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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/matthew-mcateer/Keras_pruning

Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning

https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/mayank5695/Differential-Privacy

Deep Learning with Differential Privacy

https://github.com/mdsufz/PredicTF

Blocks and Fuel: Frameworks for deep learning

https://github.com/mdv3101/Rethinking_Generalization

Understanding deep learning requires rethinking generalization

https://github.com/meabhishekkumar/iith_session_2020

Wide & Deep Learning for Recommender Systems

https://github.com/mediteamC/teamC

Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

https://github.com/mekty2012/CS470_SymbolicIntegration

Deep Learning for Symbolic Mathematics

https://github.com/messi-q/rechecker

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

https://github.com/meyresearch/online_vampnets

Online Deep Learning: Learning Deep Neural Networks on the Fly

https://github.com/mfayk/cu_icar_image_captioning

Deep Learning for Video Classification and Captioning

https://github.com/miccai2022-155/autoseg

One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

https://github.com/michelle0924hhx/DiffPrivate_FedLearning

Deep Learning with Differential Privacy

https://github.com/microsoft/MWSS

Learning to Reweight Examples for Robust Deep Learning

https://github.com/microsoft/distance-learner

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/microsoft/recommenders

Wide & Deep Learning for Recommender Systems

https://github.com/mikel-brostrom/Deep_Spammer_Detection_GCN

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

https://github.com/mikgroup/ufloss

MoDL: Model Based Deep Learning Architecture for Inverse Problems

https://github.com/mila-iqia/blocks

Blocks and Fuel: Frameworks for deep learning

https://github.com/mila-iqia/fuel

Blocks and Fuel: Frameworks for deep learning

https://github.com/mila-udem/blocks

Blocks and Fuel: Frameworks for deep learning

https://github.com/mila-udem/fuel

Blocks and Fuel: Frameworks for deep learning

https://github.com/mila-udem/platoon

Deep learning with Elastic Averaging SGD

https://github.com/milutter/deep_lagrangian_networks

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

https://github.com/mindspore-ai/models/tree/master/official/cv/xception

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/mindspore-ai/models/tree/master/official/recommend/wide_and_deep

Wide & Deep Learning for Recommender Systems

https://github.com/mindspore-ai/models/tree/master/research/hpc/deepbsde

Solving high-dimensional partial differential equations using deep learning

https://github.com/mindspore-ai/models/tree/master/research/hpc/molecular_dynamics

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

https://github.com/mindspore-ai/models/tree/master/research/recommend/DIEN

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/mindspore-ai/models/tree/master/research/recommend/dlrm

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/xception.py

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/mink007/Emotion_Recog_Deep_Neural_Networks/blob/master/CS231N_Project_Presentation.pptx

Emotion Recognition in Audio and Video Using Deep Neural Networks

https://github.com/mjhwright/error-correction

DGM: A deep learning algorithm for solving partial differential equations

https://github.com/mjohn5/dnn_laplace_arctan_regularization

Regularized deep learning with nonconvex penalties

https://github.com/mkskeller/mnist-mpc

Secure Quantized Training for Deep Learning

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