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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 321330 of 9051 papers

TitleStatusHype
TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven EvolutionCode1
Token Coordinated Prompt Attention is Needed for Visual PromptingCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Adapting Precomputed Features for Efficient Graph CondensationCode1
Instruction-Tuning Data Synthesis from Scratch via Web ReconstructionCode1
RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity SearchCode1
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language ModelsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
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