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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 7276–7300 of 9051 papers

TitleStatusHype
Scaling Language Data Import/Export with a Data Transformer Interface—0
A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling—0
A multimodal gesture recognition dataset for desktop human-computer interaction—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
A Multimodal Approach for Advanced Pest Detection and Classification—0
Scaling up Image Segmentation across Data and Tasks—0
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction—0
A multilayer PPI network analysis of different life stages in C. elegans—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
Using LLMs as prompt modifier to avoid biases in AI image generators—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
Using LSTMs for climate change assessment studies on droughts and floods—0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding—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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