Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Meirom, Eli"'
Autor:
Lutsker, Guy, Sapir, Gal, Godneva, Anastasia, Shilo, Smadar, Greenfield, Jerry R, Samocha-Bonet, Dorit, Mannor, Shie, Meirom, Eli, Chechik, Gal, Rossman, Hagai, Segal, Eran
Recent advances in self-supervised learning enabled novel medical AI models, known as foundation models (FMs) that offer great potential for characterizing health from diverse biomedical data. Continuous glucose monitoring (CGM) provides rich, tempor
Externí odkaz:
http://arxiv.org/abs/2408.11876
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computational complexity associated with performing message passing on many subg
Externí odkaz:
http://arxiv.org/abs/2310.20082
A major challenge of reinforcement learning (RL) in real-world applications is the variation between environments, tasks or clients. Meta-RL (MRL) addresses this issue by learning a meta-policy that adapts to new tasks. Standard MRL methods optimize
Externí odkaz:
http://arxiv.org/abs/2301.11147
Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit requires computi
Externí odkaz:
http://arxiv.org/abs/2204.09052
Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V&L problems. However, it is unclear how they can be used for reasoning about user-specific visual concepts in unstructured language.
Externí odkaz:
http://arxiv.org/abs/2204.01694
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, 2023
Reasoning and interacting with dynamic environments is a fundamental problem in AI, but it becomes extremely challenging when actions can trigger cascades of cross-dependent events. We introduce a new supervised learning setup called {\em Cascade} wh
Externí odkaz:
http://arxiv.org/abs/2202.01108
Graph neural networks (GNNs) can process graphs of different sizes, but their ability to generalize across sizes, specifically from small to large graphs, is still not well understood. In this paper, we identify an important type of data where genera
Externí odkaz:
http://arxiv.org/abs/2010.08853
We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to
Externí odkaz:
http://arxiv.org/abs/2010.05313
We consider a social choice problem where only a small number of people out of a large population are sufficiently available or motivated to vote. A common solution to increase participation is to allow voters use a proxy, that is, transfer their vot
Externí odkaz:
http://arxiv.org/abs/1611.08308
We establish a network formation game for the Internet's Autonomous System (AS) interconnection topology. The game includes different types of players, accounting for the heterogeneity of ASs in the Internet. In this network formation game, the utili
Externí odkaz:
http://arxiv.org/abs/1604.08179