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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 4676–4700 of 9051 papers

TitleStatusHype
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Bias and Diversity in Synthetic-based Face Recognition—0
Instruction-based Hypergraph Pretraining—0
Instruction Diversity Drives Generalization To Unseen Tasks—0
Instruction-Driven Game Engine: A Poker Case Study—0
Beyond the Norms: Detecting Prediction Errors in Regression Models—0
Insular microbiogeography—0
Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages—0
Integrating Chaotic Evolutionary and Local Search Techniques in Decision Space for Enhanced Evolutionary Multi-Objective Optimization—0
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era—0
Integrating Image Captioning with Rule-based Entity Masking—0
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
Toward Robust Long Range Policy Transfer—0
Active Learning on Synthons for Molecular Design—0
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition—0
Intelligence Graph—0
Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks—0
IntenT5: Search Result Diversification using Causal Language Models—0
We Need to Talk About Classification Evaluation Metrics in NLP—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations—0
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks—0
Interaction patterns and diversity in assembled ecological communities—0
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes—0
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