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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 3326–3350 of 9051 papers

TitleStatusHype
Evaluating text coherence based on the graph of the consistency of phrases to identify symptoms of schizophrenia—0
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations—0
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder—0
Brain-Computer Interfaces for Emotional Regulation in Patients with Various Disorders—0
Diversifying Question Generation over Knowledge Base via External Natural Questions—0
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?—0
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration—0
Evaluation and Comparison of Edge-Preserving Filters—0
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling—0
Shadow Transfer: Single Image Relighting For Urban Road Scenes—0
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation—0
Evaluation of large-scale synthetic data for Grammar Error Correction—0
Evaluation of RF Fingerprinting-Aided RSS-Based Target Localization for Emergency Response—0
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition—0
Evaluation of Synthetic Datasets for Conversational Recommender Systems—0
FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting—0
Evaluation of Word Embeddings for the Social Sciences—0
Diversifying Neural Conversation Model with Maximal Marginal Relevance—0
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning—0
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks—0
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks—0
Diversifying Multi-aspect Search Results Using Simpson's Diversity Index—0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
Comparative Analysis of Indicators for Multiobjective Diversity Optimization—0
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition—0
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