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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 5176–5200 of 9051 papers

TitleStatusHype
Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation—0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
Segmentation-based Extraction of Key Components from ECG Images: A Framework for Precise Classification and Digitization—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation—0
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation—0
Selecting Diverse Features via Spectral Regularization—0
Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search—0
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms—0
Mutation Effect Generalizability under Selection-Drift—0
Selective clustering ensemble based on kappa and F-score—0
Selective Focusing Learning in Conditional GANs—0
Selectively increasing the diversity of GAN-generated samples—0
Self-Adversarial Learning with Comparative Discrimination for Text Generation—0
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity—0
Self-Consuming Generative Models Go MAD—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Self-Feedback DETR for Temporal Action Detection—0
Self-Paced Learning with Diversity—0
Self-Referential Quality Diversity Through Differential Map-Elites—0
Self-reinforcing Unsupervised Matching—0
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