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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 2926–2950 of 9051 papers

TitleStatusHype
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain AdaptationCode0
Investigating the Impact of Text Summarization on Topic Modeling—0
Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets—0
WHOMP: Optimizing Randomized Controlled Trials via Wasserstein HomogeneityCode0
On the Power of Decision Trees in Auto-Regressive Language Modeling—0
Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications—0
Challenges of Generating Structurally Diverse GraphsCode0
Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity—0
Diverse Code Query Learning for Speech-Driven Facial Animation—0
Reducing Diversity to Generate Hierarchical Archetypes—0
GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation—0
Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features—0
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation—0
Open-World Evaluation for Retrieving Diverse Perspectives—0
Text Image Generation for Low-Resource Languages with Dual Translation Learning—0
ID^3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition—0
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation—0
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight AdjustmentCode0
The Impact of Designated Market Makers on Market Liquidity and Competition: A Simulation Approach—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
A Character-Centric Creative Story Generation via Imagination—0
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models—0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling—0
Automated test generation to evaluate tool-augmented LLMs as conversational AI agents—0
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