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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 5526–5550 of 9051 papers

TitleStatusHype
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning—0
MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation—0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning—0
Metropolis-Hastings Data Augmentation for Graph Neural Networks—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer—0
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model—0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models—0
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination—0
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation—0
When Debate Fails: Bias Reinforcement in Large Language Models—0
A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias—0
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination—0
A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding—0
Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products—0
microGAN: Promoting Variety through Microbatch Discrimination—0
Training Group Orthogonal Neural Networks with Privileged Information—0
Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images—0
Microphone Array Signal Processing and Deep Learning for Speech Enhancement—0
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