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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 3791–3800 of 9051 papers

TitleStatusHype
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks—0
Attentive Aspect Modeling for Review-aware Recommendation—0
A COLD Approach to Generating Optimal Samples—0
Learning temporal relationships between symbols with Laplace Neural Manifolds—0
Geodesic-HOF: 3D Reconstruction Without Cutting Corners—0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
Diverse Neural Network Learns True Target Functions—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
Boosting Dialog Response Generation—0
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