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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 5501–5550 of 9051 papers

TitleStatusHype
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations—0
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection—0
Large-Scale Auto-Regressive Modeling Of Street NetworksCode0
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game—0
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and MethodCode0
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment AnalysisCode0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles—0
Chosen methods of improving small object recognition with weak recognizable features—0
Understanding Diversity in Session-Based RecommendationCode0
An Energy Activity Dataset for Smart Homes—0
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
Tensor Decomposition based Personalized Federated Learning—0
A Diversity-Aware Domain Development Methodology—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
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives—0
A Block-Based Adaptive Decoupling Framework for Graph Neural NetworksCode0
Fundamentals of Task-Agnostic Data Valuation—0
Multimedia Generative Script Learning for Task PlanningCode0
PEER: A Collaborative Language Model—0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Tunable Hybrid Proposal Networks for the Open World—0
Unsupervised Question Answering via Answer DiversifyingCode0
Decentralized Collaborative Learning with Probabilistic Data Protection—0
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques—0
Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems—0
An Entropy-based Measure of Intelligence Degree of System Structures—0
Towards Confidence-aware Calibrated Recommendation—0
Do diverse and inclusive workplaces benefit investors? An Empirical Analysis on Europe and the United States—0
Automated Pruning of Polyculture Plants—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Text to Image Generation: Leaving no Language Behind—0
Diverse Video Captioning by Adaptive Spatio-temporal AttentionCode0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing based MIMO Radars—0
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork—0
Road detection via a dual-task network based on cross-layer graph fusion modules—0
Quality Diversity Evolutionary Learning of Decision Trees—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
A User-Centered Investigation of Personal Music Tours—0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning—0
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery—0
Reducing Exploitability with Population Based TrainingCode0
Adaptive Learning Rates for Faster Stochastic Gradient Methods—0
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