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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 7826–7850 of 9051 papers

TitleStatusHype
Learning Diverse Image ColorizationCode0
T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story CompletionCode0
Photo-Sketching: Inferring Contour Drawings from ImagesCode0
T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance ObjectiveCode0
eCARLA-scenes: A synthetically generated dataset for event-based optical flow predictionCode0
Image Captioning via Dynamic Path CustomizationCode0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Learning Diversified Feature Representations for Facial Expression Recognition in the WildCode0
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Benchmarking Linguistic Diversity of Large Language ModelsCode0
Creative Diversity: Patterns in the Creative Habits of ~10,000 PeopleCode0
Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975-1999)Code0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
WLD-Reg: A Data-dependent Within-layer Diversity RegularizerCode0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
Constraining Generative Models for Engineering Design with Negative DataCode0
Team Formation amidst ConflictsCode0
Semantically Rich Local Dataset Generation for Explainable AI in GenomicsCode0
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in SummarizationCode0
TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with TransformersCode0
Im2Pencil: Controllable Pencil Illustration from PhotographsCode0
Learning from Multiple Sources for Data-to-Text and Text-to-DataCode0
ABD-Net: Attentive but Diverse Person Re-IdentificationCode0
Semantic Component Analysis: Discovering Patterns in Short Texts Beyond TopicsCode0
PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to EnglishCode0
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