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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 15411550 of 9051 papers

TitleStatusHype
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational ModelCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
QGFN: Controllable Greediness with Action ValuesCode1
Qimera: Data-free Quantization with Synthetic Boundary Supporting SamplesCode1
Quality Controlled Paraphrase GenerationCode1
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features CriticsCode1
Quality-Diversity Generative Sampling for Learning with Synthetic DataCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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