Saved in:
| Main Authors: | Boursier, Etienne, Bowditch, Matthew, Englert, Matthias, Lazic, Ranko |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.22578 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias
by: Town, James, et al.
Published: (2026)
by: Town, James, et al.
Published: (2026)
First-order ANIL provably learns representations despite overparametrization
by: Yüksel, Oğuz Kaan, et al.
Published: (2023)
by: Yüksel, Oğuz Kaan, et al.
Published: (2023)
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
by: Boursier, Etienne, et al.
Published: (2022)
by: Boursier, Etienne, et al.
Published: (2022)
Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective
by: Boursier, Etienne, et al.
Published: (2025)
by: Boursier, Etienne, et al.
Published: (2025)
Early alignment in two-layer networks training is a two-edged sword
by: Boursier, Etienne, et al.
Published: (2024)
by: Boursier, Etienne, et al.
Published: (2024)
Simplicity bias and optimization threshold in two-layer ReLU networks
by: Boursier, Etienne, et al.
Published: (2024)
by: Boursier, Etienne, et al.
Published: (2024)
Penalising the biases in norm regularisation enforces sparsity
by: Boursier, Etienne, et al.
Published: (2023)
by: Boursier, Etienne, et al.
Published: (2023)
A survey on multi-player bandits
by: Boursier, Etienne, et al.
Published: (2022)
by: Boursier, Etienne, et al.
Published: (2022)
A weighted-likelihood framework for class imbalance in Bayesian prediction models
by: Lazic, Stanley E.
Published: (2025)
by: Lazic, Stanley E.
Published: (2025)
A Theoretical Framework for Grokking: Interpolation followed by Riemannian Norm Minimisation
by: Boursier, Etienne, et al.
Published: (2025)
by: Boursier, Etienne, et al.
Published: (2025)
Effects of noise on the overparametrization of quantum neural networks
by: García-Martín, Diego, et al.
Published: (2023)
by: García-Martín, Diego, et al.
Published: (2023)
Zero loss guarantees and explicit minimizers for generic overparametrized Deep Learning networks
by: Chen, Thomas, et al.
Published: (2025)
by: Chen, Thomas, et al.
Published: (2025)
Approximate information maximization for bandit games
by: Barbier-Chebbah, Alex, et al.
Published: (2023)
by: Barbier-Chebbah, Alex, et al.
Published: (2023)
Tractability from overparametrization: The example of the negative perceptron
by: Montanari, Andrea, et al.
Published: (2021)
by: Montanari, Andrea, et al.
Published: (2021)
ODE approximation for the Adam algorithm: General and overparametrized setting
by: Dereich, Steffen, et al.
Published: (2025)
by: Dereich, Steffen, et al.
Published: (2025)
Online Decision-Focused Learning
by: Capitaine, Aymeric, et al.
Published: (2025)
by: Capitaine, Aymeric, et al.
Published: (2025)
Optimal Design for Reward Modeling in RLHF
by: Scheid, Antoine, et al.
Published: (2024)
by: Scheid, Antoine, et al.
Published: (2024)
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality
by: Scheid, Antoine, et al.
Published: (2024)
by: Scheid, Antoine, et al.
Published: (2024)
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
by: Hassani, Hamed, et al.
Published: (2022)
by: Hassani, Hamed, et al.
Published: (2022)
Visualizing the loss landscapes of physics-informed neural networks
by: Rowan, Conor, et al.
Published: (2026)
by: Rowan, Conor, et al.
Published: (2026)
Benign landscape for Burer-Monteiro factorizations of MaxCut-type semidefinite programs
by: Endor, Faniriana Rakoto, et al.
Published: (2024)
by: Endor, Faniriana Rakoto, et al.
Published: (2024)
MD tree: a model-diagnostic tree grown on loss landscape
by: Zhou, Yefan, et al.
Published: (2024)
by: Zhou, Yefan, et al.
Published: (2024)
Exploring the loss landscape of regularized neural networks via convex duality
by: Kim, Sungyoon, et al.
Published: (2024)
by: Kim, Sungyoon, et al.
Published: (2024)
Phase retrieval via overparametrized nonconvex optimization: nonsmooth amplitude loss landscapes
by: McRae, Andrew D.
Published: (2025)
by: McRae, Andrew D.
Published: (2025)
On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
by: Lazić, Petra, et al.
Published: (2026)
by: Lazić, Petra, et al.
Published: (2026)
Visualizing the loss landscape of Self-supervised Vision Transformer
by: Lee, Youngwan, et al.
Published: (2024)
by: Lee, Youngwan, et al.
Published: (2024)
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
by: György, András, et al.
Published: (2025)
by: György, András, et al.
Published: (2025)
Architecture independent generalization bounds for overparametrized deep ReLU networks
by: Bapu, Anandatheertha, et al.
Published: (2025)
by: Bapu, Anandatheertha, et al.
Published: (2025)
How does the optimizer implicitly bias the model merging loss landscape?
by: Zhang, Chenxiang, et al.
Published: (2025)
by: Zhang, Chenxiang, et al.
Published: (2025)
Neural network optimization strategies and the topography of the loss landscape
by: Yu, Jianneng, et al.
Published: (2026)
by: Yu, Jianneng, et al.
Published: (2026)
Visualizing high-dimensional loss landscapes with Hessian directions
by: Böttcher, Lucas, et al.
Published: (2022)
by: Böttcher, Lucas, et al.
Published: (2022)
Dynamical loss functions shape landscape topography and improve learning in artificial neural networks
by: Pallero, Eduardo Lavin, et al.
Published: (2024)
by: Pallero, Eduardo Lavin, et al.
Published: (2024)
Using dynamic loss weighting to boost improvements in forecast stability
by: Caljon, Daan, et al.
Published: (2024)
by: Caljon, Daan, et al.
Published: (2024)
The loss landscape of deep linear neural networks: a second-order analysis
by: Achour, El Mehdi, et al.
Published: (2021)
by: Achour, El Mehdi, et al.
Published: (2021)
Rare anomalies require large datasets: About proving the existence of anomalies
by: Klüttermann, Simon, et al.
Published: (2025)
by: Klüttermann, Simon, et al.
Published: (2025)
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime
by: Camilli, Francesco, et al.
Published: (2025)
by: Camilli, Francesco, et al.
Published: (2025)
Benign Overfitting in Single-Head Attention
by: Magen, Roey, et al.
Published: (2024)
by: Magen, Roey, et al.
Published: (2024)
Rolling Ball Optimizer: Learning by ironing out loss landscape wrinkles
by: Belgoumri, Mohammed Djameleddine, et al.
Published: (2025)
by: Belgoumri, Mohammed Djameleddine, et al.
Published: (2025)
Incentivized Learning in Principal-Agent Bandit Games
by: Scheid, Antoine, et al.
Published: (2024)
by: Scheid, Antoine, et al.
Published: (2024)
Gradient descent in matrix factorization: Understanding large initialization
by: Chen, Hengchao, et al.
Published: (2023)
by: Chen, Hengchao, et al.
Published: (2023)
Similar Items
-
Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias
by: Town, James, et al.
Published: (2026) -
First-order ANIL provably learns representations despite overparametrization
by: Yüksel, Oğuz Kaan, et al.
Published: (2023) -
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
by: Boursier, Etienne, et al.
Published: (2022) -
Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective
by: Boursier, Etienne, et al.
Published: (2025) -
Early alignment in two-layer networks training is a two-edged sword
by: Boursier, Etienne, et al.
Published: (2024)