Copertina

Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Boetius, David, Leue, Stefan, Sutter, Tobias
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
di: Boetius, David, et al.
Pubblicazione: (2026)

BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks
di: Wang, Fangji, et al.
Pubblicazione: (2025)

Neural Network Verification with Branch-and-Bound for General Nonlinearities
di: Shi, Zhouxing, et al.
Pubblicazione: (2024)

E-Globe: Scalable $ε$-Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branching
di: Li, Wenting, et al.
Pubblicazione: (2026)

On the Probabilistic Learnability of Compact Neural Network Preimage Bounds
di: Marzari, Luca, et al.
Pubblicazione: (2025)

SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks
di: Sivaramakrishnan, Vignesh, et al.
Pubblicazione: (2024)

Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification
di: Marzari, Luca, et al.
Pubblicazione: (2025)

Exact Verification of Graph Neural Networks with Incremental Constraint Solving
di: Liu, Minghao, et al.
Pubblicazione: (2025)

Learning to Solve Resource-Constrained Project Scheduling Problems with Duration Uncertainty using Graph Neural Networks
di: Infantes, Guillaume, et al.
Pubblicazione: (2025)

Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics
di: Boetius, David, et al.
Pubblicazione: (2024)

Neural Network Verification with PyRAT
di: Lemesle, Augustin, et al.
Pubblicazione: (2024)

When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
di: Singhi, Nishad, et al.
Pubblicazione: (2025)

Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
di: Schlegel, Udo, et al.
Pubblicazione: (2024)

NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
di: Wang, Wenxi, et al.
Pubblicazione: (2021)

COPYCOP: Ownership Verification for Graph Neural Networks
di: Nandakumar, Rahul, et al.
Pubblicazione: (2026)

CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
di: Anand, Akhil S, et al.
Pubblicazione: (2025)

Policy-Guided Search on Tree-of-Thoughts for Efficient Problem Solving with Bounded Language Model Queries
di: Pendurkar, Sumedh, et al.
Pubblicazione: (2026)

Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification
di: Sayed, Abdelrahman Sayed, et al.
Pubblicazione: (2025)

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
di: Požgaj, Stjepan, et al.
Pubblicazione: (2025)

Probabilistic ML Verification via Weighted Model Integration
di: Morettin, Paolo, et al.
Pubblicazione: (2024)

Adversarial Generative Flow Network for Solving Vehicle Routing Problems
di: Zhang, Ni, et al.
Pubblicazione: (2025)

Neural Network Approximators for Marginal MAP in Probabilistic Circuits
di: Arya, Shivvrat, et al.
Pubblicazione: (2024)

The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
di: Kaulen, Konstantin, et al.
Pubblicazione: (2025)

What Planning Problems Can A Relational Neural Network Solve?
di: Mao, Jiayuan, et al.
Pubblicazione: (2023)

Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
di: Ladner, Tobias, et al.
Pubblicazione: (2024)

Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control
di: Shi, Zhouxing, et al.
Pubblicazione: (2024)

DeepCDCL: An CDCL-based Neural Network Verification Framework
di: Liu, Zongxin, et al.
Pubblicazione: (2024)

The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
di: Papamichail, Merkouris, et al.
Pubblicazione: (2026)

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
di: Lee, Su Hyeong, et al.
Pubblicazione: (2025)

Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms
di: Zenkner, Janis, et al.
Pubblicazione: (2025)

Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
di: Liu, Zongxin, et al.
Pubblicazione: (2024)

Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits
di: Elboher, Yizhak Yisrael, et al.
Pubblicazione: (2025)

Distributionally Robust Statistical Verification with Imprecise Neural Networks
di: Dutta, Souradeep, et al.
Pubblicazione: (2023)

Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
di: Marzari, Luca, et al.
Pubblicazione: (2023)

Training Safe Neural Networks with Global SDP Bounds
di: Soletskyi, Roman, et al.
Pubblicazione: (2024)

Probabilistic Runtime Verification, Evaluation and Risk Assessment of Visual Deep Learning Systems
di: Torpmann-Hagen, Birk, et al.
Pubblicazione: (2025)

Causal Neural Probabilistic Circuits
di: Chen, Weixin, et al.
Pubblicazione: (2026)

Neural Approaches to SAT Solving: Design Choices and Interpretability
di: Mojžíšek, David, et al.
Pubblicazione: (2025)

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
di: Pourkamali-Anaraki, Farhad, et al.
Pubblicazione: (2024)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
di: Wang, Xiaoqi, et al.
Pubblicazione: (2022)