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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 3601–3625 of 9051 papers

TitleStatusHype
Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?—0
Ensemble of Counterfactual ExplainersCode0
A Comprehensive Augmentation Framework for Anomaly Detection—0
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning—0
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer SamplesCode0
Policy Diversity for Cooperative Agents—0
FaceCoresetNet: Differentiable Coresets for Face Set RecognitionCode0
Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series SummarizationCode0
Residual Denoising Diffusion ModelsCode2
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating ConditionsCode0
Predator-prey survival pressure is sufficient to evolve swarming behaviors—0
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity ConstraintsCode0
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven AnalysisCode0
Lexical Diversity in Kinship Across Languages and Dialects—0
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Audio Generation with Multiple Conditional Diffusion Model—0
Evaluation of Faithfulness Using the Longest Supported Subsequence—0
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