https://github.com/wenwenmin/mbVDiT
공식 코드Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis
Papers with Code 아카이브
Feature Engineering
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Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.
The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/western-oc2-lab/autonomouscyber-automl-based-autonomous-intrusion-detection-system
공식 코드Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
https://github.com/wglab/integer_divsibility
공식 코드Classification of integers based on residue classes via modern deep learning algorithms
https://github.com/winstonll/SynC
공식 코드SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula
https://github.com/wonbinkweon/dce_www2024
공식 코드Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
https://github.com/wooner49/sofia
공식 코드Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers
https://github.com/wq13552463699/real-robot-challenge-2022
공식 코드https://github.com/wtl52656/imputation_benchmark
공식 코드An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
https://github.com/wukevin/babel
공식 코드https://github.com/x-labs-xyz/aaai-25-fin-lstm-emg
공식 코드An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals
https://github.com/xbmxb/structurecharacterization4dd
공식 코드https://github.com/xiangzhang1015/adversarial_seizure_detection
공식 코드Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
https://github.com/xidongbo/dfei
공식 코드https://github.com/xinychen/tensor-learning
공식 코드Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
https://github.com/xinychen/transdim
공식 코드Bayesian Temporal Factorization for Multidimensional Time Series Prediction
https://github.com/xli-2020/mpin
공식 코드https://github.com/xptree/DeepInf
공식 코드https://github.com/xuangu-fang/bayotide
공식 코드BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
https://github.com/xue-pai/FuxiCTR
공식 코드DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/xzhang97666/multimodalmimic
공식 코드Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
https://github.com/y-debug-sys/diffusion-ts
공식 코드Diffusion-TS: Interpretable Diffusion for General Time Series Generation
https://github.com/y-xue/MixMI
공식 코드Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
https://github.com/yagguc/deep_impression
공식 코드https://github.com/yair-schiff/ipnp
공식 코드Semi-Parametric Inducing Point Networks and Neural Processes
https://github.com/yandex-research/tabred
공식 코드TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
https://github.com/yangmeng96/mmvmvae-hippocampal
공식 코드Unity by Diversity: Improved Representation Learning in Multimodal VAEs
https://github.com/yanyachen/xdeepint
공식 코드xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions
https://github.com/yaolu-zjut/dynamic-graphs-construction
공식 코드https://github.com/yaringal/CLGP
공식 코드Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
https://github.com/yeganehfrh/yare-gan
공식 코드https://github.com/yhzhu99/prism
공식 코드https://github.com/yinchangchang/sepsislab
공식 코드SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing
https://github.com/yingtiny/bandgap_prediction_roberta
공식 코드Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction
https://github.com/yixinliu233/simpdm
공식 코드Self-Supervision Improves Diffusion Models for Tabular Data Imputation
https://github.com/yoonkim/neural-qcfg
공식 코드https://github.com/yuchenz/tdp_ranking
공식 코드Neural Ranking Models for Temporal Dependency Structure Parsing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.