https://github.com/francismontalbo/covid19_diagnosis_and_analysis_of_truncated_dcnns
MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 2
This branch include most common machine learning fundamental algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/francois-rozet/diffusion-priors
https://github.com/fraziezr/Machine_Learning_Final_Project_Team_12
A Bayesian Perspective of Statistical Machine Learning for Big Data
https://github.com/freakonometrics/autocalibration
Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning
https://github.com/fredriko/draviz
https://github.com/french-paragon/paperfold-bnn
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
https://github.com/fritz-max/replicating-1607.00133
https://github.com/frobertpixto/hand-drawn-shapes-dataset
https://github.com/fseclab-osaka/eth2vec
https://github.com/ftheberge/GraphMiningNotebooks
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/funkyvoong/band-gaps
Band gap prediction for large organic crystal structures with machine learning
https://github.com/fuweijie/Paper-on-Large-scale-Machine-Learning
https://github.com/fvilmos/multi_task_learning_shape_color_position
https://github.com/gabrieldewetLM/ml-demo
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/galdeia/iirsbenchmark
https://github.com/gauthiercler/adversarial-mnist
https://github.com/gbrltv/business_process_encoding
Characterizing classification datasets: a study of meta-features for meta-learning
https://github.com/geetha2601/Speaker-fluency
Speaker Fluency Level Classification Using Machine Learning Techniques
https://github.com/georgeyiasemis/2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
https://github.com/georgiosdb/PINN-COVID-19
https://github.com/gihants/darknet_built
https://github.com/gingsmith/cocoa
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
https://github.com/gitmehdis/hamiltonian-learning
https://github.com/gmodena/scalagrad
https://github.com/goldblum/AdversarialQuerying
https://github.com/gomboskriszta/yolo
https://github.com/gonepas/stock-predict
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
https://github.com/google-deepmind/lab
https://github.com/google-parfait/federated-compute
https://github.com/google-research/federated
https://github.com/google-research/google-research/tree/master/neural_additive_models
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/google-research/tensorflow_constrained_optimization
Two-Player Games for Efficient Non-Convex Constrained Optimization
https://github.com/google/codex
https://github.com/google/federated-compute
https://github.com/google/jax-cfd
Learning to correct spectral methods for simulating turbulent flows
https://github.com/google/uncertainty-baselines
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.