SOTAVerified

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 13411350 of 9051 papers

TitleStatusHype
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning0
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
Test-time Adaptation for Cross-modal Retrieval with Query Shift0
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?Code1
A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach0
ComPO: Community Preferences for Language Model Personalization0
Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery0
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