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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 8526–8550 of 9051 papers

TitleStatusHype
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Diversified Late Acceptance Search—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Diversified Mutual Learning for Deep Metric Learning—0
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Diversified Sampling Improves Scaling LLM inference—0
Diversified Texture Synthesis with Feed-forward Networks—0
Diversified Visual Attention Networks for Fine-Grained Object Classification—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
Compositional Operators in Distributional Semantics—0
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images—0
Diversifying Agent's Behaviors in Interactive Decision Models—0
Diversifying AI: Towards Creative Chess with AlphaZero—0
Compositional Fine-Grained Low-Shot Learning—0
Diversifying Database Activity Monitoring with Bandits—0
Voice Conversion with Diverse Intonation using Conditional Variational Auto-Encoder—0
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning—0
Compositional diversity in visual concept learning—0
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies—0
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition—0
Diversifying Multi-aspect Search Results Using Simpson's Diversity Index—0
Diversifying Neural Conversation Model with Maximal Marginal Relevance—0
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling—0
Diversifying Question Generation over Knowledge Base via External Natural Questions—0
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