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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 3801–3825 of 9051 papers

TitleStatusHype
FrameNet and Typology—0
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection—0
Angle diversity receiver as a key enabler for reliable ORIS-based Visible Light Communication—0
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
Free-Space Optical MISO Communications With an Array of Detectors—0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation—0
Gini Coefficient as a Unified Metric for Evaluating Many-versus-Many Similarity in Vector Spaces—0
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study—0
Offline Diversity Maximization Under Imitation Constraints—0
A COLD Approach to Generating Optimal Samples—0
giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI—0
Cost-Effective Cyber-Physical System Prototype for Precision Agriculture with a Focus on Crop Growth—0
Attribute analysis with synthetic dataset for person re-identification—0
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
From Lifestyle Vlogs to Everyday Interactions—0
Diverse Neural Network Learns True Target Functions—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
Boosting Dialog Response Generation—0
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