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Deep Learning

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

https://github.com/NIPC-DL/covec

SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

https://github.com/NIPC-DL/torchplp

SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

https://github.com/NPilis/Facial-Expression-Recognition

Facial Expression Recognition with Deep Learning

https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep

Wide & Deep Learning for Recommender Systems

https://github.com/NVIDIA/GraphQSat

Relational inductive biases, deep learning, and graph networks

https://github.com/NVlabs/sionna/blob/main/examples/Weighted_BP_Algorithm.ipynb

Learning to Decode Linear Codes Using Deep Learning

https://github.com/Nahid1992/CAD_PE

Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection

https://github.com/NajibYavari/DD2412

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/NaldoHoracio/CornealAIPaper

Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

https://github.com/NasimISU/OptSLA

Deep learning from crowds

https://github.com/NervanaSystems/ngraph-python

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

https://github.com/NeuroAI-HD/HD-GLIO

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/NeuroAI-HD/HD-GLIO-AUTO

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/NihadHassan999/DeepIris_Recognition

DeepIris: Iris Recognition Using A Deep Learning Approach

https://github.com/Nixtla/neuralforecast

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

https://github.com/NorthPolesky/GNNpaper

Deep Learning on Graphs: A Survey

https://github.com/NovaisGabriel/CNN_RNN_for_NLP

Notes on Deep Learning for NLP

https://github.com/P2333/Max-Mahalanobis-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/PENGUINLIONG/addernet-pytorch

AdderNet: Do We Really Need Multiplications in Deep Learning?

https://github.com/PaddlePaddle/PaddleClas

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/PaddlePaddle/PaddleOCR

LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dien

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dlrm

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dsin

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/wide_deep

Wide & Deep Learning for Recommender Systems

https://github.com/PaddlePaddle/PaddleScience

Neural networks for topology optimization

https://github.com/PaddlePaddle/PaddleScience/tree/develop/examples/deephpms

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

https://github.com/Pek20180909/dp-transformer

Tensor2Tensor for Neural Machine Translation

https://github.com/PhIMaL/DeePyMoD_torch

DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/PhysicsTeacher13/Deepxde

DeepXDE: A deep learning library for solving differential equations

https://github.com/Plemeur/DGM

DGM: A deep learning algorithm for solving partial differential equations

https://github.com/Plemeur/pre_stage

DGM: A deep learning algorithm for solving partial differential equations

https://github.com/Pradyumn-Patil/updated-research-paper-links-

Video Content Classification using Deep Learning

https://github.com/Przemo23/Reversing_Gradient_Differentiation_NN

Efficient Computation of Hessian Matrices in TensorFlow

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