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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 2676–2700 of 9051 papers

TitleStatusHype
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space ModelsCode2
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation—0
Federated Data Model—0
Distributed Deep Learning for Modulation Classification in 6G Cell-Free Wireless Networks—0
Historical Astronomical Diagrams Decomposition in Geometric Primitives—0
LAFS: Landmark-based Facial Self-supervised Learning for Face RecognitionCode1
GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection MethodCode1
Humans-in-the-Building: Getting Rid of Thermostats for Optimal Thermal Comfort Control in Energy Management Systems—0
Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control—0
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study—0
Multiple Population Alternate Evolution Neural Architecture Search—0
Complementing cell taxonomies with a multicellular functional analysis of tissues—0
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution—0
Attacking Transformers with Feature Diversity Adversarial Perturbation—0
Speeding up 6-DoF Grasp Sampling with Quality-Diversity—0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model—0
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via PromptsCode1
SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target DetectionCode1
Face2Diffusion for Fast and Editable Face PersonalizationCode2
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