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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 2301–2325 of 9051 papers

TitleStatusHype
Orthogonally Initiated Particle Swarm Optimization with Advanced Mutation for Real-Parameter Optimization—0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models—0
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in MammographyCode2
Asymptotic theory of in-context learning by linear attentionCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB ImagesCode1
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
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
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented DialoguesCode0
Flow Score Distillation for Diverse Text-to-3D Generation—0
Grounded 3D-LLM with Referent TokensCode2
Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary AlgorithmsCode0
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models—0
Generative Design through Quality-Diversity Data Synthesis and Language Models—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Sharpness-Aware Minimization in Genetic Programming—0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
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