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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 126150 of 9051 papers

TitleStatusHype
Flaming-hot Initiation with Regular Execution Sampling for Large Language ModelsCode2
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
PUMA: Empowering Unified MLLM with Multi-granular Visual GenerationCode2
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language ModelsCode2
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven InteractionsCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Conditional Image Synthesis with Diffusion Models: A SurveyCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
HSIGene: A Foundation Model For Hyperspectral Image GenerationCode2
Vista3D: Unravel the 3D Darkside of a Single ImageCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Segment Any Mesh: Zero-shot Mesh Part Segmentation via Lifting Segment Anything 2 to 3DCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian LanguagesCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive DiversityCode2
MusiConGen: Rhythm and Chord Control for Transformer-Based Text-to-Music GenerationCode2
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation TasksCode2
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
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