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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 591600 of 9051 papers

TitleStatusHype
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7TCode1
MGF: Mixed Gaussian Flow for Diverse Trajectory PredictionCode1
PreAct: Prediction Enhances Agent's Planning AbilityCode1
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation StrategiesCode1
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual ConnectionsCode1
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language ModelsCode1
QGFN: Controllable Greediness with Action ValuesCode1
INSIDE: LLMs' Internal States Retain the Power of Hallucination DetectionCode1
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