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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 3376–3400 of 9051 papers

TitleStatusHype
Quality-Diversity through AI Feedback—0
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion ModelsCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
CLAIR: Evaluating Image Captions with Large Language Models—0
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation—0
AutoMix: Automatically Mixing Language ModelsCode1
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly DetectionCode1
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text GenerationCode0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
Program Translation via Code Distillation—0
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision ModelsCode1
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search DecodingCode0
An Empirical Study of Translation Hypothesis Ensembling with Large Language ModelsCode0
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignCode1
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models—0
CoCoFormer: A controllable feature-rich polyphonic music generation methodCode0
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack—0
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer—0
Private Synthetic Data Meets Ensemble Learning—0
Graph Neural Network approaches for single-cell data: A recent overview—0
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