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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 3776–3800 of 9051 papers

TitleStatusHype
Measuring Lexical Diversity in Texts: The Twofold Length Problem—0
SVIT: Scaling up Visual Instruction TuningCode3
Answering Ambiguous Questions via Iterative PromptingCode1
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI—0
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel SynthesisCode1
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure—0
Pattern and Polarization Diversity Multi-Sector Annular Antenna for IoT Applications—0
Amplifying Limitations, Harms and Risks of Large Language Models—0
Grammatical Parameters from a Gene-like Code to Self-Organizing Attractors—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
A Dataset of Inertial Measurement Units for Handwritten English Alphabets—0
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning BenchmarksCode1
Self-Consuming Generative Models Go MAD—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Fighting the disagreement in Explainable Machine Learning with consensus—0
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder—0
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport—0
REAL: A Representative Error-Driven Approach for Active LearningCode0
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review—0
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations—0
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCoCode0
Generative Data Augmentation for Aspect Sentiment Quad PredictionCode1
Discovering Patterns of Definitions and Methods from Scientific Documents—0
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