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 9001–9025 of 9051 papers

TitleStatusHype
AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners—0
Adaptive Testing for LLM-Based Applications: A Diversity-based Approach—0
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training—0
Empowering Large Language Models for Textual Data Augmentation—0
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites—0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning—0
WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning—0
Encoding architecture algebra—0
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation—0
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization—0
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models—0
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting—0
Text Complexity And Linguistic Features: Is The Relationship Multilingual?—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems—0
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification—0
End-to-end Learnable Diversity-aware News Recommendation—0
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Energy Clustering for Unsupervised Person Re-identification—0
Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption—0
Energy-Efficient Design of Broad Beams for Massive MIMO Systems—0
Show:102550
← PrevPage 361 of 363Next →

No leaderboard results yet.