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Journal of Economic Theory, 2014 The importance of categorical reasoning in human cognition is well-established in psychology and cognitive science, and it is generally acknowledged that one of the most important functions of categorization is to facilitate prediction.
Mohlin, Erik
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Forum of Mathematics, Sigma, 2020 We introduce a notion of complexity of diagrams (and, in particular, of objects and morphisms) in an arbitrary category, as well as a notion of complexity of functors between categories equipped with complexity functions. We discuss several examples of this new definition in categories of wide common interest such as finite sets, Boolean functions ...
SAUGATA BASU, UMUT ISIK
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Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [PDF]
IEEE International Conference on Computer Vision, 2021 Attention mechanism has demonstrated great potential in fine-grained visual recognition tasks. In this paper, we present a counterfactual attention learning method to learn more effective attention based on causal inference.
Yongming Rao +3 more
semanticscholar +1 more source
Machine learning in automated text categorization [PDF]
CSUR, 2001 The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them.
F. Sebastiani
semanticscholar +1 more source
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales [PDF]
Annual Meeting of the Association for Computational Linguistics, 2005 We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one ...
B. Pang, Lillian Lee
semanticscholar +1 more source
Channel Interaction Networks for Fine-Grained Image Categorization [PDF]
AAAI Conference on Artificial Intelligence, 2020 Fine-grained image categorization is challenging due to the subtle inter-class differences. We posit that exploiting the rich relationships between channels can help capture such differences since different channels correspond to different semantics.
Yu Gao +4 more
semanticscholar +1 more source
Proceedings of the American Mathematical Society, 1972 The main purpose of this paper is to describe some properties of categorical semigroups, commutative semigroups which are categorical at zero, and determine the structure of commutative categorical semigroups. We also investigate whether Petrich’s tree condition, for categorical semigroups which are completely semisimple inverse semigroups, is ...
McMorris, F. R., Satyanarayana, M.
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Collaborative Categorization on the Web [PDF]
, 2003 Collaborative categorization is an emerging direction for research and innovative applications. Arguably, collaborative categorization on the Web is an especially promising emerging form of collaborative Web systems because of both, the widespread use
Bry, François, Wagner, Holger
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Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (FGVC) tasks (e.g., recognizing bird species or car make & model).
Yin Cui +4 more
semanticscholar +1 more source
A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model [PDF]
, 2010 According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation.
Baker +43 more
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