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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 1–25 of 9051 papers

TitleStatusHype
Visual Place Recognition for Large-Scale UAV Applications—0
Adversarial attacks to image classification systems using evolutionary algorithms—0
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems—0
Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms—0
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection—0
Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning—0
Sparse Regression Codes exploit Multi-User Diversity without CSI—0
Mixture of Experts in Large Language Models—0
Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMsCode0
Turning the Tide: Repository-based Code Reflection—0
Exploring Design of Multi-Agent LLM Dialogues for Research IdeationCode1
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality—0
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image RetrievalCode0
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework—0
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Is Diversity All You Need for Scalable Robotic Manipulation?Code7
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding—0
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation—0
CTR-Guided Generative Query Suggestion in Conversational Search—0
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics EmulationCode2
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
3D Shape Generation: A Survey—0
Efficient Skill Discovery via Regret-Aware Optimization—0
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs—0
From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market ForecastingCode0
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