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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/fs302/EasyLink

Link Prediction Based on Graph Neural Networks

https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py

Link Prediction Based on Graph Neural Networks

https://github.com/fteufel/PyTorch-GRU-D

Recurrent Neural Networks for Multivariate Time Series with Missing Values

https://github.com/fulifeng/Temporal_Relational_Stock_Ranking

Temporal Relational Ranking for Stock Prediction

https://github.com/galdeia/iirsbenchmark

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

https://github.com/galkampel/HyperNetworks

Molecule Property Prediction and Classification with Graph Hypernetworks

https://github.com/ganwanshui/simpleoccupancy

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

https://github.com/garroud/human-motion-prediction-pytorch

On human motion prediction using recurrent neural networks

https://github.com/gatheluck/pytorch-selectivenet

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset

On human motion prediction using recurrent neural networks

https://github.com/gcucurull/visual-compatibility

Context-Aware Visual Compatibility Prediction

https://github.com/geffy/tffm

Higher-Order Factorization Machines

https://github.com/gentlemanman/fcrn_pytorch

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/geohot/twitchcoq

Value Prediction Network

https://github.com/georgeyiasemis/FCRN-PyTorch

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/gewu-lab/ogm-ge_cvpr2022

IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling

https://github.com/gioelece/cppconformal

Conformal Prediction: a Unified Review of Theory and New Challenges

https://github.com/gleefe1995/GCNv2

GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

https://github.com/gmum/Zero-Time-Waste

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking

https://github.com/google-parfait/tensorflow-federated

Federated Learning for Mobile Keyboard Prediction

https://github.com/google-research/sparf

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/google/burst-denoising

Burst Denoising with Kernel Prediction Networks

https://github.com/gradio-app/saliency

Contextual Encoder-Decoder Network for Visual Saliency Prediction

https://github.com/graph-star-team/graph_star

Graph Star Net for Generalized Multi-Task Learning

https://github.com/gregor160300/federated

Federated Learning for Mobile Keyboard Prediction

https://github.com/gurkirt/3D-RetinaNet

Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

https://github.com/gurkirt/AMTNet

Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

https://github.com/gychant/CSKMTermDefn

KG-BERT: BERT for Knowledge Graph Completion

https://github.com/h3lio5/linguistic-style-transfer-pytorch

Disentangled Representation Learning for Non-Parallel Text Style Transfer

https://github.com/hansbambel/multidim_conv

Wind speed prediction using multidimensional convolutional neural networks

https://github.com/hansongfang/CompNet

Compositionally Generalizable 3D Structure Prediction

https://github.com/harshayugirase/human-path-prediction

It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction

https://github.com/harshmaru7/DilatedConv

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/havrylovv/isegprobe

FeatUp: A Model-Agnostic Framework for Features at Any Resolution

https://github.com/henry1iu/tnt-trajectory-prediction

TNT: Target-driveN Trajectory Prediction

https://github.com/henry1iu/tnt-trajectory-predition

TNT: Target-driveN Trajectory Prediction

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