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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 376400 of 9051 papers

TitleStatusHype
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Real-World Depth Recovery via Structure Uncertainty Modeling and Inaccurate GT Depth Fitting0
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets0
Evaluating the Diversity and Quality of LLM Generated Content0
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach0
SLURG: Investigating the Feasibility of Generating Synthetic Online Fallacious Discourse0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning0
How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions0
Unravelling Technical debt topics through Time, Programming Languages and Repository0
Voice Conversion with Diverse Intonation using Conditional Variational Auto-Encoder0
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents0
Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence0
Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models0
Elucidating the Design Space of Multimodal Protein Language ModelsCode3
Using LLMs as prompt modifier to avoid biases in AI image generators0
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space0
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language ModelsCode1
Relation-Rich Visual Document Generator for Visual Information ExtractionCode0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation0
Can genomic analysis actually estimate past population size?0
Weight Ensembling Improves Reasoning in Language Models0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading0
Diversity-Fair Online Selection0
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