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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 4476–4500 of 9051 papers

TitleStatusHype
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites—0
Climate land use and other drivers impacts on island ecosystem services: a global review—0
A Quality-Diversity Controllable GAN for Text Generation—0
Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints—0
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation—0
Empowering Large Language Models for Textual Data Augmentation—0
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
Empirical evidence of Large Language Model's influence on human spoken communication—0
CLearViD: Curriculum Learning for Video Description—0
EmpHi: Generating Empathetic Responses with Human-like Intents—0
Class label autoencoder for zero-shot learning—0
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation—0
Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning—0
Emotion Embeddings x2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets—0
Classifier Pool Generation based on a Two-level Diversity Approach—0
A proposed new metric for the conceptual diversity of a text—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition—0
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments—0
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models—0
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