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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
Exploring and Controlling Diversity in LLM-Agent Conversation—0
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Compression using Discrete Multi-Level Divisor Transform for Heterogeneous Sensor Data—0
Free-Space Optical MISO Communications With an Array of Detectors—0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation—0
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization—0
Compress Guidance in Conditional Diffusion Sampling—0
A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection—0
Exploratory Data Analysis of Urdu Poetry—0
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective—0
Exploration by Random Reward Perturbation—0
Compressed Sensing with Approximate Priors via Conditional Resampling—0
A Simple Background Augmentation Method for Object Detection with Diffusion Model—0
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms—0
Explorable Tone Mapping Operators—0
Exploiting Web Images for Weakly Supervised Object Detection—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition—0
EXPLOITING SEMANTIC COHERENCE TO IMPROVE PREDICTION IN SATELLITE SCENE IMAGE ANALYSIS: APPLICATION TO DISEASE DENSITY ESTIMATION—0
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph—0
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