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Prediction

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코드

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

https://github.com/liuruiyang98/Jittor-MLP

CycleMLP: A MLP-like Architecture for Dense Prediction

https://github.com/liyichen-cly/MMEA

MMKG: Multi-Modal Knowledge Graphs

https://github.com/liyichen-cly/MSNEA

MMKG: Multi-Modal Knowledge Graphs

https://github.com/lucianacendon/simile

Smooth Imitation Learning for Online Sequence Prediction

https://github.com/luoyan407/n-reference

$n$-Reference Transfer Learning for Saliency Prediction

https://github.com/lyqcom/sgcn

SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

https://github.com/ma921/XRDidentifier

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units

https://github.com/maharshi95/Pose2vec

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gae

Variational Graph Auto-Encoders

https://github.com/mandiehyewon/TPAMTL

Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

https://github.com/manshri/tesum

A Deep Reinforced Model for Abstractive Summarization

https://github.com/marcotcr/lime

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

https://github.com/maroxtn/SAINT-Transformer-riiid-kaggle

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

https://github.com/martin-danelljan/ECO

Learning Discriminative Model Prediction for Tracking

https://github.com/martinwhl/T-GCN-PyTorch

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

https://github.com/massquantity/LibRecommender

Deep Interest Network for Click-Through Rate Prediction

https://github.com/massquantity/dismember

Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

https://github.com/matinhosseiny/Inception-inspired-LSTM-for-Video-frame-Prediction

Inception-inspired LSTM for Next-frame Video Prediction

https://github.com/matinmoezzi/ups_conformal_classification

Uncertainty Sets for Image Classifiers using Conformal Prediction

https://github.com/mbarnes1/B3

On the Interaction Effects Between Prediction and Clustering

https://github.com/mcg-nju/sparsebev

Fully Sparse 3D Occupancy Prediction

https://github.com/mcvidomi/MFI

Feature Importance Measure for Non-linear Learning Algorithms

https://github.com/microsoft/ar2

ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

https://github.com/microsoft/strategically_efficient_rl

Curiosity-driven Exploration by Self-supervised Prediction

https://github.com/migalkin/NodePiece

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/migalkin/NodePiece/tree/main/ogb

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/mikudehuane/Configurable-Tensorflow-DIN

Deep Interest Network for Click-Through Rate Prediction

https://github.com/mindspore-ai/models/blob/master/research/cv/tgcn/

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

https://github.com/mindspore-ai/models/tree/master/research/recommend/DIEN

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/mks0601/V2V-PoseNet_RELEASE

Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals

https://github.com/mlfoundations/Evalchemy

CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

https://github.com/mniepert/mmkb

MMKG: Multi-Modal Knowledge Graphs

https://github.com/mohsaad/Deeper-Depth-Prediction

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/mohsenzand/reschunk

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

https://github.com/morpheu513/DA_project

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

https://github.com/morpheu513/NASDAQ_stock_analysis

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

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