Výsledky vyhledávání - "Lederman, Gil"

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Rotation Invariant Quantization for Model Compression

Autor: Kampeas, Joseph, Nahshan, Yury, Kremer, Hanoch, Lederman, Gil, Zaloshinski, Shira, Li, Zheng, Haleva, Emir

Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources. In this study, we investigate the rate-distortion tradeoff for NN model compression.

Externí odkaz: http://arxiv.org/abs/2303.03106

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Demonstration Informed Specification Search

Autor: Vazquez-Chanlatte, Marcell, Shah, Ameesh, Lederman, Gil, Seshia, Sanjit A.

This paper considers the problem of learning temporal task specifications, e.g. automata and temporal logic, from expert demonstrations. Task specifications are a class of sparse memory augmented rewards with explicit support for temporal and Boolean

Externí odkaz: http://arxiv.org/abs/2112.10807

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Learning Branching Heuristics for Propositional Model Counting

Autor: Vaezipoor, Pashootan, Lederman, Gil, Wu, Yuhuai, Maddison, Chris J., Grosse, Roger, Seshia, Sanjit A., Bacchus, Fahiem

Publikováno v: 35(14), 2021, 12427-12435

Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, including many discrete probabilistic inference problems, can be translated

Externí odkaz: http://arxiv.org/abs/2007.03204

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Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

Autor: Lederman, Gil, Rabe, Markus N., Lee, Edward A., Seshia, Sanjit A.

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size

Externí odkaz: http://arxiv.org/abs/1807.08058

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Gordian

Autor: Weber, Matthew, Jin, Baihong, Lederman, Gil, Shoukry, Yasser, Lee, Edward A, Seshia, Sanjit, Sangiovanni-Vincentelli, Alberto

Publikováno v: ACM Transactions on Cyber-Physical Systems, vol 4, iss 4

Accurate localization from Cyber-Physical Systems (CPS) is a critical enabling technology for context-aware applications and control. As localization plays an increasingly safety-critical role, location systems must be able to identify and eliminate

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::42156fd55f7e1605f5fc30b467b9e6d5
https://escholarship.org/uc/item/9d4718mw

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