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 3701–3725 of 9051 papers

TitleStatusHype
Measuring Diversity of Game Scenarios—0
Coreset Selection for Object Detection—0
Fault Detection in Mobile Networks Using Diffusion Models—0
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language RepresentationCode0
The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation—0
Understanding the Role of Temperature in Diverse Question Generation by GPT-4—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning—0
Improved Paraphrase Generation via Controllable Latent DiffusionCode0
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic SegmentationCode0
Direct May Not Be the Best: An Incremental Evolution View of Pose GenerationCode0
Analyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms—0
Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping—0
RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning—0
WaveMo: Learning Wavefront Modulations to See Through Scattering—0
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization—0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
Generalization Gap in Data Augmentation: Insights from Illumination—0
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context LearningCode0
Achieving Tight O(4^k) Runtime Bounds on Jump_k by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity—0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices—0
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
Show:102550
← PrevPage 149 of 363Next →

No leaderboard results yet.