Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Barrett, Clark"'
Autor:
Mandal, Udayan, Amir, Guy, Wu, Haoze, Daukantas, Ieva, Newell, Fletcher Lee, Ravaioli, Umberto, Meng, Baoluo, Durling, Michael, Hobbs, Kerianne, Ganai, Milan, Shim, Tobey, Katz, Guy, Barrett, Clark
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and safety-critical
Externí odkaz:
http://arxiv.org/abs/2407.07088
Stable infiniteness, strong finite witnessability, and smoothness are model-theoretic properties relevant to theory combination in satisfiability modulo theories. Theories that are strongly finitely witnessable and smooth are called strongly polite a
Externí odkaz:
http://arxiv.org/abs/2406.18912
Autor:
Mandal, Udayan, Amir, Guy, Wu, Haoze, Daukantas, Ieva, Newell, Fletcher Lee, Ravaioli, Umberto J., Meng, Baoluo, Durling, Michael, Ganai, Milan, Shim, Tobey, Katz, Guy, Barrett, Clark
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating agents that control autonomous systems. However, the "black box" nature of DRL agents limits their deployment in real-world safety-critical applications. A promi
Externí odkaz:
http://arxiv.org/abs/2405.14058
Autor:
Dalrymple, David "davidad", Skalse, Joar, Bengio, Yoshua, Russell, Stuart, Tegmark, Max, Seshia, Sanjit, Omohundro, Steve, Szegedy, Christian, Goldhaber, Ben, Ammann, Nora, Abate, Alessandro, Halpern, Joe, Barrett, Clark, Zhao, Ding, Zhi-Xuan, Tan, Wing, Jeannette, Tenenbaum, Joshua
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper
Externí odkaz:
http://arxiv.org/abs/2405.06624
Autor:
Daly, Ross, Donovick, Caleb, Terrill, Caleb, Melchert, Jackson, Raina, Priyanka, Barrett, Clark, Hanrahan, Pat
Compiling programs to an instruction set architecture (ISA) requires a set of rewrite rules that map patterns consisting of compiler instructions to patterns consisting of ISA instructions. We synthesize such rules by constructing SMT queries, whose
Externí odkaz:
http://arxiv.org/abs/2405.06127
We present a number of first- and second-order extensions to SMT theories specifically aimed at representing and analyzing SQL queries with join, projection, and selection operations. We support reasoning about SQL queries with either bag or set sema
Externí odkaz:
http://arxiv.org/abs/2405.03057
Chain-of-Thought (CoT) reasoning could in principle enable a deeper understanding of a language model's (LM) internal reasoning. However, prior work suggests that LMs can answer questions similarly despite changes in their CoT, suggesting that those
Externí odkaz:
http://arxiv.org/abs/2404.18988
Optimization Modulo Theories (OMT) has emerged as an important extension of the highly successful Satisfiability Modulo Theories (SMT) paradigm. The OMT problem requires solving an SMT problem with the restriction that the solution must be optimal wi
Externí odkaz:
http://arxiv.org/abs/2404.16122
Autor:
Wu, Haoze, Isac, Omri, Zeljić, Aleksandar, Tagomori, Teruhiro, Daggitt, Matthew, Kokke, Wen, Refaeli, Idan, Amir, Guy, Julian, Kyle, Bassan, Shahaf, Huang, Pei, Lahav, Ori, Wu, Min, Zhang, Min, Komendantskaya, Ekaterina, Katz, Guy, Barrett, Clark
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design and highlight the major features and components introduced since its in
Externí odkaz:
http://arxiv.org/abs/2401.14461
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In this work, we propose a framework for formally verifying properties of
Externí odkaz:
http://arxiv.org/abs/2312.12679