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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 10611070 of 9051 papers

TitleStatusHype
BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects0
Prompt Transfer for Dual-Aspect Cross Domain Cognitive DiagnosisCode0
Neuro-Symbolic Data Generation for Math Reasoning0
Sometimes I am a Tree: Data Drives Unstable Hierarchical GeneralizationCode0
LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation0
RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse0
Demonstration Selection for In-Context Learning via Reinforcement Learning0
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities0
Exploring Transformer-Based Music Overpainting for Jazz Piano Variations0
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
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