Papers with Code 아카이브

Deep Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/facebookresearch/pytorch3d

Accelerating 3D Deep Learning with PyTorch3D

https://github.com/facebookresearch/stochastic_gradient_push

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/fanbyprinciple/Machine-Learning-For-Cyber-Security

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

https://github.com/fanconic/this-does-not-look-like-that

This Looks Like That: Deep Learning for Interpretable Image Recognition

https://github.com/faroukmokhtar/ImageSegmentationPASCAL

Image Segmentation Using Deep Learning: A Survey

https://github.com/fastai/nbdev

fastai: A Layered API for Deep Learning

https://github.com/fastforwardlabs/deepad

Deep Learning for Anomaly Detection: A Survey

https://github.com/fatLime/Predict-Lung-Disease

Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

https://github.com/felipeescallon/layout_parser

LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

https://github.com/fengtong-xiao/DMBGN

Wide & Deep Learning for Recommender Systems

https://github.com/ferencberes/twittertennis

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

https://github.com/filipkro/tsc

Deep learning for time series classification: a review

https://github.com/filippogiruzzi/voice_activity_detection

Deep learning for time series classification: a review

https://github.com/fin-young/THEO_POULA

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks

https://github.com/fineline179/MEHTA_project

An exact mapping between the Variational Renormalization Group and Deep Learning

https://github.com/fineline179/MEHTA_project_2

An exact mapping between the Variational Renormalization Group and Deep Learning

https://github.com/firojalam/multimodal_social_media

Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response

https://github.com/flaviagiammarino/inception-time-pytorch

InceptionTime: Finding AlexNet for Time Series Classification

https://github.com/flemesre/PHYS-449-final-project

Deep learning insights into cosmological structure formation

https://github.com/flexi-framework/tffb

Deploying deep learning in OpenFOAM with TensorFlow

https://github.com/floraxhuang/Movie-Recommendation-System

Wide & Deep Learning for Recommender Systems

https://github.com/foolpanda/deepFC

Deep Learning: An Introduction for Applied Mathematicians

https://github.com/forin-xyz/Keras-HSIC-Bottleneck

The HSIC Bottleneck: Deep Learning without Back-Propagation

https://github.com/formal-verification-research/NJSMA

The Limitations of Deep Learning in Adversarial Settings

https://github.com/foxycharm/E-MSA

Maximum Principle Based Algorithms for Deep Learning

https://github.com/fpour/tgb_baselines

Temporal Graph Networks for Deep Learning on Dynamic Graphs

https://github.com/fr3fou/gone

The Matrix Calculus You Need For Deep Learning

https://github.com/frankhan91/DeepBSDE

Solving high-dimensional partial differential equations using deep learning

https://github.com/fraunhofer-aisec/shortcut-detection-vae

Salient ImageNet: How to discover spurious features in Deep Learning?

https://github.com/french-paragon/paperfold-bnn

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

https://github.com/frgfm/Holocron

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/friendsqia/friends_qia

Deep learning from crowds

https://github.com/fritz-max/replicating-1607.00133

Deep Learning with Differential Privacy

https://github.com/fseiffarth/gnn-comparison

A Gentle Introduction to Deep Learning for Graphs

https://github.com/ftramer/Handcrafted-DP

Deep Learning with Differential Privacy

https://github.com/fuenwang/3D-BoundingBox

3D Bounding Box Estimation Using Deep Learning and Geometry

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