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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 2426–2450 of 9051 papers

TitleStatusHype
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models—0
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic LanguagesCode2
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples—0
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class—0
Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior—0
CompilerDream: Learning a Compiler World Model for General Code OptimizationCode1
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language ModelsCode2
Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant—0
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code—0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering—0
Large Language Models as In-context AI Generators for Quality-Diversity—0
Understanding attention-based encoder-decoder networks: a case study with chess scoresheet recognition—0
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model—0
Pattern-Aware Chain-of-Thought Prompting in Large Language Models—0
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
Taming Diffusion Probabilistic Models for Character ControlCode3
Qualitative Approaches to Voice UX—0
Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items—0
WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction—0
Towards Multi-Morphology Controllers with Diversity and Knowledge DistillationCode0
Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-hop Question Answering—0
Fidelitous Augmentation of Human Accelerometric Data for Deep Learning—0
Better Synthetic Data by Retrieving and Transforming Existing DatasetsCode7
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