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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/sylydya/Consistent-Sparse-Deep-Learning-Theory-and-Computation

공식 코드

Consistent Sparse Deep Learning: Theory and Computation

https://github.com/sylydya/sparse-deep-learning-a-new-framework-immuneto-local-traps-and-miscalibration

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Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration

https://github.com/szabogergely0419/ppcu_ifom_yeasttracker

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Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach

https://github.com/t-shinozaki/convcp

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Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning

https://github.com/tangkai-rs/changeanywhere

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ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model

https://github.com/tanyelai/deep-breast-positioning

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Mammographic Breast Positioning Assessment via Deep Learning

https://github.com/taowangzj/awesome-face-restoration

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A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal

https://github.com/tatigabru/kaggle-rsna

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Deep Learning for Automatic Pneumonia Detection

https://github.com/tbeucler/2024_pareto_distillation

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Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications

https://github.com/team-approx-bayes/dl-with-bayes

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Practical Deep Learning with Bayesian Principles

https://github.com/team-approx-bayes/fromp

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Continual Deep Learning by Functional Regularisation of Memorable Past

https://github.com/tech-submissions/physics-aware-downsampling

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Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

https://github.com/tensorchiefs/dl_mlt

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Deep transformation models: Tackling complex regression problems with neural network based transformation models

https://github.com/tensorflow/fold

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Deep Learning with Dynamic Computation Graphs

https://github.com/tensorflow/mesh

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Mesh-TensorFlow: Deep Learning for Supercomputers

https://github.com/tensorflow/models

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Deep Learning with Differential Privacy

https://github.com/tensorflow/tensor2tensor

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Tensor2Tensor for Neural Machine Translation

https://github.com/tenstep/DeepModel

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Model-based Deep Hand Pose Estimation

https://github.com/teptwomey/deep_learning_architectures_for_fscv

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Deep Learning Architectures for FSCV, a Comparison

https://github.com/ternaus/robot-surgery-segmentation

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Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

https://github.com/terryyz/dataaug4code

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Source Code Data Augmentation for Deep Learning: A Survey

https://github.com/testingautomated-usi/uncertainty-wizard

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Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

https://github.com/tfzhou/vs-survey

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A Survey on Deep Learning Technique for Video Segmentation

https://github.com/thaihungle/UW-DNC

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Memory and attention in deep learning

https://github.com/theamrzaki/text_summurization_abstractive_methods

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Amharic Abstractive Text Summarization

https://github.com/theclassictechno/lightfielddatasetgenerator

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A Novel Approach For Generating Customizable Light Field Datasets for Machine Learning

https://github.com/theodore-pku/theorem-1-verification-python

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Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer

https://github.com/theroyakash/AKHCRNet

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AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

https://github.com/thomasehuber/s5-rf

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Scaling Up Resonate-and-Fire Networks for Fast Deep Learning

https://github.com/thomasmbury/dl_discrete_bifurcation

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Predicting discrete-time bifurcations with deep learning

https://github.com/thu-ml/zhusuan

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ZhuSuan: A Library for Bayesian Deep Learning

https://github.com/thudzj/ella

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Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

https://github.com/thulas/dac-label-noise

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Combating Label Noise in Deep Learning Using Abstention

https://github.com/thuml/HashNet

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HashNet: Deep Learning to Hash by Continuation

https://github.com/thuml/Self-Tuning

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Self-Tuning for Data-Efficient Deep Learning

https://github.com/thuml/Transfer-Learning-Library

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Transferability in Deep Learning: A Survey

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