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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 15211530 of 9051 papers

TitleStatusHype
Portfolio Search and Optimization for General Strategy Game-PlayingCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose EstimationCode1
PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation LayoutCode1
Practical Wide-Angle Portraits Correction with Deep Structured ModelsCode1
PreAct: Prediction Enhances Agent's Planning AbilityCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Controllable Group Choreography using Contrastive DiffusionCode1
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