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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 6876–6900 of 9051 papers

TitleStatusHype
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word ProblemsCode1
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue—0
Behavioral Repertoires for Soft Tensegrity Robots—0
Cosine Similarity of Multimodal Content Vectors for TV Programmes—0
Curriculum Learning with Diversity for Supervised Computer Vision Tasks—0
Target Conditioning for One-to-Many Generation—0
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
div2vec: Diversity-Emphasized Node Embedding—0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Modeling the Evolution of Networks as Shrinking Structural Diversity—0
F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax—0
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference—0
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction—0
Label-Based Diversity Measure Among Hidden Units of Deep Neural Networks: A Regularization Method—0
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Multi-source Data Mining for e-Learning—0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Group-wise Contrastive Learning for Neural Dialogue GenerationCode1
A Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationCode0
Understanding Deformable Alignment in Video Super-Resolution—0
Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development—0
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