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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/apache/tvm

공식 코드

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

https://github.com/apriyad1/deep-learning-in-admissions

공식 코드

Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach

https://github.com/arabelatso/deepfd

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DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs

https://github.com/aralab-unr/ga-mammograms

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Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

https://github.com/aras62/vision-based-prediction

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Deep Learning for Vision-based Prediction: A Survey

https://github.com/arashrabbani/placentasr

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Resolution enhancement of placenta histological images using deep learning

https://github.com/araujoalexandre/focusstackingdataset

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Towards Real-World Focus Stacking with Deep Learning

https://github.com/aravindsp/CS598_DL_Chest_X_RayClassification

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Multi-Label Chest X-Ray Classification via Deep Learning

https://github.com/architrathore/TopoAct

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TopoAct: Visually Exploring the Shape of Activations in Deep Learning

https://github.com/archon159/selor

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Self-explaining deep models with logic rule reasoning

https://github.com/argonne-lcf/TensorFlowFoam

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Deploying deep learning in OpenFOAM with TensorFlow

https://github.com/arijitthegame/Quantum-Hamiltonians

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Learning Full Configuration Interaction Electron Correlations with Deep Learning

https://github.com/arjung128/mi_detection

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Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

https://github.com/arlindkadra/imn

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Interpretable Mesomorphic Networks for Tabular Data

https://github.com/arobey1/mbrdl

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Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data

https://github.com/artcogsys/decorbp

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Efficient Deep Learning with Decorrelated Backpropagation

https://github.com/aryaxai/dlbacktrace

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DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models

https://github.com/asHauptmann/PAT_CODES

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Deep Learning in Photoacoustic Tomography: Current approaches and future directions

https://github.com/asdvfghg/classbd

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Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise

https://github.com/ashesh6810/DLC_extreme

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Analog forecasting of extreme-causing weather patterns using deep learning

https://github.com/ashesh6810/fourks

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Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution

https://github.com/asheshjain399/RNNexp

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Structural-RNN: Deep Learning on Spatio-Temporal Graphs

https://github.com/ashish1610dhiman/iuq_project

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UQ for Credit Risk Management: A deep evidence regression approach

https://github.com/asif-hanif/vafa

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Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

https://github.com/asmitapoddar/deep-learning-dna-sequences

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Identifying DNA Sequence Motifs Using Deep Learning

https://github.com/aspannaus/dl-interpretability

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Topological Interpretability for Deep-Learning

https://github.com/asperti/sst_reconstruction

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Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion

https://github.com/aspuru-guzik-group/dionysus

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Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

https://github.com/asridi/DML-Calibration-Heston-Model

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Applying Deep Learning to Calibrate Stochastic Volatility Models

https://github.com/ast-fortiss-tum/SMOO

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Targeted Deep Learning System Boundary Testing

https://github.com/astorfi/differentially-private-cgan

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Differentially Private Synthetic Medical Data Generation using Convolutional GANs

https://github.com/asucicilab/explainable-geoai

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Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection

https://github.com/athanath/detect-thermal-runaway

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Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines

https://github.com/atomashevic/face-of-populism

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The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning

https://github.com/attentionseekers/cnntention

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CNNtention: Can CNNs do better with Attention?

https://github.com/attilanagy234/enas-pytorch

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Improving the sample-efficiency of neural architecture search with reinforcement learning

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