Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
Fuente:
arXiv
Saved in:
| Main Authors: | Súkeník, Peter, Mondelli, Marco, Lampert, Christoph |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
by: Jacot, Arthur, et al.
Published: (2024)
by: Jacot, Arthur, et al.
Published: (2024)
Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers
by: Súkeník, Peter, et al.
Published: (2025)
by: Súkeník, Peter, et al.
Published: (2025)
Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data
by: Min, Hancheng, et al.
Published: (2025)
by: Min, Hancheng, et al.
Published: (2025)
The Exploration of Neural Collapse under Imbalanced Data
by: Liu, Haixia
Published: (2024)
by: Liu, Haixia
Published: (2024)
The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
by: Chaudhry, Faris
Published: (2026)
by: Chaudhry, Faris
Published: (2026)
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
by: Ma, Jianhao, et al.
Published: (2023)
by: Ma, Jianhao, et al.
Published: (2023)
Optimal Depth of Neural Networks
by: Qi, Qian
Published: (2025)
by: Qi, Qian
Published: (2025)
Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)
by: Micheli, Alessandro, et al.
Published: (2026)
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
by: Zhuang, Yubo, et al.
Published: (2023)
by: Zhuang, Yubo, et al.
Published: (2023)
Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
by: Hoffmann, Matthias K., et al.
Published: (2025)
by: Hoffmann, Matthias K., et al.
Published: (2025)
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
by: Cai, Yuhang, et al.
Published: (2025)
by: Cai, Yuhang, et al.
Published: (2025)
SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)
by: Sun, Ying, et al.
Published: (2024)
Deep Operator Neural Network Model Predictive Control
by: de Jong, Thomas Oliver, et al.
Published: (2025)
by: de Jong, Thomas Oliver, et al.
Published: (2025)
Efficient Duple Perturbation Robustness in Low-rank MDPs
by: Hu, Yang, et al.
Published: (2024)
by: Hu, Yang, et al.
Published: (2024)
Improved Scalable Lipschitz Bounds for Deep Neural Networks
by: Syed, Usman, et al.
Published: (2025)
by: Syed, Usman, et al.
Published: (2025)
Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
by: Pineda, Salvador, et al.
Published: (2024)
by: Pineda, Salvador, et al.
Published: (2024)
Compression-aware Training of Neural Networks using Frank-Wolfe
by: Zimmer, Max, et al.
Published: (2022)
by: Zimmer, Max, et al.
Published: (2022)
Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
by: Zhang, Zhong, et al.
Published: (2025)
by: Zhang, Zhong, et al.
Published: (2025)
Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
by: Qin, Zhen, et al.
Published: (2023)
by: Qin, Zhen, et al.
Published: (2023)
Reward Collapse in Aligning Large Language Models
by: Song, Ziang, et al.
Published: (2023)
by: Song, Ziang, et al.
Published: (2023)
State-Space NTK Collapse Near Bifurcations
by: Hazelden, James, et al.
Published: (2026)
by: Hazelden, James, et al.
Published: (2026)
Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groups
by: Wotte, Yannik P., et al.
Published: (2024)
by: Wotte, Yannik P., et al.
Published: (2024)
Calibrating Neural Networks' parameters through Optimal Contraction in a Prediction Problem
by: Gonzalo, Valdes
Published: (2024)
by: Gonzalo, Valdes
Published: (2024)
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
by: Anderson, Brendon G., et al.
Published: (2021)
by: Anderson, Brendon G., et al.
Published: (2021)
A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
by: Li, Anran, et al.
Published: (2025)
by: Li, Anran, et al.
Published: (2025)
Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime
by: Wu, Diyuan, et al.
Published: (2025)
by: Wu, Diyuan, et al.
Published: (2025)
Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
by: Sanguin, Gabriele, et al.
Published: (2025)
by: Sanguin, Gabriele, et al.
Published: (2025)
Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention
by: Súkeník, Peter, et al.
Published: (2026)
by: Súkeník, Peter, et al.
Published: (2026)
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)
by: Tucat, Matteo, et al.
Published: (2024)
Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations
by: Kumar, Akshay, et al.
Published: (2024)
by: Kumar, Akshay, et al.
Published: (2024)
Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)
by: Le, Bach C., et al.
Published: (2025)
Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling
by: Rosso, Haley, et al.
Published: (2025)
by: Rosso, Haley, et al.
Published: (2025)
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
by: Chen, Hao, et al.
Published: (2026)
by: Chen, Hao, et al.
Published: (2026)
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
by: Philippenko, Constantin, et al.
Published: (2024)
by: Philippenko, Constantin, et al.
Published: (2024)
How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression?
by: Lai, Kuo-Wei, et al.
Published: (2026)
by: Lai, Kuo-Wei, et al.
Published: (2026)
Bias-Optimal Bounds for SGD: A Computer-Aided Lyapunov Analysis
by: Cortild, Daniel, et al.
Published: (2025)
by: Cortild, Daniel, et al.
Published: (2025)
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
by: Zhang, Erica, et al.
Published: (2024)
by: Zhang, Erica, et al.
Published: (2024)
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
by: Feng, Miria, et al.
Published: (2024)
by: Feng, Miria, et al.
Published: (2024)
Provable Accelerated Convergence of Nesterov's Momentum for Deep ReLU Neural Networks
by: Liao, Fangshuo, et al.
Published: (2023)
by: Liao, Fangshuo, et al.
Published: (2023)
Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime
by: Zhang, Richard Y.
Published: (2021)
by: Zhang, Richard Y.
Published: (2021)
Similar Items
-
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
by: Jacot, Arthur, et al.
Published: (2024) -
Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers
by: Súkeník, Peter, et al.
Published: (2025) -
Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data
by: Min, Hancheng, et al.
Published: (2025) -
The Exploration of Neural Collapse under Imbalanced Data
by: Liu, Haixia
Published: (2024) -
The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
by: Chaudhry, Faris
Published: (2026)