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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/PaddlePaddle/PaddleRec/tree/master/models/rank/dsin

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleSpeech

FastPitch: Parallel Text-to-speech with Pitch Prediction

https://github.com/Paul-Jacquit/Birds

Recurrent Neural Networks for Multivariate Time Series with Missing Values

https://github.com/PawaritL/BayesianLSTM

Deep and Confident Prediction for Time Series at Uber

https://github.com/Penguinazor/mse.wem.project

Mesh R-CNN

https://github.com/PeterChe1990/GRU-D

Recurrent Neural Networks for Multivariate Time Series with Missing Values

https://github.com/PruneTruong/GLU-Net

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/QustKcz/BioNEV

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

https://github.com/R4h4/AIforSEA_Traffic_Management

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

https://github.com/RLAgent/state-marginal-matching

Curiosity-driven Exploration by Self-supervised Prediction

https://github.com/Rakeshpavan333/oct_dil

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/Remiphilius/PoemesProfonds

DopeLearning: A Computational Approach to Rap Lyrics Generation

https://github.com/RezaCDoobary/DrugDiscovery-Tox21

Toxicity Prediction using Deep Learning

https://github.com/RoboTurk-Platform/roboturk_real_dataset

Stochastic Variational Video Prediction

https://github.com/Robotmurlock/TNT-VectorNet-and-HOME-Trajectory-Forecasting

TNT: Target-driveN Trajectory Prediction

https://github.com/Rose-STL-Lab/Turbulent-Flow-Net

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

https://github.com/SPark9625/Large-Scale-Study-of-Curiosity-Driven-Learning

Large-Scale Study of Curiosity-Driven Learning

https://github.com/Salabh2k2/Awesome-Project-Collection/blob/main/Traffic_flow_Prediction_using_LSTM%2BFasterSTGCN.ipynb

FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting

https://github.com/SeongjuLee/DCENet-PyTorch

Exploring Dynamic Context for Multi-path Trajectory Prediction

https://github.com/SforAiDl/vformer

Vision Transformers for Dense Prediction

https://github.com/Shank2358/GGHL

Multi-Grained Angle Representation for Remote Sensing Object Detection

https://github.com/ShigemichiMatsuzaki/MSPL

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

https://github.com/ShihuaHuang95/FaPN-full

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

https://github.com/ShubhamKumarNigam/NyayaAnumana-and-INLegalLlama

NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

https://github.com/Siddharth-Rout/deepADRnet

Advection Augmented Convolutional Neural Networks

https://github.com/SilviaLauraPintea/DejaVu

Deja Vu: Motion Prediction in Static Images

https://github.com/SirRob1997/Collaborative-Filtering-under-Model-Uncertainty

Predictive Multiplicity in Classification

https://github.com/SirRob1997/collaborative_filtering

Predictive Multiplicity in Classification

https://github.com/SmallNana/GMAN_pytorch

GMAN: A Graph Multi-Attention Network for Traffic Prediction

https://github.com/StanfordVL/STR-PIP

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

https://github.com/StanfordVL/roboturk_real_dataset

Stochastic Variational Video Prediction

https://github.com/StephenBo-China/DIEN

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/StephenBo-China/DIEN-DIN

Deep Interest Network for Click-Through Rate Prediction

https://github.com/StephenBo-China/recommendation_system_sort_model

Deep Interest Network for Click-Through Rate Prediction

https://github.com/StrombergNLP/rustance

Stance Prediction for Russian: Data and Analysis

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