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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 5126–5150 of 9051 papers

TitleStatusHype
Scaffold-Based Multi-Objective Drug Candidate Optimization—0
Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs—0
Scalable Source Code Similarity Detection in Large Code Repositories—0
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
Scaled ReLU Matters for Training Vision Transformers—0
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws—0
A Comprehensive Social Bias Audit of Contrastive Vision Language Models—0
Scaling Language Data Import/Export with a Data Transformer Interface—0
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos—0
Scaling Parameter-Constrained Language Models with Quality Data—0
Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations—0
Scaling Pre-training to One Hundred Billion Data for Vision Language Models—0
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models—0
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity—0
Scaling up Image Segmentation across Data and Tasks—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
SceneGen: Learning to Generate Realistic Traffic Scenes—0
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach—0
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
SceneX: Procedural Controllable Large-scale Scene Generation—0
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness—0
Why scholars are diagramming neural network models—0
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