Cover Image

The Double Descent Behavior in Two Layer Neural Network for Binary Classification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abeykoon, Chathurika S, Beknazaryan, Aleksandr, Sang, Hailin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
by: Wang, Puyu, et al.
Published: (2023)

Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)

How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks
by: Zhou, Mo, et al.
Published: (2024)

On the Lipschitz Constant of Deep Networks and Double Descent
by: Gamba, Matteo, et al.
Published: (2023)

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
by: Moniri, Behrad, et al.
Published: (2026)

DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?
by: Quétu, Victor, et al.
Published: (2023)

Bayesian Double Descent
by: Polson, Nick, et al.
Published: (2025)

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
by: Dandi, Yatin, et al.
Published: (2024)

Manipulating Sparse Double Descent
by: Zhang, Ya Shi
Published: (2024)

Rethinking Benign Overfitting in Two-Layer Neural Networks
by: Xu, Ruichen, et al.
Published: (2025)

Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
by: Jordahn, Mikkel, et al.
Published: (2024)

Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects
by: Alrashdi, Ayed M., et al.
Published: (2026)

KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks
by: Hong, Jinyung, et al.
Published: (2021)

Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent
by: Chou, Chi-Ning, et al.
Published: (2026)

Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
by: Cai, Yuhang, et al.
Published: (2024)

Dropout Drops Double Descent
by: Yang, Tian-Le, et al.
Published: (2023)

Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training
by: Ma, Yuhan, et al.
Published: (2024)

Double Descent and Other Interpolation Phenomena in GANs
by: Luzi, Lorenzo, et al.
Published: (2021)

Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)

Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
by: Hsiao, Yen-Che, et al.
Published: (2024)

Towards Understanding Epoch-wise Double descent in Two-layer Linear Neural Networks
by: Olmin, Amanda, et al.
Published: (2024)

Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge
by: Ghawaly, James, et al.
Published: (2025)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
by: Ma, Wenquan, et al.
Published: (2026)

Astrometric Binary Classification Via Artificial Neural Networks
by: Smith, Joe
Published: (2024)

Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization
by: Boža, Vladimír, et al.
Published: (2024)

Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
by: Mahmud, Jishnu, et al.
Published: (2023)

Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis
by: Khurana, Varun, et al.
Published: (2024)

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
by: Collins, Liam, et al.
Published: (2023)

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
by: Dandi, Yatin, et al.
Published: (2023)

Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
by: Zhang, Han, et al.
Published: (2024)

Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra
by: Worschech, Roman, et al.
Published: (2024)

On The Presence of Double-Descent in Deep Reinforcement Learning
by: Veselý, Viktor, et al.
Published: (2025)

Block Coordinate Descent for Neural Networks Provably Finds Global Minima
by: Akiyama, Shunta
Published: (2025)

Class-wise Activation Unravelling the Engima of Deep Double Descent
by: Gu, Yufei
Published: (2024)

Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
by: Tsilivis, Nikolaos, et al.
Published: (2024)

Convex Formulations for Training Two-Layer ReLU Neural Networks
by: Prakhya, Karthik, et al.
Published: (2024)

On the Theory of Continual Learning with Gradient Descent for Neural Networks
by: Taheri, Hossein, et al.
Published: (2025)

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)

Information Plane Analysis of Binary Neural Networks
by: Nothnagel, Maximilian, et al.
Published: (2026)