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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 4851–4875 of 9051 papers

TitleStatusHype
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment AnalysisCode0
Understanding Diversity in Session-Based RecommendationCode0
An Energy Activity Dataset for Smart Homes—0
Chosen methods of improving small object recognition with weak recognizable features—0
Grounded Affordance from Exocentric ViewCode1
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
A Diversity-Aware Domain Development Methodology—0
Tensor Decomposition based Personalized Federated Learning—0
MDIA: A Benchmark for Multilingual Dialogue Generation in 46 LanguagesCode0
Nitrogen-induced hysteresis in grassland biodiversity: a theoretical test of litter-mediated mechanisms—0
BITS: Bi-level Imitation for Traffic SimulationCode2
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Multimedia Generative Script Learning for Task PlanningCode0
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives—0
Fundamentals of Task-Agnostic Data Valuation—0
PEER: A Collaborative Language Model—0
GAN-based generative modelling for dermatological applications -- comparative studyCode1
Semi-supervised Semantic Segmentation with Mutual Knowledge DistillationCode1
Decentralized Collaborative Learning with Probabilistic Data Protection—0
Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems—0
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques—0
Unsupervised Question Answering via Answer DiversifyingCode0
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