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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

Showing 831840 of 9051 papers

TitleStatusHype
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
Efficient Neural Neighborhood Search for Pickup and Delivery ProblemsCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive ScenariosCode1
Deep Image Harmonization with Learnable AugmentationCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
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