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Found 11 papers, 3 papers with code
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.
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.
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, Go, and Google Sheets (under the name Simple ML for Sheets).
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.
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.
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.
An Analysis of Fusion Functions for Hybrid Retrieval
In particular, we examine fusion by a convex combination (CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion (RRF) method, and identify their advantages and potential pitfalls.
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages.
Efficient and Effective Tree-based and Neural Learning to Rank
We believe that by understanding the fundamentals underpinning these algorithmic and data structure solutions for containing the contentious relationship between efficiency and effectiveness, one can better identify future directions and more efficiently determine the merits of ideas.
Bridging Dense and Sparse Maximum Inner Product Search
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval.
Foundations of Vector Retrieval
Vectors are universal mathematical objects that can represent text, images, speech, or a mix of these data modalities.
