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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/labmlai/annotated_deep_learning_paper_implementations

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/labpresse/biomodelcomparison

Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data

https://github.com/lackel/hierarchical_weighted_scl

HDLTex: Hierarchical Deep Learning for Text Classification

https://github.com/lamantinushka/StructuredCovariance

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/law-ai/summarization

Extractive Summarization using Deep Learning

https://github.com/layel2/MagNet-pytorch

MagNet: a Two-Pronged Defense against Adversarial Examples

https://github.com/layumi/Person_reID_baseline_pytorch

Deep Learning for Person Re-identification: A Survey and Outlook

https://github.com/lazharkhelifi/deeplearning_changedetection_remotesensing_review

Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

https://github.com/lbasora/bayesrul

A Benchmark on Uncertainty Quantification for Deep Learning Prognostics

https://github.com/lcckkkhaha/Suprise-Adequacy-Implementation

Guiding Deep Learning System Testing using Surprise Adequacy

https://github.com/leandromineti/ml-curriculum

The Matrix Calculus You Need For Deep Learning

https://github.com/leandromineti/ml-knowledge-graph

The Matrix Calculus You Need For Deep Learning

https://github.com/learningtitans/data-depth-design

Knowledge Transfer for Melanoma Screening with Deep Learning

https://github.com/learningtitans/isbi2017-part3

Knowledge Transfer for Melanoma Screening with Deep Learning

https://github.com/lehgtrung/egfr-att

Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors

https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/lewj85/e2cnn_experiments

Steerable Partial Differential Operators for Equivariant Neural Networks

https://github.com/lhwcv/self_attention_alignment

Deep model with built-in cross-attention alignment for acoustic echo cancellation

https://github.com/libxsmm/libxsmm

Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures

https://github.com/lifesailor/data-driven-predictive-maintenance

Deep Learning and Its Applications to Machine Health Monitoring: A Survey

https://github.com/lightning-uq-box/lightning-uq-box

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/likyoo/open-cd

TINYCD: A (Not So) Deep Learning Model For Change Detection

https://github.com/linghduoduo/NLP

Deep Learning applied to NLP

https://github.com/linqinghong/Deep-Clustering-Paper

Deep Learning with Nonparametric Clustering

https://github.com/liyuan9988/deepfeatureproxyvariable

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction

https://github.com/ljy-hy/mentormix_pytorch

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

https://github.com/lk18322280259/SMU

SMU: smooth activation function for deep networks using smoothing maximum technique

https://github.com/locuslab/convex_adversarial

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/loes5307/vocaladversary2022

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/lofrienger/onetomany_toolsynseg

Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need

https://github.com/lorenlugosch/neural-min-sum-decoding

Deep Learning Methods for Improved Decoding of Linear Codes

https://github.com/lpw007/Inception_TSC

InceptionTime: Finding AlexNet for Time Series Classification

https://github.com/lucasleesw/PCANet_python_ver

PCANet: A Simple Deep Learning Baseline for Image Classification?

https://github.com/lucidrains/Adan-pytorch

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/lucidrains/tab-transformer-pytorch

Revisiting Deep Learning Models for Tabular Data

https://github.com/lucidrains/tab-transformer-pytorch/tree/main/tab_transformer_pytorch

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

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