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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/agwaBom/towards_moe

Towards Understanding Mixture of Experts in Deep Learning

https://github.com/aiff22/PyNET-PyTorch

Replacing Mobile Camera ISP with a Single Deep Learning Model

https://github.com/aikunyi/filternet

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

https://github.com/aisafety-hkust/backdoor_safety_tuning

Bypassing Backdoor Detection Algorithms in Deep Learning

https://github.com/aisafety-hkust/stable_backdoor_purification

Bypassing Backdoor Detection Algorithms in Deep Learning

https://github.com/ait-aecid/anomaly-detection-log-datasets

Deep Learning for Anomaly Detection in Log Data: A Survey

https://github.com/aivolcano/RecSys_tf2

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep-Demand-Forecasting

Wide & Deep Learning for Recommender Systems

https://github.com/ajinkya933/Image_repo

Deep Learning for Multiple-Image Super-Resolution

https://github.com/akshaymehra24/poisoning_certified_defenses

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

https://github.com/albazahm/reading_analog_clocks_with_xception_cnn_network

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/albertmillan/adversarial-training-pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/albertogaspar/dts

Deep Learning for Time Series Forecasting: The Electric Load Case

https://github.com/alewarne/ExplainSecurityDNNs

Evaluating Explanation Methods for Deep Learning in Security

https://github.com/alewarne/Layerwise-Relevance-Propagation-for-LSTMs

Evaluating Explanation Methods for Deep Learning in Security

https://github.com/alexgkendall/caffe-posenet

Modelling Uncertainty in Deep Learning for Camera Relocalization

https://github.com/alexstein0/neural-auctions

Optimal Auctions through Deep Learning: Advances in Differentiable Economics

https://github.com/alialaradi/DeepGalerkinMethod

DGM: A deep learning algorithm for solving partial differential equations

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/wide_and_deep.py

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/TorchEasyRec

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/easyrec

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/alililia/ms_contrib/tree/main/ascend_xception

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/alililia/ms_contrib/tree/main/gpu_xception

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/aliosia/DeepPrivInf2017

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

https://github.com/alison-carrera/onn

Online Deep Learning: Learning Deep Neural Networks on the Fly

https://github.com/aliutkus/speechmetrics

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/alsoj/Recommenders-movielens

Wide & Deep Learning for Recommender Systems

https://github.com/amaleki2/graph_networks

Relational inductive biases, deep learning, and graph networks

https://github.com/ambareeshsrja16/Surgical_Tool_Tracking

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction

https://github.com/amerch/CIFAR100-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/amogh7joshi/engagement-detection

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/amogh7joshi/fer

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/amoudgl/celo

VeLO: Training Versatile Learned Optimizers by Scaling Up

https://github.com/amrutn/Information-in-Language

Deep Learning and the Information Bottleneck Principle

https://github.com/ananyajana/tooth_robustness

A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans

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