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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 2101–2125 of 9051 papers

TitleStatusHype
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive RegularizationCode0
A Corpus for Reasoning About Natural Language Grounded in PhotographsCode0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
How well do you know your summarization datasets?Code0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
How to partition diversityCode0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Class Incremental Learning with Multi-Teacher DistillationCode0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
How Well Do LLMs Identify Cultural Unity in Diversity?Code0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and PretrainingCode0
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across LanguagesCode0
LMEraser: Large Model Unlearning through Adaptive Prompt TuningCode0
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBsCode0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
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