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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 5076–5100 of 9051 papers

TitleStatusHype
Learning k-Determinantal Point Processes for Personalized Ranking—0
Learning Latent Space Models with Angular Constraints—0
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing—0
Learning Mixtures of Submodular Functions for Image Collection Summarization—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent—0
Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference—0
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics—0
Learning Personalized Alignment for Evaluating Open-ended Text Generation—0
Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design—0
Learning Policies for Contextual Submodular Prediction—0
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing—0
Learning Quadruped Locomotion Policies using Logical Rules—0
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database—0
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning—0
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Learning Semantic Segmentation from Multiple Datasets with Label Shifts—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data—0
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process—0
Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis—0
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