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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 151175 of 9051 papers

TitleStatusHype
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language ModelsCode2
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone DesignCode2
Can Go AIs be adversarially robust?Code2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Scaling Efficient Masked Image Modeling on Large Remote Sensing DatasetCode2
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
Consistency-diversity-realism Pareto fronts of conditional image generative modelsCode2
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesCode2
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow UnderstandingCode2
Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal ExamplesCode2
VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term ModelingCode2
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language ModelsCode2
Diffusion Bridge Implicit ModelsCode2
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in MammographyCode2
Grounded 3D-LLM with Referent TokensCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
Learnable Item Tokenization for Generative RecommendationCode2
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
Benchmarking Representations for Speech, Music, and Acoustic EventsCode2
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