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Found 5 papers, 2 papers with code
Modeling Text with Decision Forests using Categorical-Set Splits
We define a condition that is specific to categorical-set features -- defined as an unordered set of categorical variables -- and present an algorithm to learn it, thereby equipping decision forests with the ability to directly model text, albeit without preserving sequential order.
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data.
An Alternative Cross Entropy Loss for Learning-to-Rank
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval.
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.
