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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 4926–4950 of 9051 papers

TitleStatusHype
From Local to Global: Navigating Linguistic Diversity in the African ContextCode0
Multidimensional Fairness in Paper Recommendation—0
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
Emergent cooperative behavior in transient compartments—0
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
StyleGenes: Discrete and Efficient Latent Distributions for GANs—0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
Reconstructing seen images from human brain activity via guided stochastic search—0
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels—0
Political Strategies to Overcome Climate Policy Obstructionism—0
Motion-Conditioned Diffusion Model for Controllable Video Synthesis—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers—0
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
GlyphDiffusion: Text Generation as Image Generation—0
Learning Trajectories are Generalization Indicators—0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders—0
Towards Mode Balancing of Generative Models via Diversity WeightsCode0
Benchmark tasks for Quality-Diversity applied to Uncertain domainsCode0
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