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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 4076–4100 of 9051 papers

TitleStatusHype
Considerations for Ethical Speech Recognition Datasets—0
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingCode1
A New Non-Negative Matrix Factorization Approach for Blind Source Separation of Cardiovascular and Respiratory Sound Based on the Periodicity of Heart and Lung Function—0
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent LearningCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
From Local to Global: Navigating Linguistic Diversity in the African ContextCode0
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
Multi-Task Multi-Behavior MAP-Elites—0
Multidimensional Fairness in Paper Recommendation—0
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity AlignmentCode0
Emergent cooperative behavior in transient compartments—0
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas—0
Reconstructing seen images from human brain activity via guided stochastic search—0
StyleGenes: Discrete and Efficient Latent Distributions for GANs—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Class-Balancing Diffusion ModelsCode1
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels—0
Political Strategies to Overcome Climate Policy Obstructionism—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
Generating images of rare concepts using pre-trained diffusion modelsCode1
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers—0
Motion-Conditioned Diffusion Model for Controllable Video Synthesis—0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
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