The Prevalence of Neural Collapse in Neural Multivariate Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Andriopoulos, George, Dong, Zixuan, Guo, Li, Zhao, Zifan, Ross, Keith
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension
by: Andriopoulos, George, et al.
Published: (2025)

Cross Entropy versus Label Smoothing: A Neural Collapse Perspective
by: Guo, Li, et al.
Published: (2024)

Neural Multivariate Regression: Qualitative Insights from the Unconstrained Feature Model
by: Andriopoulos, George, et al.
Published: (2025)

Should We Ever Prefer Decision Transformer for Offline Reinforcement Learning?
by: Omori, Yumi, et al.
Published: (2025)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs
by: Dong, Zixuan, et al.
Published: (2022)

Deep Neural Regression Collapse
by: Rangamani, Akshay, et al.
Published: (2026)

Pre-training with Synthetic Data Helps Offline Reinforcement Learning
by: Wang, Zecheng, et al.
Published: (2023)

Operator Feature Neural Network for Symbolic Regression
by: Deng, Yusong, et al.
Published: (2024)

Can We Understand Plasticity Through Neural Collapse?
by: Bonifazi, Guglielmo, et al.
Published: (2024)

Model Collapse Demystified: The Case of Regression
by: Dohmatob, Elvis, et al.
Published: (2024)

Transformer Neural Processes - Kernel Regression
by: Jenson, Daniel, et al.
Published: (2024)

Preventing Model Collapse via Contraction-Conditioned Neural Filters
by: Han, Zongjian, et al.
Published: (2025)

Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective
by: Sun, Haixiang, et al.
Published: (2024)

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers
by: Súkeník, Peter, et al.
Published: (2025)

Time Series Imputation with Multivariate Radial Basis Function Neural Network
by: Jung, Chanyoung, et al.
Published: (2024)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)

Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
by: Westerhoff, Justus, et al.
Published: (2025)

Why Do Neural Networks Forget: A Study of Collapse in Continual Learning
by: Zhu, Yunqin, et al.
Published: (2026)

Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
by: Roth, Andreas, et al.
Published: (2023)

Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
by: Luthra, Achleshwar, et al.
Published: (2026)

Differential Privacy Mechanisms in Neural Tangent Kernel Regression
by: Gu, Jiuxiang, et al.
Published: (2024)

PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
by: Wu, Min, et al.
Published: (2024)

Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
by: Sriramulu, Abishek, et al.
Published: (2024)

Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks
by: Wang, Zepu, 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)

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse
by: He, Kun, et al.
Published: (2025)

Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning
by: Wang, Jinping, et al.
Published: (2025)

Scale-Agnostic Kolmogorov-Arnold Geometry in Neural Networks
by: Vanherreweghe, Mathew, et al.
Published: (2025)

One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
by: Gao, Mengzhou, et al.
Published: (2026)

Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View
by: Wang, Jinping, et al.
Published: (2026)

Anomaly Detection Based on Critical Paths for Deep Neural Networks
by: Zhao, Fangzhen, et al.
Published: (2025)

GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
by: Ranasinghe, Nisal, et al.
Published: (2023)

Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2025)

MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapse
by: Kim, Donghwan, et al.
Published: (2026)

OLR-WA: Online Weighted Average Linear Regression in Multivariate Data Streams
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Multi-Objective Neural Architecture Search by Learning Search Space Partitions
by: Zhao, Yiyang, et al.
Published: (2024)

ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
by: Scholl, Philipp, et al.
Published: (2023)

GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
by: Piao, Chengzhe, et al.
Published: (2024)

Can Kernel Methods Explain How the Data Affects Neural Collapse?
by: Kothapalli, Vignesh, et al.
Published: (2024)