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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

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Showing 2351–2375 of 9051 papers

TitleStatusHype
Towards Iris Presentation Attack Detection with Foundation Models—0
Ultrasound Image Synthesis Using Generative AI for Lung Ultrasound Detection—0
Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket RecommendationCode0
Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction—0
Supervision policies can shape long-term risk management in general-purpose AI modelsCode0
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models—0
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion ModelsCode0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models—0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation—0
Consistent Flow Distillation for Text-to-3D Generation—0
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation GenerationCode0
Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance—0
Learning Compact and Robust Representations for Anomaly Detection—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints—0
EpiCoder: Encompassing Diversity and Complexity in Code Generation—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
Multi-Hypothesis Prediction for Portfolio Optimization: A Structured Ensemble Learning Approach to Risk Diversification—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text GenerationCode0
Finding A Voice: Evaluating African American Dialect Generation for Chatbot TechnologyCode0
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends—0
SALE-Based Offline Reinforcement Learning with Ensemble Q-Networks—0
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