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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 1676–1700 of 9051 papers

TitleStatusHype
A practical generalization metric for deep networks benchmarking—0
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting—0
SCOPE: Sign Language Contextual Processing with Embedding from LLMsCode0
MarsCode Agent: AI-native Automated Bug Fixing—0
Imitating Language via Scalable Inverse Reinforcement Learning—0
Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity—0
An adaptative differential evolution with enhanced diversity and restart mechanismCode0
Seeing Your Speech Style: A Novel Zero-Shot Identity-Disentanglement Face-based Voice Conversion—0
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
TSO: Self-Training with Scaled Preference Optimization—0
Data Augmentation for Image Classification using Generative AI—0
DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity—0
LLMs Prompted for Graphs: Hallucinations and Generative Capabilities—0
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMsCode0
Sparse Uncertainty-Informed Sampling from Federated Streaming DataCode0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation—0
United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections—0
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning—0
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification—0
Illuminating the Diversity-Fitness Trade-Off in Black-Box OptimizationCode0
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling—0
Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design—0
Iterative Graph AlignmentCode0
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