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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 6051–6075 of 9051 papers

TitleStatusHype
Object detection and tracking benchmark in industry based on improved correlation filter—0
Object Detection as a Positive-Unlabeled Problem—0
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN—0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems—0
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance—0
Object Scene Representation Transformer—0
Observing the Population Dynamics in GE by means of the Intrinsic Dimension—0
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction—0
Occupational segregation in a Roy model with composition preferences—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
Offensive Language Detection on Video Live Streaming Chat—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Offline Learning of Controllable Diverse Behaviors—0
Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems—0
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management—0
Offline Reinforcement Learning Hands-On—0
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition—0
A Seft-adaptive Multicellular GEP Algorithm Based On Fuzzy Control For Function Optimization—0
Omega: An Architecture for AI Unification—0
Omega: An Architecture for AI Unification—0
A Search for Improved Performance in Regular Expressions—0
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets—0
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
A Scalable AI Approach for Clinical Trial Cohort Optimization—0
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