piecewise linear function

Decomposition Polyhedra of Piecewise Linear Functions ICLR 2025 Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks AISTATS 2025 Meta-Learning Priors Using Unrolled Proximal Networks ICLR 2024 What do Graph Neural Networks learn? Insights from Tropical Geometry NeurIPS 2024 Optimal Nonlinearities Improve Generalization Performance of Random Features ACML 2023 A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions JMLR 2022 Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions NeurIPS 2022 Approximating Lipschitz continuous functions with GroupSort neural networks AISTATS 2021 Nested Barycentric Coordinate System as an Explicit Feature Map AISTATS 2021 On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity AISTATS 2021 Learning to Approximate a Bregman Divergence NeurIPS 2020 Reverse-engineering deep ReLU networks ICML 2020 Complexity of Linear Regions in Deep Networks ICML 2019 Fast Algorithms for Segmented Regression ICML 2016 Coresets for k-Segmentation of Streaming Data NeurIPS 2014 A POMDP Extension with Belief-dependent Rewards NeurIPS 2010 Learning Monotonic Transformations for Classification NeurIPS 2007