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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 1601–1625 of 9051 papers

TitleStatusHype
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Visualizing Temporal Topic Embeddings with a Compass—0
Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language ModelsCode3
Quantile Regression for Distributional Reward Models in RLHFCode0
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science—0
Benchmarking Large Language Model Uncertainty for Prompt OptimizationCode0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?—0
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization—0
Abnormal Event Detection In Videos Using Deep Embedding—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models—0
A Compressive Memory-based Retrieval Approach for Event Argument Extraction—0
Towards Diverse and Efficient Audio Captioning via Diffusion Models—0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity—0
Frequency Tracking Features for Data-Efficient Deep Siren IdentificationCode0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Polarforming for Wireless Communications: Modeling and Performance Analysis—0
An Evaluation Framework for Attributed Information Retrieval using Large Language ModelsCode0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM)—0
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