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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 2601–2625 of 9051 papers

TitleStatusHype
Boosting LLM via Learning from Data Iteratively and SelectivelyCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
Analyzing Uncertainty in Neural Machine TranslationCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine ModelCode0
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target ImputationCode0
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple ExpertsCode0
Frequency Tracking Features for Data-Efficient Deep Siren IdentificationCode0
Diverse Video Captioning by Adaptive Spatio-temporal AttentionCode0
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple ReferencesCode0
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field SamplingCode0
Analyzing the Habitable Zones of Circumbinary Planets Using Machine LearningCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Flow-Grounded Spatial-Temporal Video Prediction from Still ImagesCode0
Determinantal Point Process as an alternative to NMSCode0
Analyzing the Dialect Diversity in Multi-document SummariesCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Diversified Arbitrary Style Transfer via Deep Feature PerturbationCode0
Flexible Modeling of Diversity with Strongly Log-Concave DistributionsCode0
Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive VehiclesCode0
Detecting Visual Relationships with Deep Relational NetworksCode0
First the worst: Finding better gender translations during beam searchCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
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