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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 5226–5250 of 9051 papers

TitleStatusHype
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System—0
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Data standardization for robust lip sync—0
Auxiliary Class Based Multiple Choice Learning—0
Liquid Crystal-Based RIS for VLC Transmitters: Performance Analysis, Challenges, and Opportunities—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs—0
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts—0
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering—0
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces—0
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity—0
AU-vMAE: Knowledge-Guide Action Units Detection via Video Masked Autoencoder—0
Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction—0
LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion—0
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations—0
LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection—0
Towards Pre-training an Effective Respiratory Audio Foundation Model—0
LLMs grasp morality in concept—0
LLMs Prompted for Graphs: Hallucinations and Generative Capabilities—0
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps—0
LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models—0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation—0
AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models—0
LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation—0
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