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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 2801–2825 of 9051 papers

TitleStatusHype
SwaQuAD-24: QA Benchmark Dataset in Swahili—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
MetaAlign: Align Large Language Models with Diverse Preferences during Inference TimeCode0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection—0
Instruction-Driven Game Engine: A Poker Case Study—0
The Disparate Benefits of Deep EnsemblesCode0
Improving the Estimation of Attenuation in Q/V Band Systems with a Kalman-Based Scintillation Filter—0
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks—0
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation—0
360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language—0
Expanding Chatbot Knowledge in Customer Service: Context-Aware Similar Question Generation Using Large Language Models—0
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability—0
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph—0
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs—0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization—0
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners—0
Multi-trait User Simulation with Adaptive Decoding for Conversational Task AssistantsCode0
Using Protected Attributes to Consider Fairness in Multi-Agent Systems—0
Preference Optimization with Multi-Sample Comparisons—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Personas with Attitudes: Controlling LLMs for Diverse Data AnnotationCode0
Cross-Dataset Generalization in Deep Learning—0
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