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Diversity
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yifei-liu-stat/pass
공식 코드Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification
https://github.com/yigang-chen/scope-dti
공식 코드https://github.com/yikunhan42/tinyllm
공식 코드https://github.com/yining043/PDP-N2S
공식 코드Efficient Neural Neighborhood Search for Pickup and Delivery Problems
https://github.com/yinyjin/dualanodiff
공식 코드DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
https://github.com/yixinliu233/simpdm
공식 코드Self-Supervision Improves Diffusion Models for Tabular Data Imputation
https://github.com/yjiangcm/webr
공식 코드Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction
https://github.com/yjyddq/DLIF
공식 코드https://github.com/ykang/gratis
공식 코드GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/ykang/tsgeneration
공식 코드GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
https://github.com/ymcui/cmrc2017
공식 코드Dataset for the First Evaluation on Chinese Machine Reading Comprehension
https://github.com/ymcui/cmrc2019
공식 코드A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
https://github.com/ymp5078/semantic-stacking
공식 코드S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
https://github.com/ymxlzgy/commonscenes
공식 코드CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
https://github.com/yongcaoplus/probingchatgpt
공식 코드Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
https://github.com/yonisGit/BEE
공식 코드BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation
https://github.com/yoshinobc/Level-Generation-for-Angry-Birds-with-Sequential-VAE-and-Latent-Variable-Evolution
공식 코드Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution
https://github.com/yossilevii100/EPiC
공식 코드EPiC: Ensemble of Partial Point Clouds for Robust Classification
https://github.com/yotam-gafni/triangulation_attack
공식 코드https://github.com/youncy-hu/mage
공식 코드Make It Move: Controllable Image-to-Video Generation with Text Descriptions
https://github.com/younggeun-kim/NCSR
공식 코드Noise Conditional Flow Model for Learning the Super-Resolution Space
https://github.com/youngseng/unifiedgesture
공식 코드UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons
https://github.com/youquanl/m3net
공식 코드https://github.com/youwyu/Adaptive-Diffusion-Terrain
공식 코드Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation
https://github.com/yqx7150/WACM
공식 코드Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
https://github.com/yqx7150/hgm
공식 코드High-dimensional Assisted Generative Model for Color Image Restoration
https://github.com/yqx7150/shgm
공식 코드Generative Modeling in Structural-Hankel Domain for Color Image Inpainting
https://github.com/ysu1989/CrossSemparse
공식 코드https://github.com/ysyscool/DINet
공식 코드https://github.com/ytongbai/LVM
공식 코드Sequential Modeling Enables Scalable Learning for Large Vision Models
https://github.com/yu-fangxu/for
공식 코드Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal Examples
https://github.com/yuanyuan-yuan/neural-coverage
공식 코드Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
https://github.com/yuecao0119/mminstruct
공식 코드MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity
https://github.com/yueyu1030/attrprompt
공식 코드Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
https://github.com/yueyu1030/patron
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.