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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 1451–1475 of 9051 papers

TitleStatusHype
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Rainbow Memory: Continual Learning with a Memory of Diverse SamplesCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
Clotho: An Audio Captioning DatasetCode1
End-to-End Optimization of Scene LayoutCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Improving Contrastive Learning on Imbalanced Data via Open-World SamplingCode1
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Adversarial Parametric Pose PriorCode1
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic SegmentationCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identificationCode1
Implicit Neural Representations for Variable Length Human Motion GenerationCode1
Regularizing Deep Networks with Semantic Data AugmentationCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
Any-Play: An Intrinsic Augmentation for Zero-Shot CoordinationCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree SearchCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
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