SOTAVerified

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 2551–2575 of 9051 papers

TitleStatusHype
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative ComprehensionCode0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Corpus Considerations for Annotator Modeling and ScalingCode0
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression—0
Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks—0
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
Diffusion Deepfake—0
Hallucination Diversity-Aware Active Learning for Text Summarization—0
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image—0
WcDT: World-centric Diffusion Transformer for Traffic Scene GenerationCode1
Voice EHR: Introducing Multimodal Audio Data for Health—0
GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems—0
Guide to k-mer approaches for genomics across the tree of lifeCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations—0
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented DiffusionCode2
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweetsCode1
A Simple Yet Effective Approach for Diversified Session-Based RecommendationCode0
Rationale-based Opinion SummarizationCode0
Advancing the Arabic WordNet: Elevating Content Quality—0
FairRAG: Fair Human Generation via Fair Retrieval Augmentation—0
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