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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 3251–3275 of 9051 papers

TitleStatusHype
Crowdsourcing Multiple Choice Science Questions—0
Crowdsourcing Lexical Diversity—0
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability—0
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework—0
Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?—0
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots—0
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos—0
Augmented Message Passing Stein Variational Gradient Descent—0
Crowded trades, market clustering, and price instability—0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Crowd Access Path Optimization: Diversity Matters—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation—0
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech—0
Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning—0
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Cross-modal Face- and Voice-style Transfer—0
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon—0
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
Adaptability of non-genetic diversity in bacterial chemotaxis—0
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data—0
Cross-Layer Discrete Concept Discovery for Interpreting Language Models—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
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