472 citations · 732 across the 35 of their papers we have counts for
6 papers · 2 filters
Teacher-Student Consistency For Multi-Source Domain Adaptation
Ohad Amosy, Gal Chechik
In Multi-Source Domain Adaptation (MSDA), models are trained on samples from multiple source domains and used for inference on a different, target, domain. Mainstream domain adapta…
From Local Structures to Size Generalization in Graph Neural Networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom +2
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 underst…
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
Eli A. Meirom, Haggai Maron, Shie Mannor +1
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 sched…
Learning the Pareto Front with Hypernetworks
Aviv Navon, Aviv Shamsian, Gal Chechik +1
Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the fron…
From Generalized zero-shot learning to long-tail with class descriptors
Dvir Samuel, Yuval Atzmon, Gal Chechik
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompani…
On Learning Sets of Symmetric Elements
Haggai Maron, Or Litany, Gal Chechik +1
Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are…