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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 7901–7925 of 9051 papers

TitleStatusHype
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text GenerationCode0
A Corpus-free State2Seq User Simulator for Task-oriented DialogueCode0
Pluralistic Image CompletionCode0
A Systematic Review of Reproducibility Research in Natural Language ProcessingCode0
Semantic Summarization of Egocentric Photo Stream EventsCode0
Understanding the Quality-Diversity Trade-off in Diffusion Language ModelsCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
CompoNet: Learning to Generate the Unseen by Part Synthesis and CompositionCode0
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and ComparisonCode0
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI RecommendationCode0
PMT-IQA: Progressive Multi-task Learning for Blind Image Quality AssessmentCode0
Hydra: Preserving Ensemble Diversity for Model DistillationCode0
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Counterfactual Explanations Using Optimization With Constraint LearningCode0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
CoT: Cooperative Training for Generative Modeling of Discrete DataCode0
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Learning to select data for transfer learning with Bayesian OptimizationCode0
Learning to Select Prototypical Parts for Interpretable Sequential Data ModelingCode0
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion ModelsCode0
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