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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 301–325 of 9051 papers

TitleStatusHype
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented DiffusionCode2
Boosting Latent Diffusion with Flow MatchingCode2
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Adapting Precomputed Features for Efficient Graph CondensationCode1
Dan: Deep attention neural network for news recommendationCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Automating Rigid Origami DesignCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Adaptable Agent Populations via a Generative Model of PoliciesCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
AutoMix: Automatically Mixing Language ModelsCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
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