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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 26–50 of 9051 papers

TitleStatusHype
A Hierarchical Deep Learning Approach for Minority Instrument DetectionCode0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study—0
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning—0
Cross-Layer Discrete Concept Discovery for Interpreting Language Models—0
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning—0
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Statistical Multicriteria Evaluation of LLM-Generated TextCode0
PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis—0
Rethinking Ecological Measures Of Functional Diversity—0
Perceptual Rationality: An Evolutionary Game Theory of Perceptually Rational Decision-Making—0
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based AgentsCode2
Beyond Blur: A Fluid Perspective on Generative Diffusion Models—0
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation—0
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario—0
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition—0
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Structured and Informed Probabilistic Modeling with the Thermodynamic Kolmogorov-Arnold ModelCode0
M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models—0
orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels—0
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMsCode0
Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMsCode0
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