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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 4851–4900 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
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Tunable Hybrid Proposal Networks for the Open World—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
An Entropy-based Measure of Intelligence Degree of System Structures—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic DataCode1
Text to Image Generation: Leaving no Language Behind—0
Diverse Video Captioning by Adaptive Spatio-temporal AttentionCode0
Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing based MIMO Radars—0
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVCode1
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social MediaCode1
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Quality Diversity Evolutionary Learning of Decision Trees—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
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual EnvironmentsCode1
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose EstimationCode1
A User-Centered Investigation of Personal Music Tours—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation—0
A Case for Rejection in Low Resource ML DeploymentCode1
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