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Smooth Function Approximation by Deep Neural Networks with General Activation Functions [PDF]
Entropy, 2019 There has been a growing interest in expressivity of deep neural networks. However, most of the existing work about this topic focuses only on the specific activation function such as ReLU or sigmoid.
Ilsang Ohn, Yongdai Kim
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Function approximation method based on weights gradient descent in reinforcement learning
网络与信息安全学报, 2023 Function approximation has gained significant attention in reinforcement learning research as it effectively addresses problems with large-scale, continuous state, and action space.Although the function approximation algorithm based on gradient descent ...
Xiaoyan QIN +3 more
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Arbitrarily Accurate Analytical Approximations for the Error Function
Mathematical and Computational Applications, 2022 A spline-based integral approximation is utilized to define a sequence of approximations to the error function that converge at a significantly faster manner than the default Taylor series.
Roy M. Howard
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Schröder-Based Inverse Function Approximation
Axioms, 2023 Schröder approximations of the first kind, modified for the inverse function approximation case, are utilized to establish general analytical approximation forms for an inverse function.
Roy M. Howard
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Approximate functional differencing
SERIEs, 2023 AbstractInference on common parameters in panel data models with individual-specific fixed effects is a classic example of Neyman and Scott’s (Econometrica 36:1–32, 1948) incidental parameter problem (IPP). One solution to this IPP is functional differencing (Bonhomme in Econometrica 80(4):1337–1385, 2012), which works when the number of time ...
Dhaene, Geert, Weidner, Martin
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Automatic Convexity Deduction for Efficient Function’s Range Bounding
Mathematics, 2021 Reliable bounding of a function’s range is essential for deterministic global optimization, approximation, locating roots of nonlinear equations, and several other computational mathematics areas.
Mikhail Posypkin, Oleg Khamisov
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Approximately convex functions [PDF]
Proceedings of the American Mathematical Society, 1952 So far we have discussed the stability of various functional equations. In the present section, we consider the stability of a well-known functional inequality, namely the inequality defining convex functions: $$f\left( {\lambda x + \left( {1 - \lambda } \right)y} \right) \leqslant \lambda f\left( x \right) + \left( {1 - \lambda } \right)f\left( y \
Hyers, D. H., Ulam, S. M.
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Frontiers in Energy Research, 2021 Solving the energy crisis and environmental pollution requires large-scale access to distributed energy and the popularization of electric vehicles.
Yuchen Liu +5 more
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Multiple Function Approximation — A New Approach Using Asymmetric Complex Fuzzy Inference System [PDF]
Vietnam Journal of Computer Science, 2019 This paper proposes an asymmetric complex fuzzy inference system (ACFIS) that improves a conventional fuzzy inference system (FIS) in two ways. First, the proposed model uses the novel neural-net-like aim–object parts, making the model flexible, in terms
Chia-Hao Tu, Chunshien Li
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Digital Fixed-Point Low Powered Area Efficient Function Estimation for Implantable Devices
IEEE Access, 2022 This article introduces a new multiplier-less 32-bit fixed point architecture for estimating complex non-linear functions based on adapted shift only series expansions.
James B. Romaine +2 more
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