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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

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Showing 1721–1730 of 9051 papers

TitleStatusHype
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification—0
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning—0
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation—0
T3M: Text Guided 3D Human Motion Synthesis from SpeechCode1
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
HiFAKES: High-frequency synthetic appliance signatures generator for non-intrusive load monitoring—0
SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging—0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation—0
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender SystemsCode0
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