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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 3251–3275 of 9051 papers

TitleStatusHype
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion—0
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental ComparisonsCode0
Can LLMs Patch Security Issues?Code1
Semantically Grounded QFormer for Efficient Vision Language Understanding—0
Exploring Values in Museum Artifacts in the SPICE project: a Preliminary Study—0
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
ALBA: Adaptive Language-based Assessments for Mental Health—0
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachCode0
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and FlatnessCode0
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related TasksCode0
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models—0
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction ModelCode2
Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization—0
Workplace diversity and innovation performance: current state of affairs and future directions—0
Reconstructing Objects in-the-wild for Realistic Sensor Simulation—0
Generalization in medical AI: a perspective on developing scalable models—0
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data—0
Assessing Distractors in Multiple-Choice Tests—0
RankAug: Augmented data ranking for text classification—0
CLearViD: Curriculum Learning for Video Description—0
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