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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 501510 of 9051 papers

TitleStatusHype
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding PerspectiveCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language ModelsCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs DistillationCode1
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language ModelCode1
SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified FlowCode1
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source DataCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
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