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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 2126–2150 of 9051 papers

TitleStatusHype
SimGen: Simulator-conditioned Driving Scene Generation—0
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on RedditCode0
Data Augmentation by Fuzzing for Neural Test Generation—0
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation—0
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic EntropyCode1
Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions—0
Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset—0
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
An Optimism-based Approach to Online Evaluation of Generative Models—0
Should XAI Nudge Human Decisions with Explanation Biasing?—0
Beyond the Norms: Detecting Prediction Errors in Regression Models—0
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow UnderstandingCode2
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI—0
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems—0
Learning Continually by Spectral Regularization—0
mHuBERT-147: A Compact Multilingual HuBERT ModelCode0
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
UnSupDLA: Towards Unsupervised Document Layout Analysis—0
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-TrainingCode1
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
An Efficient Framework for Crediting Data Contributors of Diffusion Models—0
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