:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Gupta, Kanan, Siegel, Jonathan W., Wojtowytsch, Stephan
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Optimization and Control 68T07
Accesso online:	https://arxiv.org/abs/2302.05515
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Nesterov acceleration in benignly non-convex landscapes
di: Gupta, Kanan, et al.
Pubblicazione: (2024)

Input Convex Kolmogorov Arnold Networks
di: Deschatre, Thomas, et al.
Pubblicazione: (2025)

Generative modeling of conditional probability distributions on the level-sets of collective variables
di: Akhyar, Fatima-Zahrae, et al.
Pubblicazione: (2025)

Exact Sequence Interpolation with Transformers
di: Alcalde, Albert, et al.
Pubblicazione: (2025)

Control randomisation approach for policy gradient and application to reinforcement learning in optimal switching
di: Denkert, Robert, et al.
Pubblicazione: (2024)

Explicit neural network classifiers for non-separable data
di: Ewald, Patrícia Muñoz
Pubblicazione: (2025)

Diagonal Linear Networks and the Lasso Regularization Path
di: Berthier, Raphaël
Pubblicazione: (2025)

Supplementary Materials to Graph Convolutional Branch and Bound
di: Sciandra, Lorenzo, et al.
Pubblicazione: (2024)

Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
di: Patel, Vivak, et al.
Pubblicazione: (2024)

Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization
di: Chrisnanto, Yulison Herry, et al.
Pubblicazione: (2025)

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
di: Dramko, Evan, et al.
Pubblicazione: (2025)

Tracking the Median of Gradients with a Stochastic Proximal Point Method
di: Schaipp, Fabian, et al.
Pubblicazione: (2024)

Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
di: Liu, Kang, et al.
Pubblicazione: (2024)

Convergence of gradient descent for deep neural networks
di: Chatterjee, Sourav
Pubblicazione: (2022)

Optimization Dynamics of Equivariant and Augmented Neural Networks
di: Nordenfors, Oskar, et al.
Pubblicazione: (2023)

Learning time-scales in two-layers neural networks
di: Berthier, Raphaël, et al.
Pubblicazione: (2023)

Error Bound Analysis for the Regularized Loss of Deep Linear Neural Networks
di: Chen, Po, et al.
Pubblicazione: (2025)

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization
di: Zhang, Hengrui, et al.
Pubblicazione: (2026)

Fixed-Point Neural Optimal Transport without Implicit Differentiation
di: Park, Yesom, et al.
Pubblicazione: (2026)

A Layer Separation Optimization Framework for Cross-Entropy Training in Deep Learning
di: Liu, Yaru, et al.
Pubblicazione: (2026)

Data Augmentation and Regularization for Learning Group Equivariance
di: Nordenfors, Oskar, et al.
Pubblicazione: (2025)

Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks
di: Hernández, Martín, et al.
Pubblicazione: (2024)

Terminally constrained flow-based generative models from an optimal control perspective
di: Gao, Weiguo, et al.
Pubblicazione: (2026)

A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions
di: Hoang, Nguyen Viet, et al.
Pubblicazione: (2026)

On the existence of minimizers in shallow residual ReLU neural network optimization landscapes
di: Dereich, Steffen, et al.
Pubblicazione: (2023)

On the existence of optimal shallow feedforward networks with ReLU activation
di: Dereich, Steffen, et al.
Pubblicazione: (2023)

Power Homotopy for Zeroth-Order Non-Convex Optimizations
di: Xu, Chen
Pubblicazione: (2025)

Global Optimization with A Power-Transformed Objective and Gaussian Smoothing
di: Xu, Chen
Pubblicazione: (2024)

Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
di: Jeong, Halyun, et al.
Pubblicazione: (2025)

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
di: Li, Zhong, et al.
Pubblicazione: (2020)

Resolving gradient pathology in physics-informed epidemiological models
di: Golooba, Nickson, et al.
Pubblicazione: (2026)

How to beat a Bayesian adversary
di: Ding, Zihan, et al.
Pubblicazione: (2024)

Relu and softplus neural nets as zero-sum turn-based games
di: Gaubert, Stephane, et al.
Pubblicazione: (2025)

Progressive Feedforward Collapse of ResNet Training
di: Wang, Sicong, et al.
Pubblicazione: (2024)

Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting
di: Gess, Benjamin, et al.
Pubblicazione: (2023)

An alternative formulation of attention pooling function in translation
di: Conti, Eddie
Pubblicazione: (2024)

Cluster-based classification with neural ODEs via control
di: Álvarez-López, Antonio, et al.
Pubblicazione: (2023)

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
di: Chizat, Lénaïc, et al.
Pubblicazione: (2026)

Progressive Power Homotopy for Non-convex Optimization
di: Xu, Chen
Pubblicazione: (2026)

Iso-Riemannian Optimization on Learned Data Manifolds
di: Diepeveen, Willem, et al.
Pubblicazione: (2025)