6 citations · 10 across the 5 of their papers we have counts for
5 papers
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
DisCLIP: Open-Vocabulary Referring Expression Generation
Lior Bracha, Eitan Shaar, Aviv Shamsian +2
Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achie…
Guided Deep Kernel Learning
Idan Achituve, Gal Chechik, Ethan Fetaya
Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL). Unfortunately, due to the kernel optimi…
Communication Efficient Distributed Learning over Wireless Channels
Idan Achituve, Wenbo Wang, Ethan Fetaya +1
Vertical distributed learning exploits the local features collected by multiple learning workers to form a better global model. However, the exchange of data between the workers an…
Graph Approximation and Clustering on a Budget
Ethan Fetaya, Ohad Shamir, Shimon Ullman
We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only…