SOTAVerified

Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 753 papers

TitleStatusHype
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation—0
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
Learning to Rank Personalized Search Results in Professional Networks—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning to Rank Proposals for Object Detection—0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation—0
Learning To Rank Resources with GNN—0
Learning to Rank Retargeted Images—0
Learning to Rank Salient Content for Query-focused Summarization—0
Learning to Rank Scientific Documents from the Crowd—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
Learning to Rank under Multinomial Logit Choice—0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
Learning to Rank Visual Stories From Human Ranking Data—0
Learning to Rank when Grades Matter—0
Learning-to-Rank with BERT in TF-Ranking—0
Extended Missing Data Imputation via GANs for Ranking Applications—0
Learning-to-Rank with Nested Feedback—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Learning to Rank with Small Set of Ground Truth Data—0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Learning to Select: Problem, Solution, and Applications—0
Learning to Temporally Order Medical Events in Clinical Text—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning Visual Features from Snapshots for Web Search—0
Learning what matters - Sampling interesting patterns—0
Leveraging semantically similar queries for ranking via combining representations—0
Leveraging User Behavior History for Personalized Email Search—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
ListBERT: Learning to Rank E-commerce products with Listwise BERT—0
Listwise Learning to Rank with Deep Q-Networks—0
Live Detection of Face Using Machine Learning with Multi-feature Method—0
Local Descriptors Optimized for Average Precision—0
Long Context Modeling with Ranked Memory-Augmented Retrieval—0
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling—0
Machine Comprehension Based on Learning to Rank—0
Making Better Use of Edges via Perceptual Grouping—0
MarlRank: Multi-agent Reinforced Learning to Rank—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Metalearners for Ranking Treatment Effects—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Metric-agnostic Ranking Optimization—0
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks—0
MidRank: Learning to rank based on subsequences—0
Minimax Regret for Cascading Bandits—0
Misspecified Linear Bandits—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Show:102550
← PrevPage 11 of 16Next →

No leaderboard results yet.