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

Showing 10111020 of 9051 papers

TitleStatusHype
Frequency Domain Model Augmentation for Adversarial AttackCode1
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-ArtCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular GenerationCode1
ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022Code1
Forecasting Future World Events with Neural NetworksCode1
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video FramesCode1
Siamese Contrastive Embedding Network for Compositional Zero-Shot LearningCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Show:102550
← PrevPage 102 of 906Next →

No leaderboard results yet.