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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 3001–3025 of 9051 papers

TitleStatusHype
Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation—0
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science—0
Visualizing Temporal Topic Embeddings with a Compass—0
Benchmarking Large Language Model Uncertainty for Prompt OptimizationCode0
Quantile Regression for Distributional Reward Models in RLHFCode0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Abnormal Event Detection In Videos Using Deep Embedding—0
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization—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
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping DeformationCode0
Towards Diverse and Efficient Audio Captioning via Diffusion Models—0
A Compressive Memory-based Retrieval Approach for Event Argument Extraction—0
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Frequency Tracking Features for Data-Efficient Deep Siren IdentificationCode0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
An Evaluation Framework for Attributed Information Retrieval using Large Language ModelsCode0
Polarforming for Wireless Communications: Modeling and Performance Analysis—0
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series—0
When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential—0
A Novel Mathematical Framework for Objective Characterization of Ideas—0
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