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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 3101–3125 of 9051 papers

TitleStatusHype
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation GenerationCode0
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency ParsingCode0
Ethical Considerations for Responsible Data CurationCode0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Evaluator for Emotionally Consistent ChatbotsCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering SystemsCode0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
AmCLR: Unified Augmented Learning for Cross-Modal RepresentationsCode0
Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithmsCode0
Rethinking Robustness of Model AttributionsCode0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
End-to-end Adversarial Learning for Generative Conversational AgentsCode0
Ensemble Pruning based on Objection Maximization with a General Distributed FrameworkCode0
Ensembles of Locally Independent Prediction ModelsCode0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Ensemble Distribution DistillationCode0
Ensemble of Counterfactual ExplainersCode0
A hybrid ensemble method with negative correlation learning for regressionCode0
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational VideosCode0
Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired ConstraintsCode0
Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and DivergenceCode0
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented GuesserCode0
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