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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 8501–8525 of 9051 papers

TitleStatusHype
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
XAGen: 3D Expressive Human Avatars Generation—0
Diverse Score Distillation—0
Compress Guidance in Conditional Diffusion Sampling—0
Diverse Shape Completion via Style Modulated Generative Adversarial Networks—0
Diverse Single Image Generation with Controllable Global Structure—0
Compressed Sensing with Approximate Priors via Conditional Resampling—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Table-to-Text Generation with Pretrained Diffusion Models—0
Diverse Video Captioning Through Latent Variable Expansion—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Diverse Yet Efficient Retrieval using Hash Functions—0
Diversidade linguística e inclusão digital: desafios para uma ia brasileira—0
Advanced Strategies of Portfolio Management in the Heston Market Model—0
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution—0
Comprehensive Annotation of Multiword Expressions in a Social Web Corpus—0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning—0
Diversified Hidden Markov Models for Sequential Labeling—0
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