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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 7101–7125 of 9051 papers

TitleStatusHype
Non-saturating GAN training as divergence minimization—0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
No-Regret Caching via Online Mirror Descent—0
NOSE Augment: Fast and Effective Data Augmentation Without Searching—0
Not even wrong: Reply to Wagg et al—0
Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas—0
Novel Methods for Analyzing Cellular Interactions in Deep Learning-Based Image Cytometry: Spatial Interaction Potential and Co-Localization Index—0
NoveltyBench: Evaluating Language Models for Humanlike Diversity—0
NSOAMT -- New Search Only Approach to Machine Translation—0
NU:BRIEF -- A Privacy-aware Newsletter Personalization Engine for Publishers—0
NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation—0
NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation—0
N-Version Assessment and Enhancement of Generative AI—0
Nyström landmark sampling and regularized Christoffel functions—0
Objaverse: A Universe of Annotated 3D Objects—0
Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding—0
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
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
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
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