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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 2751–2775 of 9051 papers

TitleStatusHype
Comparative Analysis of Indicators for Multiobjective Diversity Optimization—0
A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges—0
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions—0
Single-Shot Phase Diversity Wavefront Sensing in Deep Turbulence via Metasurface Optics—0
LLMs for Extremely Low-Resource Finno-Ugric LanguagesCode0
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis—0
Heterogeneous Random ForestCode0
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization—0
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms—0
Retrieving Implicit and Explicit Emotional Events Using Large Language Models—0
Towards a Similarity-adjusted Surprisal Theory—0
Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity—0
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models—0
Differentially Private Learning Needs Better Model Initialization and Self-DistillationCode0
Deep Generative Models for 3D Medical Image Synthesis—0
AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models—0
KMS states of Information Flow in Directed Brain Synaptic Networks—0
Audio-to-Score Conversion Model Based on Whisper methodology—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
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