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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 2951–2975 of 9051 papers

TitleStatusHype
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes—0
Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting—0
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning—0
Dynamic Multi-Objective Lion Swarm Optimization with Multi-strategy Fusion: An application in 6R robot trajectory planning—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks—0
Dynamic Post-Hoc Neural Ensemblers—0
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation—0
Dynamic Range Independent Image Quality Assessment—0
Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection—0
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires—0
Dynamic Scale Inference by Entropy Minimization—0
Dynamics of Algorithmic Content Amplification on TikTok—0
A Parameterized Family of Meta-Submodular Functions—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)—0
Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes—0
Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system—0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language—0
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets—0
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