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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

Showing 551575 of 9051 papers

TitleStatusHype
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Diverse and Admissible Trajectory Prediction through Multimodal Context UnderstandingCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense KnowledgeCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
DISCO: Distilling Counterfactuals with Large Language ModelsCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
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