SOTAVerified

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

Showing 11261150 of 9051 papers

TitleStatusHype
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
Differential Evolution with Reversible Linear TransformationsCode1
Controllable Multi-Interest Framework for RecommendationCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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