SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5501–5550 of 9051 papers

TitleStatusHype
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect—0
Mental Workload and Language Production in Non-Native Speaker IPA Interaction—0
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification—0
Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers—0
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters—0
Mesure simultanée des réponses impulsionnelles en macrodiversité—0
Meta Adaptation using Importance Weighted Demonstrations—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
Augmented Message Passing Stein Variational Gradient Descent—0
A systematic approach to random data augmentation on graph neural networks—0
Meta-Diversity Search in Complex Systems, A Recipe for Artificial Open-Endedness ?—0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning—0
MetaHead: An Engine to Create Realistic Digital Head—0
Metaheuristic Enhanced with Feature-Based Guidance and Diversity Management for Solving the Capacitated Vehicle Routing Problem—0
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation—0
Augmented Data Science: Towards Industrialization and Democratization of Data Science—0
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer—0
Meta-Learning for Few-Shot Land Cover Classification—0
Meta Learning for Multi-agent Communication—0
Two-Step Meta-Learning for Time-Series Forecasting Ensemble—0
Training and Inference Methods for High-Coverage Neural Machine Translation—0
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
Show:102550
← PrevPage 111 of 182Next →

No leaderboard results yet.