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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 1176–1200 of 9051 papers

TitleStatusHype
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
DreamDA: Generative Data Augmentation with Diffusion ModelsCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Contrastive Syn-to-Real GeneralizationCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
BLEU might be Guilty but References are not InnocentCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
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