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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3876–3900 of 9051 papers

TitleStatusHype
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Generating Language Corrections for Teaching Physical Control TasksCode0
Towards Diverse and Effective Question-Answer Pair Generation from Children StorybooksCode1
Contribution à l'Optimisation d'un Comportement Collectif pour un Groupe de Robots Autonomes—0
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models—0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning—0
Towards Understanding the Interplay of Generative Artificial Intelligence and the InternetCode0
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Enhancing Robustness of AI Offensive Code Generators via Data AugmentationCode0
Stochastic Multi-Person 3D Motion ForecastingCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsCode1
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelsCode2
SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs—0
Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity—0
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction ServicesCode2
Phoenix: A Federated Generative Diffusion Model—0
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions—0
GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning—0
Phylogeny-informed fitness estimation—0
GaitGCI: Generative Counterfactual Intervention for Gait Recognition—0
Convergence and Diversity in the Control Hierarchy—0
A generative framework for conversational laughter: Its 'language model' and laughter sound synthesis—0
Show:102550
← PrevPage 156 of 363Next →

No leaderboard results yet.