No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Refael, Yehonatan, Smorodinsky, Guy, Lindenbaum, Ofir, Safran, Itay |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
by: Smorodinsky, Guy, et al.
Published: (2026)
by: Smorodinsky, Guy, et al.
Published: (2026)
Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024)
by: Smorodinsky, Guy, et al.
Published: (2024)
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
by: Refael, Yehonathan, et al.
Published: (2025)
by: Refael, Yehonathan, et al.
Published: (2025)
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
by: Arbel, Iftach, et al.
Published: (2024)
by: Arbel, Iftach, et al.
Published: (2024)
Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
by: Naor, Erel, et al.
Published: (2025)
by: Naor, Erel, et al.
Published: (2025)
Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
by: Tsayag, Itamar, et al.
Published: (2026)
by: Tsayag, Itamar, et al.
Published: (2026)
The Median is Easier than it Looks: Approximation with a Constant-Depth, Linear-Width ReLU Network
by: Dutta, Abhigyan, et al.
Published: (2026)
by: Dutta, Abhigyan, et al.
Published: (2026)
Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity
by: Svirsky, Jonathan, et al.
Published: (2026)
by: Svirsky, Jonathan, et al.
Published: (2026)
Gradient Free Deep Reinforcement Learning With TabPFN
by: Schiff, David, et al.
Published: (2025)
by: Schiff, David, et al.
Published: (2025)
FineGates: LLMs Finetuning with Compression using Stochastic Gates
by: Svirsky, Jonathan, et al.
Published: (2024)
by: Svirsky, Jonathan, et al.
Published: (2024)
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
by: Refael, Yehonathan, et al.
Published: (2025)
by: Refael, Yehonathan, et al.
Published: (2025)
TempoControl: Temporal Attention Guidance for Text-to-Video Models
by: Schiber, Shira, et al.
Published: (2025)
by: Schiber, Shira, et al.
Published: (2025)
Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage
by: Chen, Xinping, et al.
Published: (2025)
by: Chen, Xinping, et al.
Published: (2025)
A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory
by: Safran, Itay
Published: (2026)
by: Safran, Itay
Published: (2026)
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
by: Refael, Yehonathan, et al.
Published: (2024)
by: Refael, Yehonathan, et al.
Published: (2024)
Unveiling Multiple Descents in Unsupervised Autoencoders
by: Rahimi, Kobi, et al.
Published: (2024)
by: Rahimi, Kobi, et al.
Published: (2024)
Anomaly Detection with Variance Stabilized Density Estimation
by: Rozner, Amit, et al.
Published: (2023)
by: Rozner, Amit, et al.
Published: (2023)
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
by: Bar-Shalom, Guy, et al.
Published: (2026)
by: Bar-Shalom, Guy, et al.
Published: (2026)
Revisiting LARS for Large Batch Training Generalization of Neural Networks
by: Do, Khoi, et al.
Published: (2023)
by: Do, Khoi, et al.
Published: (2023)
Representation-Driven Reinforcement Learning
by: Nabati, Ofir, et al.
Published: (2023)
by: Nabati, Ofir, et al.
Published: (2023)
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics
by: Avidan, Yehonatan, et al.
Published: (2023)
by: Avidan, Yehonatan, et al.
Published: (2023)
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy
by: Safran, Itay, et al.
Published: (2024)
by: Safran, Itay, et al.
Published: (2024)
Distilling Symbolic Priors for Concept Learning into Neural Networks
by: Marinescu, Ioana, et al.
Published: (2024)
by: Marinescu, Ioana, et al.
Published: (2024)
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
by: He, Mingguo, et al.
Published: (2022)
by: He, Mingguo, et al.
Published: (2022)
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
by: Chmiel, Brian, et al.
Published: (2022)
by: Chmiel, Brian, et al.
Published: (2022)
Gradient Inversion Attack on Graph Neural Networks
by: Sinha, Divya Anand, et al.
Published: (2024)
by: Sinha, Divya Anand, et al.
Published: (2024)
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
by: Schnaus, Dominik, et al.
Published: (2023)
by: Schnaus, Dominik, et al.
Published: (2023)
Interpretable Deep Clustering for Tabular Data
by: Svirsky, Jonathan, et al.
Published: (2023)
by: Svirsky, Jonathan, et al.
Published: (2023)
Learning Permutation from Structure Without Supervision
by: Eisenberg, Ran, et al.
Published: (2026)
by: Eisenberg, Ran, et al.
Published: (2026)
Modular Continual Learning via Zero-Leakage Reconstruction Routing and Autonomous Task Discovery
by: Kermiche, Noureddine
Published: (2026)
by: Kermiche, Noureddine
Published: (2026)
Unsupervised Discovery of Formulas for Mathematical Constants
by: Shalyt, Michael, et al.
Published: (2024)
by: Shalyt, Michael, et al.
Published: (2024)
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)
by: Lee, Dongwoo, et al.
Published: (2025)
Time Matters: Scaling Laws for Any Budget
by: Inbar, Itay, et al.
Published: (2024)
by: Inbar, Itay, et al.
Published: (2024)
FAME: Formal Abstract Minimal Explanation for Neural Networks
by: Boumazouza, Ryma, et al.
Published: (2026)
by: Boumazouza, Ryma, et al.
Published: (2026)
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
by: Udupi, Himanshu, et al.
Published: (2026)
by: Udupi, Himanshu, et al.
Published: (2026)
Z-Error Loss for Training Neural Networks
by: Godin, Guillaume
Published: (2025)
by: Godin, Guillaume
Published: (2025)
Energy Consumption in Parallel Neural Network Training
by: Huber, Philipp, et al.
Published: (2025)
by: Huber, Philipp, et al.
Published: (2025)
Training Neural Networks for Modularity aids Interpretability
by: Golechha, Satvik, et al.
Published: (2024)
by: Golechha, Satvik, et al.
Published: (2024)
Automatic Stability and Recovery for Neural Network Training
by: Or, Barak
Published: (2026)
by: Or, Barak
Published: (2026)
Gradient-Free Training of Quantized Neural Networks
by: Cohen, Noa, et al.
Published: (2024)
by: Cohen, Noa, et al.
Published: (2024)
Similar Items
-
On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
by: Smorodinsky, Guy, et al.
Published: (2026) -
Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024) -
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
by: Refael, Yehonathan, et al.
Published: (2025) -
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
by: Arbel, Iftach, et al.
Published: (2024) -
Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
by: Naor, Erel, et al.
Published: (2025)