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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/pra-dan/Worth-Reading-ML-Papers

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/pranshupant/SR-DNS_Net

Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data

https://github.com/prashanth-prakash/Autoencode-using-weight-mirrors

Deep Learning without Weight Transport

https://github.com/pratikkakkar/deep-diff

DeepXDE: A deep learning library for solving differential equations

https://github.com/preetum/cifar5m

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

https://github.com/priyeshshukla/graph_nets_ids576

Relational inductive biases, deep learning, and graph networks

https://github.com/probabilistic-and-interactive-ml/breaking-the-reclustering-barrier

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

https://github.com/pskiers/symbotunes

Music transcription modelling and composition using deep learning

https://github.com/pui-nantheera/volcano_deform_detection

Detecting Volcano Deformation in InSAR using Deep learning

https://github.com/puneethshankar/slum-segmentation

Slum Segmentation and Change Detection : A Deep Learning Approach

https://github.com/purine/purine2

Purine: A bi-graph based deep learning framework

https://github.com/pushpendughosh/Stock-market-forecasting

Neural Paraphrase Generation with Stacked Residual LSTM Networks

https://github.com/puzzlef/graph-csr-openmp

Optimizing Memory-Access Patterns for Deep Learning Accelerators

https://github.com/pvnieo/SURFMNet-pytorch

Unsupervised Deep Learning for Structured Shape Matching

https://github.com/pwc-1/Paper-9/tree/main/3/AGW

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

https://github.com/pwc-1/Paper-9/tree/main/3/smu

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

https://github.com/pykt-team/pykt-toolkit

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

https://github.com/pytorch/ao

FP8 Formats for Deep Learning

https://github.com/pytorchbearer/torchbearer

Torchbearer: A Model Fitting Library for PyTorch

https://github.com/q3erf/topo_bbgm

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

https://github.com/qijimrc/tensor2tensor

Tensor2Tensor for Neural Machine Translation

https://github.com/qin67/Multistep-Time-Series-DO-Case

Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction

https://github.com/qinzheng93/geotransformer

RPM-Net: Robust Point Matching using Learned Features

https://github.com/qiyuantian/DeepDTI

SRDTI: Deep learning-based super-resolution for diffusion tensor MRI

https://github.com/qmonmous/BigData-X-Python

Wide & Deep Learning for Recommender Systems

https://github.com/qmonmous/pySpark-X-RecommenderSys--intro

Wide & Deep Learning for Recommender Systems

https://github.com/radhen/BebionicFingerSensor

Real-time regression analysis with deep convolutional neural networks

https://github.com/rajkundu/myoband

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

https://github.com/ramsesproject/TomoGAN

Deep Learning Accelerated Light Source Experiments

https://github.com/ranandalon/mtl

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

https://github.com/raphaelBarman/dhSegment

dhSegment: A generic deep-learning approach for document segmentation

https://github.com/raphaelavalos/attention_tsp_graph_net

Relational inductive biases, deep learning, and graph networks

https://github.com/ravikt/sceneednet

SceneEDNet: A Deep Learning Approach for Scene Flow Estimation

https://github.com/ravikucb/dnas

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/rdadolf/fathom

Fathom: Reference Workloads for Modern Deep Learning Methods

https://github.com/rdcep/clouds

Cloud Classification with Unsupervised Deep Learning

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