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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 13511375 of 9051 papers

TitleStatusHype
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputCode1
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier ExamplesCode1
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid MotionsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Language-guided Human Motion Synthesis with Atomic ActionsCode1
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User SimulatorCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Large-scale Unsupervised Semantic SegmentationCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
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