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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 4701–4725 of 9051 papers

TitleStatusHype
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks—0
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
Optimizing Hierarchical Image VAEs for Sample QualityCode1
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text GenerationCode1
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
Measures of Information Reflect Memorization Patterns—0
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent DiscoveryCode0
Learning Diversified Feature Representations for Facial Expression Recognition in the WildCode0
Packed-Ensembles for Efficient Uncertainty Estimation—0
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains—0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images—0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational ModelCode1
Style Transfer as Data Augmentation: A Case Study on Named Entity RecognitionCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories—0
Query Expansion Using Contextual Clue Sampling with Language Models—0
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization—0
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
What Makes Graph Neural Networks Miscalibrated?Code1
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