Image de couverture de livre

Critical feature learning in deep neural networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fischer, Kirsten, Lindner, Javed, Dahmen, David, Ringel, Zohar, Krämer, Michael, Helias, Moritz
Format:	Preprint
Publié:	2024
Sujets:	Disordered Systems and Neural Networks
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Lecture notes: From Gaussian processes to feature learning
par: Helias, Moritz, et autres
Publié: (2026)

Renormalization group for deep neural networks: Universality of learning and scaling laws
par: Coppola, Gorka Peraza, et autres
Publié: (2025)

From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
par: Rubin, Noa, et autres
Publié: (2025)

Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues
par: Kramp, Jakob, et autres
Publié: (2026)

Field theory for optimal signal propagation in ResNets
par: Fischer, Kirsten, et autres
Publié: (2023)

A unified theory of feature learning in RNNs and DNNs
par: Bauer, Jan P., et autres
Publié: (2026)

Effect of Synaptic Heterogeneity on Neuronal Coordination
par: Layer, Moritz, et autres
Publié: (2023)

Reduction of interaction order in hard combinatorial optimization via conditionally independent degrees of freedom
par: Ciobanu, Alexandru, et autres
Publié: (2025)

Applications of Statistical Field Theory in Deep Learning
par: Ringel, Zohar, et autres
Publié: (2025)

Demystifying Spectral Bias on Real-World Data
par: Lavie, Itay, et autres
Publié: (2024)

Algorithmic Task Capture, Computational Complexity, and Inductive Bias of Infinite Transformers
par: Davidovich, Orit, et autres
Publié: (2026)

Beyond-mean-field fluctuations for the solution of constraint satisfaction problems
par: Foos, Niklas, et autres
Publié: (2025)

Linking Network and Neuron-level Correlations by Renormalized Field Theory
par: Dick, Michael, et autres
Publié: (2023)

Grokking as a First Order Phase Transition in Two Layer Networks
par: Rubin, Noa, et autres
Publié: (2023)

Optimizing p-spin models through hypergraph neural networks and deep reinforcement learning
par: Zeng, Li, et autres
Publié: (2026)

Towards Understanding Inductive Bias in Transformers: A View From Infinity
par: Lavie, Itay, et autres
Publié: (2024)

Spring-block theory of feature learning in deep neural networks
par: Shi, Cheng, et autres
Publié: (2024)

Graph Neural Networks Do Not Always Oversmooth
par: Epping, Bastian, et autres
Publié: (2024)

Microscopic and collective signatures of feature learning in neural networks
par: Corti, Andrea, et autres
Publié: (2025)

Improving deep neural network performance through sampling
par: Ghantasala, Lakshmi A., et autres
Publié: (2025)

Arbitrage equilibrium and the emergence of universal microstructure in deep neural networks
par: Venkatasubramanian, Venkat, et autres
Publié: (2024)

Adaptive kernel predictors from feature-learning infinite limits of neural networks
par: Lauditi, Clarissa, et autres
Publié: (2025)

Statistical physics through the lens of real-space mutual information
par: Gökmen, Doruk Efe, et autres
Publié: (2021)

Topological mechanical neural networks as classifiers through in situ backpropagation learning
par: Li, Shuaifeng, et autres
Publié: (2025)

A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit
par: Pacelli, R., et autres
Publié: (2022)

Emergent weight morphologies in deep neural networks
par: de Jong, Pascal, et autres
Publié: (2025)

Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
par: D'Amico, Francesco, et autres
Publié: (2025)

Computing frustration and near-monotonicity in deep neural networks
par: Wendin, Joel, et autres
Publié: (2025)

Finite-time Lyapunov exponents of deep neural networks
par: Storm, L., et autres
Publié: (2023)

Impact of heavy-tailed synaptic strength distributions on self-sustained activity in networks of spiking neurons
par: Tönjes, Ralf, et autres
Publié: (2026)

Generalized hetero-associative neural networks
par: Agliari, Elena, et autres
Publié: (2024)

Compression theory for inhomogeneous systems
par: Gökmen, Doruk Efe, et autres
Publié: (2023)

High-dimensional learning of narrow neural networks
par: Cui, Hugo
Publié: (2024)

Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks
par: Shao, Yuxiu, et autres
Publié: (2024)

Partial annealing and pattern decorrelation in associative neural networks
par: Albanese, Linda, et autres
Publié: (2026)

Instant prediction of relaxation in moiré superlattices using neural networks
par: Belonovskii, Aleksei V., et autres
Publié: (2025)

Glassy dynamics near the interpolation transition in deep recurrent networks
par: Hertz, John, et autres
Publié: (2024)

Pattern Expansion of Spin Glasses
par: Shen, Mutian, et autres
Publié: (2026)

Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
par: Zhao, Yueqi, et autres
Publié: (2025)

Liquid and solid layers in a thermal deep learning machine
par: Huang, Gang, et autres
Publié: (2025)