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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 1251–1300 of 9051 papers

TitleStatusHype
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain RemovalCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Biological Sequence Design with GFlowNetsCode1
GenDexGrasp: Generalizable Dexterous GraspingCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Generalized Probabilistic U-Net for medical image segementationCode1
Controllable Multi-Interest Framework for RecommendationCode1
Amortizing intractable inference in large language modelsCode1
Generating Object StampsCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
AutoMix: Automatically Mixing Language ModelsCode1
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiersCode1
Automating Rigid Origami DesignCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Goals as Reward-Producing ProgramsCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection MethodCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Greedy Bayesian Posterior Approximation with Deep EnsemblesCode1
grenedalf: population genetic statistics for the next generation of pool sequencingCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
BoostTree and BoostForest for Ensemble LearningCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
GS-Blur: A 3D Scene-Based Dataset for Realistic Image DeblurringCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Data Curation Alone Can Stabilize In-context LearningCode1
Harvesting Event Schemas from Large Language ModelsCode1
Heterogeneous Multi-task Learning with Expert DiversityCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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