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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 4001–4025 of 9051 papers

TitleStatusHype
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
Enhancing biodiversity through intraspecific suppression in large ecosystems—0
Multi-factor Sequential Re-ranking with Perception-Aware Diversification—0
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion DiffusionCode1
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data—0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training—0
Modeling the Q-Diversity in a Min-max Play Game for Robust OptimizationCode0
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
DiffCap: Exploring Continuous Diffusion on Image Captioning—0
Self-QA: Unsupervised Knowledge Guided Language Model AlignmentCode3
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
Few-shot 3D Shape Generation—0
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis—0
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments—0
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction—0
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time SeriesCode2
Learning Diverse Risk Preferences in Population-based Self-playCode1
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language ModelsCode2
DMDD: A Large-Scale Dataset for Dataset Mentions Detection—0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Augmented Message Passing Stein Variational Gradient Descent—0
Improving Recommendation System Serendipity Through Lexicase Selection—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
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