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Found 4 papers, 2 papers with code
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
It is essential to provide recommendations that are both personalized and diverse, rather than solely relying on achieving high rank-based performance, such as Click-through Rate, Recall, etc.
Leveraging Unlabeled Data for Sketch-based Understanding
Sketch-based understanding is a critical component of human cognitive learning and is a primitive communication means between humans.
Semi-discrete optimization through semi-discrete optimal transport: a framework for neural architecture search
With this aim in mind, we discuss the geometric and theoretical motivation for new techniques for neural architecture search (in a companion paper we show that algorithms inspired by our framework are competitive with contemporaneous methods).
Traditional and accelerated gradient descent for neural architecture search
In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [5] which used the geometric structure of optimal transport to introduce the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-discrete space.
