Unlearning regularization for Boltzmann Machines
Fuente:
arXiv
Saved in:
| Main Authors: | Ventura, Enrico, Cocco, Simona, Monasson, Rémi, Zamponi, Francesco |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Task learning through stimulation-induced plasticity in neural networks
by: Borra, Francesco, et al.
Published: (2024)
by: Borra, Francesco, et al.
Published: (2024)
Replica Theory of Spherical Boltzmann Machine Ensembles
by: Tulinski, Thomas, et al.
Published: (2026)
by: Tulinski, Thomas, et al.
Published: (2026)
Restoring balance: principled under/oversampling of data for optimal classification
by: Loffredo, Emanuele, et al.
Published: (2024)
by: Loffredo, Emanuele, et al.
Published: (2024)
Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity
by: Kühn, Tobias, et al.
Published: (2023)
by: Kühn, Tobias, et al.
Published: (2023)
Learning and Unlearning: Bridging classification, memory and generative modeling in Recurrent Neural Networks
by: Ventura, Enrico
Published: (2024)
by: Ventura, Enrico
Published: (2024)
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
by: Annesi, Brandon L., et al.
Published: (2024)
by: Annesi, Brandon L., et al.
Published: (2024)
Rare Trajectories in a Prototypical Mean-field Disordered Model: Insights into Landscape and Instantons
by: Charbonneau, Patrick, et al.
Published: (2025)
by: Charbonneau, Patrick, et al.
Published: (2025)
Dreaming improves memorization in a Hopfield model with bounded synaptic strength
by: Marinari, Enzo, et al.
Published: (2026)
by: Marinari, Enzo, et al.
Published: (2026)
Data augmentation enables label-specific generation of homologous protein sequences
by: Rosset, Lorenzo, et al.
Published: (2025)
by: Rosset, Lorenzo, et al.
Published: (2025)
Demolition and Reinforcement of Memories in Spin-Glass-like Neural Networks
by: Ventura, Enrico
Published: (2024)
by: Ventura, Enrico
Published: (2024)
Transition path sampling in Ising models on heterogeneous graphs
by: Cipolloni, Riccardo, et al.
Published: (2026)
by: Cipolloni, Riccardo, et al.
Published: (2026)
Further testing the validity of generalized heterogeneous-elasticity theory for low-frequency excitations in structural glasses
by: Schirmacher, Walter, et al.
Published: (2025)
by: Schirmacher, Walter, et al.
Published: (2025)
Modeling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Setting
by: Thériault, Robin, et al.
Published: (2024)
by: Thériault, Robin, et al.
Published: (2024)
Memorization and Generalization in Generative Diffusion under the Manifold Hypothesis
by: Achilli, Beatrice, et al.
Published: (2025)
by: Achilli, Beatrice, et al.
Published: (2025)
The Capacity of Modern Hopfield Networks under the Data Manifold Hypothesis
by: Achilli, Beatrice, et al.
Published: (2025)
by: Achilli, Beatrice, et al.
Published: (2025)
Training neural networks with structured noise improves classification and generalization
by: Benedetti, Marco, et al.
Published: (2023)
by: Benedetti, Marco, et al.
Published: (2023)
The effect of priors on Learning with Restricted Boltzmann Machines
by: Manzan, Gianluca, et al.
Published: (2024)
by: Manzan, Gianluca, et al.
Published: (2024)
Fluctuations and the limit of predictability in protein evolution
by: Rossi, Saverio, et al.
Published: (2024)
by: Rossi, Saverio, et al.
Published: (2024)
Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
by: Leone, Luca, et al.
Published: (2026)
by: Leone, Luca, et al.
Published: (2026)
Dataset-Free Weight-Initialization on Restricted Boltzmann Machine
by: Yasuda, Muneki, et al.
Published: (2024)
by: Yasuda, Muneki, et al.
Published: (2024)
Emergence of Distortions in High-Dimensional Guided Diffusion Models
by: Ventura, Enrico, et al.
Published: (2026)
by: Ventura, Enrico, et al.
