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Learning-To-Rank

23 papers with code · Graphs

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Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns

CVPR 2018 ahangchen/TFusion

Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.

LEARNING-TO-RANK TRANSFER LEARNING UNSUPERVISED PERSON RE-IDENTIFICATION

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

17 Feb 2019xialeiliu/RankIQA

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

20 Jun 2017AdeDZY/K-NRM

Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.

AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING LEARNING-TO-RANK WORD EMBEDDINGS

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

CVPR 2018 xialeiliu/CrowdCountingCVPR18

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.

CROWD COUNTING IMAGE RETRIEVAL LEARNING-TO-RANK

Learning Latent Vector Spaces for Product Search

25 Aug 2016cvangysel/SERT

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.

LEARNING-TO-RANK

Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

16 Sep 2018acbull/Unbiased_LambdaMart

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i. e., learning-to-rank.

LEARNING-TO-RANK

Unbiased Learning to Rank with Unbiased Propensity Estimation

16 Apr 2018QingyaoAi/Dual-Learning-Algorithm-for-Unbiased-Learning-to-Rank

We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.

LEARNING-TO-RANK

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

19 Sep 2018graytowne/rank_distill

We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}.

LEARNING-TO-RANK RECOMMENDATION SYSTEMS

Using Titles vs. Full-text as Source for Automated Semantic Document Annotation

15 May 2017Quadflor/quadflor

For the first time, we offer a systematic comparison of classification approaches to investigate how far semantic annotations can be conducted using just the metadata of the documents such as titles published as labels on the Linked Open Data cloud.

DOCUMENT CLASSIFICATION KNOWLEDGE GRAPHS LEARNING-TO-RANK