https://github.com/lyn1874/fedpvr
공식 코드On the effectiveness of partial variance reduction in federated learning with heterogeneous data
Papers with Code 아카이브
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/lyndonzheng/Pluralistic-Inpainting
공식 코드https://github.com/lynecho/repbias_nbr
공식 코드Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation
https://github.com/lyq312318224/dream
공식 코드DREAM: Efficient Dataset Distillation by Representative Matching
https://github.com/m-salehpour/spq
공식 코드https://github.com/m0re4u/acal-subjective
공식 코드https://github.com/m1k2zoo/fedmedicl
공식 코드FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
https://github.com/m3dv/lefusion
공식 코드LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
https://github.com/madlab-ucr/grid
공식 코드GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
https://github.com/madrylab/journey-trak
공식 코드The Journey, Not the Destination: How Data Guides Diffusion Models
https://github.com/magenta/magenta/tree/main/magenta/models/image_stylization
공식 코드https://github.com/magiccircuit/treemix
공식 코드TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
https://github.com/magnumresearchgroup/bash_gen
공식 코드NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation
https://github.com/magnumresearchgroup/magnum-nlc2cmd
공식 코드NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation
https://github.com/mahmad000/atl-sst
공식 코드Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
https://github.com/mahossam/OptiGAN
공식 코드OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
https://github.com/mahyarkoy/emergent_asymmetry_pr
공식 코드https://github.com/mailong25/peft_diversity
공식 코드Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
https://github.com/mainlp/barner
공식 코드Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
https://github.com/mainlp/nnose
공식 코드https://github.com/mainlp/semantic_components
공식 코드Semantic Component Analysis: Discovering Patterns in Short Texts Beyond Topics
https://github.com/majumderb/compac
공식 코드Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions
https://github.com/malagoutou/select-mosaic
공식 코드Select-Mosaic: Data Augmentation Method for Dense Small Object Scenes
https://github.com/manasgg44/meta_overfit
공식 코드https://github.com/manga-uofa/ed4udp
공식 코드Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing
https://github.com/maniadisg/divclust
공식 코드https://github.com/manikandan-ravikiran/cs6460-proj
공식 코드https://github.com/manish-pra/safemac
공식 코드https://github.com/manon-reusens/native_en_bias
공식 코드Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance
https://github.com/mansheej/icl-task-diversity
공식 코드Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
https://github.com/manuelcebrianramos/llm_supervision_policies
공식 코드Supervision policies can shape long-term risk management in general-purpose AI models
https://github.com/manueltonneau/twitter-unemployment
공식 코드Multilingual Detection of Personal Employment Status on Twitter
https://github.com/mapleleaf6/zode
공식 코드Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
https://github.com/mapooon/face2diffusion
공식 코드https://github.com/marclafon/gallop
공식 코드GalLoP: Learning Global and Local Prompts for Vision-Language Models
https://github.com/marcojira/fld
공식 코드Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.