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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/vrunm/text-classification-financial-phrase-bank

On Empirical Comparisons of Optimizers for Deep Learning

https://github.com/wangjinzhuo/wearables

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

https://github.com/wangying-ict/FCN

Accelerating Generative Neural Networks on Unmodified Deep Learning Processors -- A Software Approach

https://github.com/wdlctc/horovod_SYS

Horovod: fast and easy distributed deep learning in TensorFlow

https://github.com/wellecks/symbolic_generalization

Deep Learning for Symbolic Mathematics

https://github.com/wenqifan03/CopyAttack

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

https://github.com/west-gates/3DCNN-Vis

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

https://github.com/williamcaicedo/ISeeU2

ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes

https://github.com/willisk/GDH

Post-Hoc Domain Adaptation via Guided Data Homogenization

https://github.com/willislwang/cs152_lab3_boom

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

https://github.com/willsharpless/deepreach

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling

Spatial Deep Learning for Wireless Scheduling

https://github.com/wiseodd/laplace-bayesopt

Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/wittmaan/a2d2

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/wlawt/capsnet-trafficsigns

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

https://github.com/wmz9/ire-algorithm-framework

On the Implicit Bias in Deep-Learning Algorithms

https://github.com/woans0104/sk_project

Automatic salt deposits segmentation: A deep learning approach

https://github.com/wolny/pytorch-3dunet

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

https://github.com/woodyx218/Deep-Learning-with-GDP-Pytorch

Deep Learning with Gaussian Differential Privacy

https://github.com/woodyx218/Deep-Learning-with-GDP-Tensorflow

Deep Learning with Gaussian Differential Privacy

https://github.com/woodyx218/opacus_global_clipping

Deep Learning with Differential Privacy

https://github.com/wwzjer/Semi-supervised-IRR

Semi-supervised Transfer Learning for Image Rain Removal

https://github.com/wyd1502/DGNN

Temporal Graph Networks for Deep Learning on Dynamic Graphs

https://github.com/xcgoner/AISTATS2020-AdaAlter-GluonNLP

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

https://github.com/xianggebenben/dlADMM

ADMM for Efficient Deep Learning with Global Convergence

https://github.com/xiaominglalala/Session_based_Recommendation

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/xiaoqian19940510/text-classification-

Deep Learning Based Text Classification: A Comprehensive Review

https://github.com/xiaoyu258/geoproj

Blind Geometric Distortion Correction on Images Through Deep Learning

https://github.com/xilaili/AOGNet

AOGNets: Compositional Grammatical Architectures for Deep Learning

https://github.com/xingyifei2016/RotLieNet

SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold

https://github.com/xinwangchen/edt

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/xldrx/tictac

TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

https://github.com/xtractopen/meganet.m

Stable Architectures for Deep Neural Networks

https://github.com/xuangu-fang/bayotide

Deep Learning for Multivariate Time Series Imputation: A Survey

https://github.com/xue-pai/FuxiCTR

Wide & Deep Learning for Recommender Systems

https://github.com/xumengwei/MobileDL

A First Look at Deep Learning Apps on Smartphones

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