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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 12511275 of 9051 papers

TitleStatusHype
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Deep Color Transfer using Histogram AnalogyCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
Length-Controllable Image CaptioningCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
Biological Sequence Design with GFlowNetsCode1
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Amortizing intractable inference in large language modelsCode1
Lipschitz-constrained Unsupervised Skill DiscoveryCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
AutoMix: Automatically Mixing Language ModelsCode1
Automating Rigid Origami DesignCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive TestingCode1
Grounded Affordance from Exocentric ViewCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
Generative Flow Network for Listwise RecommendationCode1
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