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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 676–700 of 9051 papers

TitleStatusHype
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Diversity-Guided Multi-Objective Bayesian Optimization With Batch EvaluationsCode1
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identificationCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
Diversity-Aware Meta Visual PromptingCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Diversity-based Trajectory and Goal Selection with Hindsight Experience ReplayCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
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