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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 6251–6275 of 9051 papers

TitleStatusHype
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
XS-VID: An Extremely Small Video Object Detection Dataset—0
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents—0
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
Zero-Shot 4D Lidar Panoptic Segmentation—0
Zero-Shot Human-Object Interaction Synthesis with Multimodal Priors—0
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Zonotope hit-and-run for efficient sampling from projection DPPs—0
Interactive Speech and Noise Modeling for Speech Enhancement—0
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?—0
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion—0
Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting—0
Intermittent migration can induce pulses of speciation in a two-island system—0
Internal Feedback in Biological Control: Architectures and Examples—0
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study—0
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation—0
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition—0
Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation—0
Interspeech 2025 URGENT Speech Enhancement Challenge—0
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations—0
Intra-Ensemble in Neural Networks—0
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