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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

Showing 14511475 of 9051 papers

TitleStatusHype
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Multi-Objective GFlowNetsCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Adversarial Parametric Pose PriorCode1
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activityCode1
Neural Syntactic Preordering for Controlled Paraphrase GenerationCode1
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of GarmentsCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill DiscoveryCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
Any-Play: An Intrinsic Augmentation for Zero-Shot CoordinationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language ModelsCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
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