems, Jacobians, transformations with several variables, adversarial attacks, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control
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