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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/lucylow/Covid_Control

Evolutionary Neural AutoML for Deep Learning

https://github.com/ludwig-ai/ludwig

Ludwig: a type-based declarative deep learning toolbox

https://github.com/luizgh/adversarial_signatures

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/lundalana/Bayesian-ID-Comparison

PyTorch: An Imperative Style, High-Performance Deep Learning Library

https://github.com/luo-lorry/cti

TorchCP: A Python Library for Conformal Prediction

https://github.com/luoxd1996/wsl4mis

Mumford-Shah Loss Functional for Image Segmentation with Deep Learning

https://github.com/lzccccc/3d-bounding-box-estimation-for-autonomous-driving

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/lzhengchun/TomoGAN

Deep Learning Accelerated Light Source Experiments

https://github.com/m-parchami/3DVehicleDetection

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/machine-reasoning-ufrgs/GNN-GCP

Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems

https://github.com/machine-reasoning-ufrgs/graph-neural-networks

Relational inductive biases, deep learning, and graph networks

https://github.com/machine-reasoning-ufrgs/typed-graph-network

Relational inductive biases, deep learning, and graph networks

https://github.com/madehong/Seq2Seq4ATE

Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

https://github.com/magahub/songrnn

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/magister-informatica-uach/INFO320

Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

https://github.com/magister-informatica-uach/INFO3XX

Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

https://github.com/mahmoodlab/DeepLSR

DeepLSR: a deep learning approach for laser speckle reduction

https://github.com/mahmoodlab/HistoFL

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

https://github.com/mainguyenanhvu/DeepSurf

DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

https://github.com/makslevental/openhls

BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

https://github.com/marbleton/FPGA_MNIST

A guide to convolution arithmetic for deep learning

https://github.com/marcobellagente93/Bayesian_Regression

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/marcoforte/fba_matting

Getting to 99% Accuracy in Interactive Segmentation

https://github.com/mariel-pettee/choreography

Generative Choreography using Deep Learning

https://github.com/markWJJ/horovod

Horovod: fast and easy distributed deep learning in TensorFlow

https://github.com/markloyman/LungNoduleRetrieval

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/marload/ConvNets-TensorFlow2

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/marpogaus/tensorflow-probability-bernstein-polynomial-bijector

Deep transformation models: Tackling complex regression problems with neural network based transformation models

https://github.com/martin-gorner/tensorflow-rnn-shakespeare

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/martius-lab/blackbox-backprop

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

https://github.com/martius-lab/l4-optimizer

L4: Practical loss-based stepsize adaptation for deep learning

https://github.com/massquantity/LibRecommender

Wide & Deep Learning for Recommender Systems

https://github.com/matanbt/attack-tabular

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/matciotola/hyperspectral_pansharpening_toolbox

Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity

https://github.com/materialsvirtuallab/matgl

Relational inductive biases, deep learning, and graph networks

https://github.com/materialsvirtuallab/megnet

Relational inductive biases, deep learning, and graph networks

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