Does the Performance of Text-to-Image Retrieval Models Generalize Beyond Captions-as-a-Query?

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.

Descriptive Image Captioning +2

Rotation Invariant Quantization for Model Compression

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.

Model Compression Quantization

Demonstration Informed Specification Search

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.

Learning Branching Heuristics for Propositional Model Counting

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.

Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL)

Learning Heuristics for Automated Reasoning through 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.

reinforcement-learning Reinforcement Learning (RL)

Learning Heuristics for Quantified Boolean Formulas 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.

reinforcement-learning Reinforcement Learning (RL)