:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Dherin, Benoit, Munn, Michael
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2510.03470
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Margin-based Multiclass Generalization Bound via Geometric Complexity
par: Munn, Michael, et autres
Publié: (2024)

The Impact of Geometric Complexity on Neural Collapse in Transfer Learning
par: Munn, Michael, et autres
Publié: (2024)

Equivalence of Context and Parameter Updates in Modern Transformer Blocks
par: Goldwaser, Adrian, et autres
Publié: (2025)

Transmuting prompts into weights
par: Mazzawi, Hanna, et autres
Publié: (2025)

Learning without training: The implicit dynamics of in-context learning
par: Dherin, Benoit, et autres
Publié: (2025)

Learning by solving differential equations
par: Dherin, Benoit, et autres
Publié: (2025)

How iteration order influences convergence and stability in deep learning
par: Dherin, Benoit, et autres
Publié: (2025)

Corridor Geometry in Gradient-Based Optimization
par: Dherin, Benoit, et autres
Publié: (2024)

Grow, Don't Overwrite: Fine-tuning Without Forgetting
par: Adila, Dyah, et autres
Publié: (2026)

Deep Fusion: Efficient Network Training via Pre-trained Initializations
par: Mazzawi, Hanna, et autres
Publié: (2023)

A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
par: Munn, Michael, et autres
Publié: (2024)

Implicit regularization of deep residual networks towards neural ODEs
par: Marion, Pierre, et autres
Publié: (2023)

Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning
par: Munn, Humphrey, et autres
Publié: (2022)

The sampling complexity of learning invertible residual neural networks
par: Li, Yuanyuan, et autres
Publié: (2024)

Geometric States
par: Dherin, Benoit, et autres
Publié: (2024)

Scale-invariant Gaussian derivative residual networks
par: Perzanowski, Andrzej, et autres
Publié: (2026)

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
par: Thuy, Arthur, et autres
Publié: (2024)

Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolution
par: Munn, Humphrey, et autres
Publié: (2025)

RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Robots
par: Munn, Humphrey, et autres
Publié: (2026)

Fading memory as inductive bias in residual recurrent networks
par: Dubinin, Igor, et autres
Publié: (2023)

Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
par: Kilicsoy, A. O. M., et autres
Publié: (2024)

Explainability through uncertainty: Trustworthy decision-making with neural networks
par: Thuy, Arthur, et autres
Publié: (2024)

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
par: Chen, Wenqian, et autres
Publié: (2024)

Towards protein folding pathways by reconstructing protein residue networks with a policy-driven model
par: Khor, Susan
Publié: (2026)

Understanding the role of depth in the neural tangent kernel for overparameterized neural networks
par: St-Arnaud, William, et autres
Publié: (2025)

Size and depth of monotone neural networks: interpolation and approximation
par: Mikulincer, Dan, et autres
Publié: (2022)

Hard-constraint physics-residual networks enable robust extrapolation for hydrogen crossover prediction in PEM water electrolyzers
par: Kim, Yong-Woon, et autres
Publié: (2025)

Characterizing stable regions in the residual stream of LLMs
par: Janiak, Jett, et autres
Publié: (2024)

Convolutional neural network for early detection of lameness and irregularity in horses using an IMU sensor
par: Savoini, Benoît, et autres
Publié: (2025)

Time-aware UNet and super-resolution deep residual networks for spatial downscaling
par: Sipilä, Mika, et autres
Publié: (2025)

Memorization capacity of deep ReLU neural networks characterized by width and depth
par: Yang, Xin, et autres
Publié: (2026)

Speeding Up MACE: Low-Precision Tricks for Equivarient Force Fields
par: Benoit, Alexandre
Publié: (2025)

Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays
par: Ferrière, Arsène, et autres
Publié: (2026)

Dynamical transition in controllable quantum neural networks with large depth
par: Zhang, Bingzhi, et autres
Publié: (2023)

HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
par: Kirchgässner, Wilhelm, et autres
Publié: (2024)

Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
par: Sheng, Zihao, et autres
Publié: (2024)

Learning residue level protein dynamics with multiscale Gaussians
par: Bafna, Mihir, et autres
Publié: (2025)

Differentiable Tripartite Modularity for Clustering Heterogeneous Graphs
par: Hurpeau, Benoît
Publié: (2026)

Analysis of a multi-target linear shrinkage covariance estimator
par: Oriol, Benoit
Publié: (2024)

Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence
par: Oriol, Benoit
Publié: (2024)