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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/coleygroup/openretro

Learning Graph Models for Retrosynthesis Prediction

https://github.com/come880412/STSANet

Spatio-Temporal Self-Attention Network for Video Saliency Prediction

https://github.com/continental/kiwissen-bayesian-trajectory-prediction

CoverNet: Multimodal Behavior Prediction using Trajectory Sets

https://github.com/cookbook-ms/learning_on_scs

Principled Simplicial Neural Networks for Trajectory Prediction

https://github.com/coqui-ai/TTS

FastPitch: Parallel Text-to-speech with Pitch Prediction

https://github.com/cosmoquester/2021-dialogue-summary-competition

A Deep Reinforced Model for Abstractive Summarization

https://github.com/coupriec/VideoPredictionICLR2016

Deep multi-scale video prediction beyond mean square error

https://github.com/cran/RFpredInterval

RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

https://github.com/cruvadom/Prediction_Intervals

Calibrated Prediction Intervals for Neural Network Regressors

https://github.com/cschoeller/flomo_motion_prediction

FloMo: Tractable Motion Prediction with Normalizing Flows

https://github.com/cv516Buaa/tph-yolov5

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

https://github.com/cvzoya/visuallydata

Understanding Infographics through Textual and Visual Tag Prediction

https://github.com/d-eremeev/SC-GAN

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

https://github.com/d294270681/ProphetNet-paddle

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

https://github.com/dailab/maxi-xai-lib

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

https://github.com/dalgu90/icd-coding-benchmark

Explainable Prediction of Medical Codes from Clinical Text

https://github.com/danielzgsilva/MonoDepthAttacks

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/davendw49/gakg

SimplE Embedding for Link Prediction in Knowledge Graphs

https://github.com/david-amirault/amhs

Incrementally Improving Graph WaveNet Performance on Traffic Prediction

https://github.com/davidnvq/lwit-alfred

Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks

https://github.com/dayanayuan/raa-kgc

Complex Embeddings for Simple Link Prediction

https://github.com/debajyotid2/noisy_prediction_calibration

From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

https://github.com/deepgraphlearning/esm-gearnet

Protein Representation Learning by Geometric Structure Pretraining

https://github.com/deepmodeling/Uni-Mol

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

https://github.com/deepsuncode/Machine-learning-as-a-service

DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction

https://github.com/deezer/linear_graph_autoencoders

Variational Graph Auto-Encoders

https://github.com/diablordking/TheWolfofWallStreet

Stock trend prediction using news sentiment analysis

https://github.com/dice-group/hybridfc

Convolutional Hypercomplex Embeddings for Link Prediction

https://github.com/dmis-lab/olaph

Large language model validity via enhanced conformal prediction methods

https://github.com/dmlc/dgl/tree/master/examples/pytorch/P-GNN

Position-aware Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal

Link Prediction Based on Graph Neural Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/vgae

Variational Graph Auto-Encoders

https://github.com/dns43/fairness

Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

https://github.com/dougbrion/pytorch-classification-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/drexelai/nsclc-radiogenomics

Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer

https://github.com/dsrestrepo/DF-DM

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

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