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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

Showing 16011625 of 9051 papers

TitleStatusHype
Is It Navajo? Accurate Language Detection in Endangered Athabaskan LanguagesCode0
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion ModelsCode0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMsCode0
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
Is Limited Participant Diversity Impeding EEG-based Machine Learning?Code0
Investigating Metric Diversity for Evaluating Long Document SummarisationCode0
Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text DetectionCode0
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple ReferencesCode0
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across LanguagesCode0
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMsCode0
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech EnhancementCode0
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?Code0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature DimensionsCode0
Interactive Image Segmentation With Latent DiversityCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
A Unified Theory of Diversity in Ensemble LearningCode0
Intentional Computational Level DesignCode0
Interactive Neural Style Transfer with ArtistsCode0
Intrinsically-Motivated Humans and Agents in Open-World ExplorationCode0
A Unified Substrate for Body-Brain Co-evolutionCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Adaptation of olfactory receptor abundances for efficient codingCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
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