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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 651–675 of 9051 papers

TitleStatusHype
Efficient Dataset Distillation via Minimax DiffusionCode1
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text RecognizerCode1
Generating Progressive Images from Pathological Transitions via Diffusion ModelCode1
Multi-Task Reinforcement Learning with Mixture of Orthogonal ExpertsCode1
Safer-Instruct: Aligning Language Models with Automated Preference DataCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
AI-generated text boundary detection with RoFTCode1
Towards Reasoning in Large Language Models via Multi-Agent Peer Review CollaborationCode1
Self-Evolved Diverse Data Sampling for Efficient Instruction TuningCode1
Can LLMs Patch Security Issues?Code1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysisCode1
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language ModelsCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient SelectionCode1
SimMMDG: A Simple and Effective Framework for Multi-modal Domain GeneralizationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
TarGEN: Targeted Data Generation with Large Language ModelsCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
Generative Fractional Diffusion ModelsCode1
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