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Learning-To-Rank

18 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. Finally, we propose a learning-to-rank based mutual promotion procedure to incrementally optimize the classifiers based on the unlabeled data in the target domain.

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

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. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix.

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

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. Our latent vector space model achieves its enhanced performance as it learns better product representations.

LEARNING-TO-RANK

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. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image.

CROWD COUNTING IMAGE RETRIEVAL LEARNING-TO-RANK

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

15 May 2017Quadflor/quadflor

While the widespread access to the document metadata is a tremendous advancement, it is yet not so easy to assign semantic annotations and organize the documents along semantic concepts. 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

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

19 Sep 2018graytowne/rank_distill

We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}.

LEARNING-TO-RANK RECOMMENDATION SYSTEMS

Modeling Label Ambiguity for Neural List-Wise Learning to Rank

24 Jul 2017rjagerman/shoelace

List-wise learning to rank methods are considered to be the state-of-the-art. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity.

LEARNING-TO-RANK

Hashing as Tie-Aware Learning to Rank

CVPR 2018 kunhe/TALR

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG).

IMAGE RETRIEVAL LEARNING-TO-RANK

Off-policy evaluation for slate recommendation

NeurIPS 2017 adith387/slates_semisynth_expts

This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance.

LEARNING-TO-RANK