96 citations · 99 across the 11 of their papers we have counts for
12 papers
Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu +2
The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show promise in mitigating this issue…
Provably Explaining Neural Additive Models
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +4
Despite significant progress in post-hoc explanation methods for neural networks, many remain heuristic and lack provable guarantees. A key approach for obtaining explanations with…
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
Yizhak Y. Elboher, Reuven Peleg, Zhouxing Shi +2
Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation…
Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits
Yizhak Yisrael Elboher, Avraham Raviv, Amihay Elboher +4
Ensuring the safety and efficiency of AI systems is a central goal of modern research. Formal verification provides guarantees of neural network robustness, while early exits impro…
Abstraction-Based Proof Production in Formal Verification of Neural Networks
Yizhak Yisrael Elboher, Omri Isac, Guy Katz +2
Modern verification tools for deep neural networks (DNNs) increasingly rely on abstraction to scale to realistic architectures. In parallel, proof production is becoming a critical…
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +2
Despite significant advancements in post-hoc explainability techniques for neural networks, many current methods rely on heuristics and do not provide formally provable guarantees…
Towards Formal Verification of Deep Neural Networks for Object Detection
Yizhak Y. Elboher, Avraham Raviv, Yael Leibovich Weiss +8
Deep neural networks (DNNs) are widely used in real-world computer vision applications, yet they remain vulnerable to errors and adversarial attacks. Formal verification offers a s…
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
Yizhak Elboher, Raya Elsaleh, Omri Isac +6
As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot work…
Efficiently Finding Adversarial Examples with DNN Preprocessing
Avriti Chauhan, Mohammad Afzal, Hrishikesh Karmarkar +3
Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for machines to carry out. In doing so, they do a lot of decisi…
Tighter Abstract Queries in Neural Network Verification
Elazar Cohen, Yizhak Yisrael Elboher, Clark Barrett +1
Neural networks have become critical components of reactive systems in various domains within computer science. Despite their excellent performance, using neural networks entails n…
Neural Network Verification using Residual Reasoning
Yizhak Yisrael Elboher, Elazar Cohen, Guy Katz
With the increasing integration of neural networks as components in mission-critical systems, there is an increasing need to ensure that they satisfy various safety and liveness re…
An Abstraction-Based Framework for Neural Network Verification
Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
Deep neural networks are increasingly being used as controllers for safety-critical systems. Because neural networks are opaque, certifying their correctness is a significant chall…