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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 5801–5825 of 9051 papers

TitleStatusHype
Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Toward Robust Long Range Policy Transfer—0
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes—0
Towards a computational model of grammaticalization and lexical diversity—0
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving—0
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go—0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—0
Towards Algorithmic Transparency: A Diversity Perspective—0
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection—0
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model—0
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble—0
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics—0
Towards a Similarity-adjusted Surprisal Theory—0
Towards assessing agricultural land suitability with causal machine learning—0
Towards a Universal Features Set for IoT Botnet Attacks Detection—0
Towards Automatic Construction of Diverse, High-quality Image Dataset—0
Towards Automatic Gesture Stroke Detection—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Towards Bridging the Digital Language Divide—0
Towards building a Robust Industry-scale Question Answering System—0
Towards complete representation of bacterial contents in metagenomic samples—0
Towards Comprehensive Preference Data Collection for Reward Modeling—0
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data—0
Towards Confidence-aware Calibrated Recommendation—0
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