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ORCIDNeuS 2025: Philadelphia, PA, USA
- George J. Pappas, Pradeep Ravikumar, Sanjit A. Seshia:
International Conference on Neuro-symbolic Systems, 28-30 May 2025, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Proceedings of Machine Learning Research 288, PMLR 2025 - Chuqin Geng, Zhaoyue Wang, Haolin Ye, Xujie Si:
Learning Minimal Neural Specifications. 1-21 - Dylan Goetting, Himanshu Gaurav Singh, Antonio Loquercio:
End-to-End Navigation with Vision-Language Models: Transforming Spatial Reasoning into Question-Answering. 22-35 - Peihao Wang, Zhangyang (Atlas) Wang:
Why Neural Networks Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning. 36-65 - Jordan Peper, Zhenjiang Mao, Yuang Geng, Siyuan Pan, Ivan Ruchkin:
Four Principles for Physically Interpretable World Models. 66-89 - Fabian Kresse, Emily Yu, Christoph H. Lampert, Thomas A. Henzinger:
Logic Gate Neural Networks are Good for Verification. 90-103 - Nicholas Potteiger, Diego Manzanas Lopez, Taylor T. Johnson, Xenofon D. Koutsoukos:
Real-Time Reachability for Neurosymbolic Reinforcement Learning-based Safe Autonomous Navigation. 104-126 - Thomas Waite, Yuang Geng, Trevor Turnquist, Ivan Ruchkin, Radoslav Ivanov:
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems. 127-143 - Peiyang Song, Kaiyu Yang, Anima Anandkumar:
Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean. 144-169 - Samuel Sasaki, Diego Manzanas Lopez, Taylor T. Johnson:
Neurosymbolic Finite and Pushdown Automata: Improved Multimodal Reasoning versus Vision Language Models (VLMs). 170-187 - Jacob K. Christopher, Michael Cardei, Jinhao Liang, Ferdinando Fioretto:
Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation. 188-213 - Shiwen Yu, Wanwei Liu, Zengyu Liu, Liqian Chen, Ting Wang, Naijun Zhan, Ji Wang:
Learning Subject to Constraints via Abstract Gradient Descent. 214-230 - Yu Huang, Ziji Wu, Kexin Ma, Ji Wang:
Differentiable Synthesis of Behavior Tree Architectures and Execution Nodes. 231-259 - Juyong Kim, Chandler Squires, Pradeep Ravikumar:
Knowledge-Enriched Machine Learning for Tabular Data. 260-292 - Paulo Pirozelli, Fábio G. Cozman:
A Study of Modus Ponens in Transformer Models. 293-315 - Kishor Jothimurugan, Suguman Bansal, Osbert Bastani, Rajeev Alur:
Specification-Guided Reinforcement Learning. 316-330 - Johnathan Chi-Ho Leung, Guansen Tong, Parasara Sridhar Duggirala, Praneeth Chakravarthula:
From Road to Code: Neuro-Symbolic Program Synthesis for Autonomous Driving Scene Translation and Analysis. 331-351 - Virginie Debauche, Alec Edwards, Raphaël M. Jungers, Alessandro Abate:
Formal Synthesis of Lyapunov Stability Certificates for Linear Switched Systems using ReLU Neural Networks. 352-364 - Ameesh Shah, Marcell Vazquez-Chanlatte, Sebastian Junges, Sanjit A. Seshia:
Learning Formal Specifications from Membership and Preference Queries. 365-383 - Tergel Molom-Ochir, Naman Saxena, Jiwoo Kim, Yiran Chen, Zhangyang (Atlas) Wang, Miroslav Pajic, Hai (Helen) Li:
Efficient Neuro-Symbolic Policy using In-Memory Computing. 384-395 - Luca Bortolussi, Francesca Cairoli, Julia Klein, Tatjana Petrov:
Neuro-Symbolic Discovery of Markov Population Processes. 396-408 - Serena Serafina Serbinowska, Diego Manzanas Lopez, Dung Thuy Nguyen, Taylor T. Johnson:
Neuro-Symbolic Behavior Trees (NSBTs) and Their Verification. 409-423 - Hanning Chen, Ali Zakeri, Yang Ni, Fei Wen, Behnam Khaleghi, Hugo Latapie, Alvaro Velasquez, Mohsen Imani:
KGAccel: A Domain-Specific Reconfigurable Accelerator for Knowledge Graph Reasoning. 424-445 - Héctor Muñoz-Avila, David W. Aha, Paola Rizzo:
ChatHTN: Interleaving Approximate (LLM) and Symbolic HTN Planning. 446-458 - Zekun Wang, Ethan L. Haarer, Nicki Barari, Christopher J. MacLellan:
Taxonomic Networks: A Representation for Neuro-Symbolic Pairing. 459-471 - Wenliang Liu, Danyang Li, Erfan Aasi, Daniela Rus, Roberto Tron, Calin Belta:
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control. 472-489 - Zishen Wan, Che-Kai Liu, Hanchen Yang, Ritik Raj, Arijit Raychowdhury, Tushar Krishna:
Efficient Processing of Neuro-Symbolic AI: A Tutorial and Cross-Layer Co-Design Case Study. 490-504 - R. Spencer Hallyburton, Miroslav Pajic:
Assured Autonomy with Neuro-Symbolic Perception. 505-523 - Hadi Partovi Aria, Zhe Xu:
Mining Causal Signal Temporal Logic Formulas for Efficient Reinforcement Learning with Temporally Extended Tasks. 524-542 - Marcell Vazquez-Chanlatte, Karim Elmaaroufi, Stefan J. Witwicki, Matei Zaharia, Sanjit A. Seshia:
L*LM: Learning Automata from Demonstrations, Examples, and Natural Language. 543-569 - Elvis Nunez, Luca Zancato, Benjamin Bowman, Aditya Golatkar, Wei Xia, Stefano Soatto:
Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models. 570-596 - Alireza Nadali, Ashutosh Trivedi, Majid Zamani:
Stochastic Neural Simulation Relations for Control Transfer. 597-620 - Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur, Tuhin Sahai, Susmit Jha:
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations. 621-642 - Mahyar Alinejad, Precious Nwaorgu, Chinwendu Enyioha, Yue Wang, Alvaro Velasquez, George K. Atia:
Bidirectional End-to-End Framework for Transfer from Abstract Models in Non-Markovian Reinforcement Learning. 643-660 - Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia:
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning. 661-675 - Sahil Shah, Harsh Goel, Sai Shankar Narasimhan, Minkyu Choi, S. P. Sharan, Oguzhan Akcin, Sandeep Chinchali:
A Challenge to Build Neuro-Symbolic Video Agents. 676-692 - Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh:
PCA-DDReach: Efficient Statistical Reachability Analysis of Stochastic Dynamical Systems via Principal Component Analysis. 693-707 - Alexi Canesse, Zhe Fu, Nathan Lichtlé, Hossein Nick Zinat Matin, Zihe Liu, Maria Laura Delle Monache, Alexandre M. Bayen:
A Tutorial on Neural Network-Based Solvers for Hyperbolic Conservation Laws: Supervised vs. Unsupervised Learning, and Applications to Traffic Modeling. 708-720 - Benjamin Caulfield, Sanjit A. Seshia:
Modularity in Query-Based Concept Learning. 721-744 - Tian Yu Liu, Stefano Soatto, Matteo Marchi, Pratik Chaudhari, Paulo Tabuada:
Observability of Latent States in Generative AI Models. 745-764 - Yunhao Yang, Cyrus Neary, Ufuk Topcu:
Automaton-Based Representations of Task Knowledge from Generative Language Models. 765-783
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