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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/avinashbarnwal/Deep-Learning

Revisiting Deep Learning Models for Tabular Data

https://github.com/awasthiabhijeet/Learning-From-Rules

Learning to Reweight Examples for Robust Deep Learning

https://github.com/axbaretto/horovod

Horovod: fast and easy distributed deep learning in TensorFlow

https://github.com/axd465/pam_dataset

Reconstructing undersampled photoacoustic microscopy images using deep learning

https://github.com/axrwl/fttransformer

Revisiting Deep Learning Models for Tabular Data

https://github.com/ayushnawal/Channel-Estimation

Deep Learning-Based Channel Estimation

https://github.com/azshue/boosting_robust

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

https://github.com/b-thi/FNN

Deep Learning with Functional Inputs

https://github.com/b-thi/FuncNN

Deep Learning with Functional Inputs

https://github.com/badrmarani/tsp-attn

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem

https://github.com/balbok0/bayes-nn-qsh

Bayesian Deep Learning on a Quantum Computer

https://github.com/bayesianGirl/Antihydrogen-Detection-by-Deep-Learning

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

https://github.com/bdqnghi/ast-node-encoding

Building Program Vector Representations for Deep Learning

https://github.com/beamlab-hsph/coverage_quantification

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

https://github.com/beauCoker/bayesian_neural_networks

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

https://github.com/benearnthof/TimeGAN

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

https://github.com/benedekrozemberczki/benedekrozemberczki

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

https://github.com/best-of-acrv/toposlam

Visual Odometry Revisited: What Should Be Learnt?

https://github.com/bethgelab/cifar10_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/bhaddow/dev-nematus

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/bharathrajcl/A-Robust-Hybrid-Approach-for-Textual-Document-Classification

A Robust Hybrid Approach for Textual Document Classification

https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets

Enforcing temporal consistency in Deep Learning segmentation of brain MR images

https://github.com/billcai/treepedia_dl_public

Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover

https://github.com/billy-mosse/spiderman

Deep Learning with Topological Signatures

https://github.com/bingcheng45/hnr-extension

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/biolearning-stadius/rdkit

ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning

https://github.com/bird-tao/clcrn

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

https://github.com/black0017/MedicalZooPytorch

NiftyNet: a deep-learning platform for medical imaging

https://github.com/blackmints/3DGCN

Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

https://github.com/bluejurand/Photos-colorization

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/bluelancer/gnnet

Open World Learning Graph Convolution for Latency Estimation in Routing Networks

https://github.com/bluove/__DL-prediction-on-earth-phenology

Deep learning in remote sensing: a review

https://github.com/bmitra-msft/TREC-Deep-Learning-Quick-Start

Overview of the TREC 2019 deep learning track

https://github.com/bonaventuredossou/ece526_course_project

Deep Bayesian Active Learning with Image Data

https://github.com/boyangumn/dcn-new

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

https://github.com/boyellow/adaad

Towards Deep Learning Models Resistant to Adversarial Attacks

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