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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 3076–3100 of 9051 papers

TitleStatusHype
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation GenerationCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency ParsingCode0
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High DimensionsCode0
Automatic Fused Multimodal Deep Learning for Plant IdentificationCode0
Reinforcement Learning for Topic ModelsCode0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
Relation-Rich Visual Document Generator for Visual Information ExtractionCode0
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of MetricsCode0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programmingCode0
Ensemble of Counterfactual ExplainersCode0
Ensemble Pruning based on Objection Maximization with a General Distributed FrameworkCode0
Ensemble Distribution DistillationCode0
Class Incremental Learning with Multi-Teacher DistillationCode0
Replica Tree-based Federated Learning using Limited DataCode0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Ensembles of Locally Independent Prediction ModelsCode0
AmCLR: Unified Augmented Learning for Cross-Modal RepresentationsCode0
Re-purposing Heterogeneous Generative Ensembles with Evolutionary ComputationCode0
Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithmsCode0
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational VideosCode0
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