Added NeurReps25 Paper info

Author: ab-sayed

_bibliography/papers.bib

@@ -4,6 +4,24 @@
 @string{aps = {American Physical Society,}}
 
 
+@inproceedings{sayed2025mixedmonotonicityreachabilityanalysis,
+  bibtex_show={true},
+  title={Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency}, 
+  author={Abdelrahman Sayed Sayed and Pierre-Jean Meyer and Mohamed Ghazel},
+  abstract={Neural ordinary differential equations (neural ODE) are powerful continuous-time machine learning models for depicting the behavior of complex dynamical systems, but their verification remains challenging due to limited reachability analysis tools adapted to them. We propose a novel interval-based reachability method that leverages continuous-time mixed monotonicity techniques for dynamical systems to compute an over-approximation for the neural ODE reachable sets. By exploiting the geometric structure of full initial sets and their boundaries via the homeomorphism property, our approach ensures efficient bound propagation. By embedding neural ODE dynamics into a mixed monotone system, our interval-based reachability approach, implemented in TIRA with single-step, incremental, and boundary-based approaches, provides sound and computationally efficient over-approximations compared with CORA's zonotopes and NNV2.0 star set representations, while trading tightness for efficiency. This trade-off makes our method particularly suited for high-dimensional, real-time, and safety-critical applications. Applying mixed monotonicity to neural ODE reachability analysis paves the way for lightweight formal analysis by leveraging the symmetric structure of monotone embeddings and the geometric simplicity of interval boxes, opening new avenues for scalable verification aligned with the symmetry and geometry of neural representations. This novel approach is illustrated on two numerical examples of a spiral system and a fixed-point attractor system modeled as a neural ODE.},
+  booktitle={NeurIPS Workshop on Symmetry and Geometry in Neural Representations},
+  year={2025},
+  eprint={2510.17859},
+  archivePrefix={arXiv},
+  primaryClass={eess.SY},
+  url={hhttps://openreview.net/forum?id=7tQsz4zosO&referrer=%5Bthe%20profile%20of%20Abdelrahman%20Sayed%20Sayed%5D(%2Fprofile%3Fid%3D~Abdelrahman_Sayed_Sayed1)}, 
+  pdf={https://arxiv.org/abs/2510.17859},
+  code={https://github.com/ab-sayed/Mixed-Monotonicity-Reachability-Analysis-of-neural-ODE},
+  abbr={AI},
+  preview={NeurReps_Framework.png},
+  selected={true}
+}
+
 @inproceedings{sayed2025bridgingneuraloderesnet,
   bibtex_show={true},
   title={Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification}, 
@@ -14,10 +32,11 @@ @inproceedings{sayed2025bridgingneuraloderesnet
   eprint={2506.03227},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
-  url={https://openreview.net/forum?id=8r0dHcrZ2j}, 
-  pdf={https://openreview.net/forum?id=8r0dHcrZ2j},
+  url={https://arxiv.org/abs/2506.03227}, 
+  pdf={https://arxiv.org/abs/2506.03227},
   code={https://github.com/ab-sayed/Formal-Error-Bound-for-Safety-Verification-of-neural-ODE},
   slides={2025_Abdelrahman_Sayed_SAIV25_slides.pdf},
+  poster={2025_Abdelrahman_Sayed_SAIV25_poster.pdf},
   abbr={AI},
   preview={SAIV25.gif},
   selected={true}
@@ -33,7 +52,7 @@ @inproceedings{sayed2023risk
   year={2023},
   organization={IEEE},
   url={https://ieeexplore.ieee.org/abstract/document/10244516},
-  pdf={https://hal.science/hal-04721094/},
+  pdf={https://arxiv.org/abs/2510.19415},
   abbr={Robotics},
   preview={OCEANS23.gif},
   selected={false}

_news/announcement_SAIV25.md

@@ -7,5 +7,5 @@ related_posts: false
 
 <!-- A simple inline announcement. -->
 
-Our paper "[Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification](https://openreview.net/forum?id=8r0dHcrZ2j)" was accepted in [SAIV 2025](https://www.aiverification.org/2025/) co-located with [CAV 2025](https://conferences.i-cav.org/2025/)
+Our paper "[Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification](https://arxiv.org/abs/2506.03227)" was accepted in [SAIV 2025](https://www.aiverification.org/2025/) co-located with [CAV 2025](https://conferences.i-cav.org/2025/)