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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 626–650 of 9051 papers

TitleStatusHype
olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language ModelsCode11
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
AIRIS2 : a Smart Gateway Diversity Algorithm for Very High-Throughput Satellite Systems—0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up QuestionsCode0
Delta Decompression for MoE-based LLMs CompressionCode2
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference OptimizationCode1
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation—0
HybridLinker: Topology-Guided Posterior Sampling for Enhanced Diversity and Validity in 3D Molecular Linker Generation—0
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable MetricCode1
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
SFLD: Reducing the content bias for AI-generated Image Detection—0
Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule GenerationCode0
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent SystemsCode0
Multi-objective Cat Swarm Optimization Algorithm based on a Grid System—0
Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming—0
ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems—0
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing—0
Chitrarth: Bridging Vision and Language for a Billion People—0
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence—0
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning—0
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