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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 3551–3575 of 9051 papers

TitleStatusHype
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT ScansCode0
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving—0
MC-GPT: Empowering Vision-and-Language Navigation with Memory Map and Reasoning Chains—0
Generative Design through Quality-Diversity Data Synthesis and Language Models—0
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models—0
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented DialoguesCode0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder—0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Flow Score Distillation for Diverse Text-to-3D Generation—0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
Sharpness-Aware Minimization in Genetic Programming—0
Facilitating Opinion Diversity through Hybrid NLP Approaches—0
BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform—0
HAAP: Vision-context Hierarchical Attention Autoregressive with Adaptive Permutation for Scene Text Recognition—0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—0
Growing Artificial Neural Networks for Control: the Role of Neuronal DiversityCode0
Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video—0
Open-Vocabulary Object Detection via Neighboring Region Attention Alignment—0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
Targeted Augmentation for Low-Resource Event Extraction—0
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants—0
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization—0
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