Tootfinder

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 67963 nodes and 3710420 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://…

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 402660 nodes and 69679427 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

bag_of_words: Bag of words (2008)
Five text collections in the form of bags-of-words, i.e. a bipartite document–word network. Left nodes are documents and right nodes are words. Edge weights are multiplicities. .
This network has 10336 nodes and 353160 edges.
Tags: Informational, Text, Bipartite, Weighted, Metadata
https://

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Bijay Gurung, David T. Hoffmann, Thomas Brox
https://arxiv.org/abs/2507.07985

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Contrastive vision-language models like CLIP are used for a large variety of applications, such as zero-shot classification or as vision encoder for multi-modal models. Despite their popularity, their representations show major limitations. For instance, CLIP models learn bag-of-words representations and, as a consequence, fail to distinguish whether an image is of "a yellow submarine and a blue bus" or "a blue submarine and a yellow bus". Previous attempts to fix this issue added hard negative…