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 8701–8725 of 9051 papers

TitleStatusHype
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
The Structure and Dynamics of Knowledge Graphs, with SuperficialityCode0
CausalDialogue: Modeling Utterance-level Causality in ConversationsCode0
Direct May Not Be the Best: An Incremental Evolution View of Pose GenerationCode0
Fair and Diverse DPP-based Data SummarizationCode0
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning ModelsCode0
Unsupervised Stereo Matching Network For VHR Remote Sensing Images Based On Error PredictionCode0
Facts That MatterCode0
Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket RecommendationCode0
VinaFood21: A Novel Dataset for Evaluating Vietnamese Food RecognitionCode0
An Evaluation Framework for Attributed Information Retrieval using Large Language ModelsCode0
Negative Training for Neural Dialogue Response GenerationCode0
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with DroneCode0
NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning ProblemsCode0
Replica Tree-based Federated Learning using Limited DataCode0
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone ProgrammingCode0
Autoregressive Quantile Networks for Generative ModelingCode0
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic SystemsCode0
CatVRNN: Generating Category Texts via Multi-task LearningCode0
Representation Matters: Assessing the Importance of Subgroup Allocations in Training DataCode0
Streaming Algorithms for Diversity Maximization with Fairness ConstraintsCode0
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model SmoothnessCode0
Direction Concentration Learning: Enhancing Congruency in Machine LearningCode0
Accelerating Prototype-Based Drug Discovery using Conditional Diversity NetworksCode0
Neural-based Modeling for Performance Tuning of Spark Data AnalyticsCode0
Show:102550
← PrevPage 349 of 363Next →

No leaderboard results yet.