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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 751–775 of 9051 papers

TitleStatusHype
ZYN: Zero-Shot Reward Models with Yes-No Questions for RLAIFCode1
Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in RoboticsCode1
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain RemovalCode1
Path Shadowing Monte-CarloCode1
Deep Image Harmonization with Learnable AugmentationCode1
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor ScenesCode1
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent SpaceCode1
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVCode1
Rumor Detection with Diverse Counterfactual EvidenceCode1
Towards Task Sampler Learning for Meta-LearningCode1
Towards Viewpoint-Invariant Visual Recognition via Adversarial TrainingCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image ModelsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Answering Ambiguous Questions via Iterative PromptingCode1
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel SynthesisCode1
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning BenchmarksCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
Generative Data Augmentation for Aspect Sentiment Quad PredictionCode1
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural NetworksCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasCode1
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regressionCode1
Restart Sampling for Improving Generative ProcessesCode1
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-StepCode1
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