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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 376–400 of 9051 papers

TitleStatusHype
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
AutoMix: Automatically Mixing Language ModelsCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Dataset Factorization for CondensationCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Deep Color Transfer using Histogram AnalogyCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Dan: Deep attention neural network for news recommendationCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Dataset GrowthCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
Automating Rigid Origami DesignCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
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