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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 2026–2050 of 9051 papers

TitleStatusHype
Illumination Spectrum Estimation for Multispectral Images via Surface Reflectance Modeling and Spatial-Spectral Feature GenerationCode0
IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and EnglishCode0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
Multi-trait User Simulation with Adaptive Decoding for Conversational Task AssistantsCode0
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian LanguagesCode0
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention MechanismCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic ServicesCode0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion ModelsCode0
Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and PretrainingCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific DocumentsCode0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
Negative Training for Neural Dialogue Response GenerationCode0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and InclusionCode0
Neural Data Augmentation via Example ExtrapolationCode0
Image Captioning via Dynamic Path CustomizationCode0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive RegularizationCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
AGAIN: Adversarial Training With Attribution Span Enlargement and Hybrid Feature FusionCode0
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