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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 4026–4050 of 9051 papers

TitleStatusHype
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques—0
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Comparison of classifiers in challenge scheme—0
Fairness and Diversity in Information Access Systems—0
Dynamics of niche construction in adaptable populations evolving in diverse environmentsCode0
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking—0
Private Training Set Inspection in MLaaS—0
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
DarkBERT: A Language Model for the Dark Side of the Internet—0
The Structure and Dynamics of Knowledge Graphs, with SuperficialityCode0
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2Code0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Harvesting Event Schemas from Large Language ModelsCode1
Comparison of machine learning models applied on anonymized data with different techniques—0
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity—0
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
Semantic uncertainty guides the extension of conventions to new referents—0
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
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