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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 5051–5060 of 9051 papers

TitleStatusHype
RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Road detection via a dual-task network based on cross-layer graph fusion modules—0
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs—0
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation—0
RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer—0
Robo-Troj: Attacking LLM-based Task Planners—0
Robust Allocations with Diversity Constraints—0
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text—0
Robust Deep Learning Ensemble against Deception—0
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