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 76–100 of 9051 papers

TitleStatusHype
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction—0
Scalable and Cost-Efficient de Novo Template-Based Molecular GenerationCode1
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
The Cell Ontology in the age of single-cell omicsCode0
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field SamplingCode0
Exploration by Random Reward Perturbation—0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
Diversity-Guided MLP Reduction for Efficient Large Vision TransformersCode1
Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity—0
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark—0
A weighted quantum ensemble of homogeneous quantum classifiers—0
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models—0
OpenDance: Multimodal Controllable 3D Dance Generation Using Large-scale Internet Data—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
Correlated Errors in Large Language Models—0
CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction—0
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationCode0
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models—0
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms—0
Noise Consistency Regularization for Improved Subject-Driven Image Synthesis—0
The Lock-in Hypothesis: Stagnation by Algorithm—0
Antithetic Noise in Diffusion Models—0
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly—0
Combating Misinformation in the Arab World: Challenges & Opportunities—0
Show:102550
← PrevPage 4 of 363Next →

No leaderboard results yet.