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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 851–875 of 9051 papers

TitleStatusHype
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient SpaceCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Design of Chain-of-Thought in Math Problem SolvingCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputCode1
KLPT – Kurdish Language Processing ToolkitCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Euler-Lagrange Analysis of Generative Adversarial NetworksCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
Exploring Design of Multi-Agent LLM Dialogues for Research IdeationCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
A single-cell gene expression language modelCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
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