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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 2276–2300 of 9051 papers

TitleStatusHype
Diverse Score Distillation—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models—0
A review of TinyML—0
Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation—0
Collaborative Perception Datasets in Autonomous Driving: A Survey—0
A Review of the Marathi Natural Language Processing—0
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems—0
Collaborative Machine Learning Model Building with Families Using Co-ML—0
A review of approaches to modeling applied vehicle routing problems—0
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems—0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation—0
Co-Learning Empirical Games and World Models—0
Co-Learning Bayesian Optimization—0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments—0
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"—0
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues—0
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Coherent FDA Radar: Transmitter and Receiver Design and Analysis—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
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