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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/Kumar-Tarun/summarization

A Deep Reinforced Model for Abstractive Summarization

https://github.com/Kungbohan/EECSMed

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

https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction

Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction/blob/master/README.md

Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

https://github.com/KurochkinAlexey/IMV_LSTM

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

https://github.com/LakshmiTeja17/NNFL-Project

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

https://github.com/LaurentLava/Lime

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

https://github.com/LeDorean/album_score_prediction

Song Hit Prediction: Predicting Billboard Hits Using Spotify Data

https://github.com/LeiBAI/STG2Seq

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

https://github.com/LeiCaiwsu/LGLP

Line Graph Neural Networks for Link Prediction

https://github.com/LeoSerena/MSThesis

Federated Learning for Mobile Keyboard Prediction

https://github.com/LeonSun0101/CD-SD

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/Li-fAngyU/DSIN_paddle

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/LinJayan/FLEN-Paddle

FLEN: Leveraging Field for Scalable CTR Prediction

https://github.com/Linardos/SalEMA

Simple vs complex temporal recurrences for video saliency prediction

https://github.com/LionQYW/Dien----Deep-Interest-Evolution-Network-for-Click-Through-Rate-Prediction

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/LucaAngioloni/ProteinSecondaryStructure-CNN

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction

https://github.com/LukeBolly/tf-tspn

Conditional Set Generation with Transformers

https://github.com/MIT-Omnipush/video-prediction

Stochastic Adversarial Video Prediction

https://github.com/MK-tech20/CGA2M_plus

Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions

https://github.com/MS-P3/code4/tree/main/HPMR

Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation

https://github.com/MS-P3/code7/tree/main/xlm_prophetnet

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

https://github.com/MachineLearningJournalClub/LearningNLP

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

https://github.com/MacroMayhem/WikiChallenge

Wikipedia Edit Number Prediction based on Temporal Dynamics Only

https://github.com/Mahdidrm/Emotion-Recognition

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

https://github.com/ManasRMohanty/DS5500-capstone

KG-BERT: BERT for Knowledge Graph Completion

https://github.com/ManjunathAdi/Seq2Seq_RNN

Deep and Confident Prediction for Time Series at Uber

https://github.com/MapItAnywhere/MapItAnywhere

Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data

https://github.com/MatildeTristany/DEEP-model-ESANN-2020

Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation

https://github.com/MaxLikesMath/Adventures-In-Reinforcement-Learning

Curiosity-driven Exploration by Self-supervised Prediction

https://github.com/MiG98789/urop-desire

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

https://github.com/Microsoft/fairlearn

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

https://github.com/Microsoft/singleshotpose

Real-Time Seamless Single Shot 6D Object Pose Prediction

https://github.com/MinChen00/UnlearningLeaks

Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

https://github.com/Mind23-2/MindCode-118

Utilizing Self-supervised Representations for MOS Prediction

https://github.com/Mind23-2/MindCode-166

Utilizing Self-supervised Representations for MOS Prediction

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