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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 4251–4275 of 9051 papers

TitleStatusHype
Siamese Graph Learning for Semi-supervised Age EstimationCode0
Architext: Language-Driven Generative Architecture Design—0
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos—0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
Compressed Heterogeneous Graph for Abstractive Multi-Document SummarizationCode0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic NetworksCode0
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning—0
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous DrivingCode1
An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimation—0
Semi-Federated Learning for Collaborative Intelligence in Massive IoT NetworksCode1
Diversity-Measurable Anomaly DetectionCode1
Knowledge-augmented Few-shot Visual Relation Detection—0
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
Unifying Layout Generation with a Decoupled Diffusion Model—0
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
The evolution of cooperation and diversity by integrated indirect reciprocity—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back—0
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired SkillsCode0
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding—0
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
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