Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy
Fuente:
arXiv
Saved in:
| Main Authors: | Thurn, Yanick, Jefferson, Ro, Erdmenger, Johanna |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Wilsonian Renormalization of Neural Network Gaussian Processes
by: Howard, Jessica N., et al.
Published: (2024)
by: Howard, Jessica N., et al.
Published: (2024)
Gauge-covariant stochastic neural fields: Stability and finite-width effects
by: Terin, Rodrigo Carmo
Published: (2025)
by: Terin, Rodrigo Carmo
Published: (2025)
The neural networks with tensor weights and emergent fermionic Wick rules in the large-width limit
by: Huang, Guojun, et al.
Published: (2025)
by: Huang, Guojun, et al.
Published: (2025)
Learning topological defects formation with neural networks in a quantum phase transition
by: Shi, Han-Qing, et al.
Published: (2022)
by: Shi, Han-Qing, et al.
Published: (2022)
Bulk-boundary decomposition of neural networks
by: Lee, Donghee, et al.
Published: (2025)
by: Lee, Donghee, et al.
Published: (2025)
Towards Worst-Case Guarantees with Scale-Aware Interpretability
by: Greenspan, Lauren, et al.
Published: (2026)
by: Greenspan, Lauren, et al.
Published: (2026)
Phase diagram and eigenvalue dynamics of stochastic gradient descent in multilayer neural networks
by: Park, Chanju, et al.
Published: (2025)
by: Park, Chanju, et al.
Published: (2025)
Emergent weight morphologies in deep neural networks
by: de Jong, Pascal, et al.
Published: (2025)
by: de Jong, Pascal, et al.
Published: (2025)
Krylov space approach to Singular Value Decomposition in non-Hermitian systems
by: Nandy, Pratik, et al.
Published: (2024)
by: Nandy, Pratik, et al.
Published: (2024)
Computing frustration and near-monotonicity in deep neural networks
by: Wendin, Joel, et al.
Published: (2025)
by: Wendin, Joel, et al.
Published: (2025)
Finite-time Lyapunov exponents of deep neural networks
by: Storm, L., et al.
Published: (2023)
by: Storm, L., et al.
Published: (2023)
Topological Effects in Neural Network Field Theory
by: Ferko, Christian, et al.
Published: (2026)
by: Ferko, Christian, et al.
Published: (2026)
Lecture Notes on Statistical Physics and Neural Networks
by: Hohm, Olaf
Published: (2026)
by: Hohm, Olaf
Published: (2026)
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks
by: He, Tianyu, et al.
Published: (2024)
by: He, Tianyu, et al.
Published: (2024)
A Two-Phase Perspective on Deep Learning Dynamics
by: Koch, Robert de Mello, et al.
Published: (2025)
by: Koch, Robert de Mello, et al.
Published: (2025)
Neural Network Quantum Field Theory from Transformer Architectures
by: Ageev, Dmitry S., et al.
Published: (2026)
by: Ageev, Dmitry S., et al.
Published: (2026)
Bayesian RG Flow in Neural Network Field Theories
by: Howard, Jessica N., et al.
Published: (2024)
by: Howard, Jessica N., et al.
Published: (2024)
Detecting quantum chaos via pseudo-entropy
by: He, Song, et al.
Published: (2024)
by: He, Song, et al.
Published: (2024)
Gauge covariant neural network for quarks and gluons
by: Nagai, Yuki, et al.
Published: (2021)
by: Nagai, Yuki, et al.
Published: (2021)
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
by: D'Amico, Francesco, et al.
Published: (2025)
by: D'Amico, Francesco, et al.
Published: (2025)
The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets
by: Levi, Noam, et al.
Published: (2023)
by: Levi, Noam, et al.
Published: (2023)
Grokking Modular Polynomials
by: Doshi, Darshil, et al.
Published: (2024)
by: Doshi, Darshil, et al.
Published: (2024)
Random matrix theory of sparse neuronal networks with heterogeneous timescales
by: Chotibut, Thiparat, et al.
Published: (2025)
by: Chotibut, Thiparat, et al.
Published: (2025)
The twin peaks of learning neural networks
by: Demyanenko, Elizaveta, et al.
Published: (2024)
by: Demyanenko, Elizaveta, et al.
Published: (2024)
Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit
by: Zhang, Zhengkang
Published: (2024)
by: Zhang, Zhengkang
Published: (2024)
High-dimensional learning of narrow neural networks
by: Cui, Hugo
Published: (2024)
by: Cui, Hugo
Published: (2024)
Neural network representation of quantum systems
by: Hashimoto, Koji, et al.
Published: (2024)
by: Hashimoto, Koji, et al.
Published: (2024)
Spring-block theory of feature learning in deep neural networks
by: Shi, Cheng, et al.
Published: (2024)
by: Shi, Cheng, et al.
Published: (2024)
Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
by: Ahn, Byoungjoon, et al.
Published: (2024)
by: Ahn, Byoungjoon, et al.
Published: (2024)
Robust Reasoning as a Symmetry-Protected Topological Phase
by: Sung, Ilmo
Published: (2026)
by: Sung, Ilmo
Published: (2026)
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)
Supervised and Unsupervised protocols for hetero-associative neural networks
by: Alessandrelli, Andrea, et al.
Published: (2025)
by: Alessandrelli, Andrea, et al.
Published: (2025)
Deep neural networks from the perspective of ergodic theory
by: Zhang, Fan
Published: (2023)
by: Zhang, Fan
Published: (2023)
Fixed width treelike neural networks capacity analysis -- generic activations
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
High-dimensional manifold of solutions in neural networks: insights from statistical physics
by: Malatesta, Enrico M.
Published: (2023)
by: Malatesta, Enrico M.
Published: (2023)
Training neural networks with structured noise improves classification and generalization
by: Benedetti, Marco, et al.
Published: (2023)
by: Benedetti, Marco, et al.
Published: (2023)
Exact capacity of the \emph{wide} hidden layer treelike neural networks with generic activations
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
Bayes-optimal learning of an extensive-width neural network from quadratically many samples
by: Maillard, Antoine, et al.
Published: (2024)
by: Maillard, Antoine, et al.
Published: (2024)
How does training shape the Riemannian geometry of neural network representations?
by: Zavatone-Veth, Jacob A., et al.
Published: (2023)
by: Zavatone-Veth, Jacob A., et al.
Published: (2023)
Towards a theory of how the structure of language is acquired by deep neural networks
by: Cagnetta, Francesco, et al.
Published: (2024)
by: Cagnetta, Francesco, et al.
Published: (2024)
Similar Items
-
Wilsonian Renormalization of Neural Network Gaussian Processes
by: Howard, Jessica N., et al.
Published: (2024) -
Gauge-covariant stochastic neural fields: Stability and finite-width effects
by: Terin, Rodrigo Carmo
Published: (2025) -
The neural networks with tensor weights and emergent fermionic Wick rules in the large-width limit
by: Huang, Guojun, et al.
Published: (2025) -
Learning topological defects formation with neural networks in a quantum phase transition
by: Shi, Han-Qing, et al.
Published: (2022) -
Bulk-boundary decomposition of neural networks
by: Lee, Donghee, et al.
Published: (2025)