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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 7776–7800 of 9051 papers

TitleStatusHype
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search—0
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving—0
Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content—0
Modeling competitive evolution of multiple languages—0
Modeling User Selection in Quality Diversity—0
Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision—0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
On the Evaluation of Conditional GANsCode0
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos—0
Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex—0
Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection—0
An Optimistic Perspective on Offline Reinforcement LearningCode1
Procedural Content Generation through Quality DiversityCode1
Diverse Agents for Ad-Hoc Cooperation in Hanabi—0
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm—0
Video Question Generation via Cross-Modal Self-Attention Networks Learning—0
Twin Auxiliary Classifiers GANCode0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue—0
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationCode0
Supervised Uncertainty Quantification for Segmentation with Multiple AnnotationsCode0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
How we do things with words: Analyzing text as social and cultural data—0
Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment—0
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