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Prediction

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/bi-graph/emgraph

Complex Embeddings for Simple Link Prediction

https://github.com/bigdata-ustc/ekt

EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

https://github.com/birdortyedi/vcnet-blind-image-inpainting

VCNet: A Robust Approach to Blind Image Inpainting

https://github.com/blazecolby/PyTorch-LIME

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://github.com/blzhu0823/pathfusion

MMKG: Multi-Modal Knowledge Graphs

https://github.com/bouracha/Gen_Motion

Learning Trajectory Dependencies for Human Motion Prediction

https://github.com/bowenbaker/metaqnn

Accelerating Neural Architecture Search using Performance Prediction

https://github.com/brightmart/ai_law

CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

https://github.com/brunocampos01/finding-donors

A Statistical Approach to Adult Census Income Level Prediction

https://github.com/bugbug-lou/CVP

Compositional Video Prediction

https://github.com/cassie07/pathomics

Multimodal Multitask Representation Learning for Pathology Biobank Metadata Prediction

https://github.com/castorini/Anserini

Document Expansion by Query Prediction

https://github.com/castorini/docTTTTTquery

Document Expansion by Query Prediction

https://github.com/catid/spectral_ssm

Spectral State Space Models

https://github.com/cbvrp-acmmm-2018/cbvrp-acmmm-2018

Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline

https://github.com/cdt15/lingam

Estimation of interventional effects of features on prediction

https://github.com/chancejohnstone/piRF

Conformalized Quantile Regression

https://github.com/changmin-yu/Prediction_and_Generalisation_over_Directed_Actions_by_Grid_Cells

Prediction and Generalisation over Directed Actions by Grid Cells

https://github.com/chengtan9907/OpenSTL

SimVP: Simpler yet Better Video Prediction

https://github.com/chengtan9907/simvpv2

Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

https://github.com/chenjiyan2001/paddle-FGCNN

BARS-CTR: Open Benchmarking for Click-Through Rate Prediction

https://github.com/chenxiliu-hnu/timecma

Spatial-Temporal Large Language Model for Traffic Prediction

https://github.com/china-ai-law-challenge/CAIL

CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

https://github.com/china-ai-law-challenge/cail2018

CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

https://github.com/chiragsamal/Importance-Sampling-in-Reinforcement-Learning

Importance Resampling for Off-policy Prediction

https://github.com/chriswxho/dynamic-inference

Vision Transformers for Dense Prediction

https://github.com/ciselab/lampion

Adversarial Examples for Models of Code

https://github.com/ck37/coral-ordinal

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/cmasch/cnn-text-classification

Neural Document Embeddings for Intensive Care Patient Mortality Prediction

https://github.com/cocoon2wong/socialcircle

SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction

https://github.com/code-implementation1/Code2/tree/main/ADNet

ADNet: Lane Shape Prediction via Anchor Decomposition

https://github.com/code-implementation1/Code4/tree/main/hlg

Vision Transformers: From Semantic Segmentation to Dense Prediction

https://github.com/code-implementation1/Code9/tree/main/m2n

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

https://github.com/code-implementation1/Code9/tree/main/sgcn

SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

https://github.com/codelion/adaptive-classifier

C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets

https://github.com/cognitivemodeling/finn

Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.