1 code implementation • European Conference on Information Retrieval 2024 • Juan Manuel Rodriguez, Nima Tavassoli, Eliezer Levy, Gil Lederman, Dima Sivov, Matteo Lissandrini, Davide Mottin
ConQA comprises 30 descriptive and 50 conceptual queries on 43k images with more than 100 manually annotated images per query.
Ranked #1 on
Image Retrieval
on ConQA Conceptual
1 code implementation • 3 Mar 2023 • Joseph Kampeas, Yury Nahshan, Hanoch Kremer, Gil Lederman, Shira Zaloshinski, Zheng Li, Emir Haleva
Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources.
1 code implementation • 20 Dec 2021 • Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman, Sanjit A. Seshia
This paper considers the problem of learning temporal task specifications, e. g. automata and temporal logic, from expert demonstrations.
no code implementations • 7 Jul 2020 • Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger Grosse, Sanjit A. Seshia, Fahiem Bacchus
In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model.
no code implementations • ICLR 2020 • Gil Lederman, Markus Rabe, Sanjit Seshia, Edward A. Lee
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
no code implementations • ICLR 2019 • Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia
We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning.
1 code implementation • 20 Jul 2018 • Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.