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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 381390 of 9051 papers

TitleStatusHype
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
Image Generation Diversity Issues and How to Tame ThemCode1
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in ThaiCode1
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language ModelsCode1
SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code GenerationCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
Generating Highly Designable Proteins with Geometric Algebra Flow MatchingCode1
Community Forensics: Using Thousands of Generators to Train Fake Image DetectorsCode1
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