Published: (2026)
Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions
by: Xu, Yizhou, et al.
Published: (2025)
by: Xu, Yizhou, et al.
Published: (2025)
Emergent time scales of epistasis in protein evolution
by: Di Bari, Leonardo, et al.
Published: (2024)
by: Di Bari, Leonardo, et al.
Published: (2024)
Comparing the effects of Boltzmann machines as associative memory in Generative Adversarial Networks between classical and quantum sampling
by: Urushibata, Mitsuru, et al.
Published: (2022)
by: Urushibata, Mitsuru, et al.
Published: (2022)
Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models
by: Del Bono, Luca Maria, et al.
Published: (2024)
by: Del Bono, Luca Maria, et al.
Published: (2024)
The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models
by: Nicoletti, Flavio, et al.
Published: (2026)
by: Nicoletti, Flavio, et al.
Published: (2026)
Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics
by: Di Bari, Leonardo, et al.
Published: (2026)
by: Di Bari, Leonardo, et al.
Published: (2026)
Causality and instability in wave propagation in random time-varying media
by: Pierrat, Romain, et al.
Published: (2024)
by: Pierrat, Romain, et al.
Published: (2024)
Inferring effective couplings with Restricted Boltzmann Machines
by: Decelle, Aurélien, et al.
Published: (2023)
by: Decelle, Aurélien, et al.
Published: (2023)
Fast training and sampling of Restricted Boltzmann Machines
by: Béreux, Nicolas, et al.
Published: (2024)
by: Béreux, Nicolas, et al.
Published: (2024)
Creating equilibrium glassy states via random particle bonding
by: Ozawa, Misaki, et al.
Published: (2023)
by: Ozawa, Misaki, et al.
Published: (2023)
Yielding and plasticity in amorphous solids
by: Berthier, Ludovic, et al.
Published: (2024)
by: Berthier, Ludovic, et al.
Published: (2024)
Functional bottlenecks can emerge from non-epistatic underlying traits
by: Schulte, Anna Ottavia, et al.
Published: (2025)
by: Schulte, Anna Ottavia, et al.
Published: (2025)
Multiple scattering theory in one dimensional space and time dependent disorder: Average field
by: Selvestrel, Alexandre, et al.
Published: (2024)
by: Selvestrel, Alexandre, et al.
Published: (2024)
Extreme Quantum Cognition Machines for Deliberative Decision Making
by: Romeo, Francesco, et al.
Published: (2026)
by: Romeo, Francesco, et al.
Published: (2026)
Predicting the Mpemba Effect Using Machine Learning
by: Amorim, Felipe, et al.
Published: (2022)
by: Amorim, Felipe, et al.
Published: (2022)
Scalar field Restricted Boltzmann Machine as an ultraviolet regulator
by: Aarts, Gert, et al.
Published: (2023)
by: Aarts, Gert, et al.
Published: (2023)
Analysis of the Hopfield Model Incorporating the Effects of Unlearning
by: Takeuchi, Shuta, et al.
Published: (2026)
by: Takeuchi, Shuta, et al.
Published: (2026)
Machine learning of phases and structures for model systems in physics
by: Bayo, Djenabou, et al.
Published: (2024)
by: Bayo, Djenabou, et al.
Published: (2024)
Universal Spin Models are Universal Approximators in Machine Learning
by: Reinhart, Tobias, et al.
Published: (2025)
by: Reinhart, Tobias, et al.
Published: (2025)
Similar Items
-
Task learning through stimulation-induced plasticity in neural networks
by: Borra, Francesco, et al.
Published: (2024) -
Replica Theory of Spherical Boltzmann Machine Ensembles
by: Tulinski, Thomas, et al.
Published: (2026) -
Restoring balance: principled under/oversampling of data for optimal classification
by: Loffredo, Emanuele, et al.
Published: (2024) -
Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity
by: Kühn, Tobias, et al.
Published: (2023) -
Learning and Unlearning: Bridging classification, memory and generative modeling in Recurrent Neural Networks
by: Ventura, Enrico
Published: (2024)