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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/mhassandev/ShoeNet

공식 코드

Deep Learning Analysis and Age Prediction from Shoeprints

https://github.com/mia1q/code-clone-dl-frameworks

공식 코드

Unraveling Code Clone Dynamics in Deep Learning Frameworks

https://github.com/michaelshiyu/kerNET

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Modularizing Deep Learning via Pairwise Learning With Kernels

https://github.com/michaelthoreau/SearchAndRescueNet

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SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery

https://github.com/micheledelliveneri/almasim

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3D Detection and Characterisation of ALMA Sources through Deep Learning

https://github.com/micheledelliveneri/deepfocus

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3D Detection and Characterisation of ALMA Sources through Deep Learning

https://github.com/microsoft/ContextualSP

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Does Deep Learning Learn to Abstract? A Systematic Probing Framework

https://github.com/microsoft/cameratraps

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Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation

https://github.com/microsoft/microxcaling

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Microscaling Data Formats for Deep Learning

https://github.com/microsoft/torchgeo

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TorchGeo: Deep Learning With Geospatial Data

https://github.com/mikefairbank/dlts_paper_code

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Deep Learning in Target Space

https://github.com/miketheologitis/FedL-Sync-FDA

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Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging

https://github.com/mil-tokyo/distmljs

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DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers

https://github.com/mila-iqia/milabench

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Introducing Milabench: Benchmarking Accelerators for AI

https://github.com/milancurcic/neural-fortran-paper

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A parallel Fortran framework for neural networks and deep learning

https://github.com/mileswyn/ich_mllms_validation

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Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

https://github.com/mili7522/ChromAlignNet

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Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

https://github.com/milindmrao/nlp_comm

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Deep Learning for Joint Source-Channel Coding of Text

https://github.com/mingxuan-he/nft-pred

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Deep Learning for Dynamic NFT Valuation

https://github.com/minnervva/correctness

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Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

https://github.com/minoriwww/MeterDetection

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Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study

https://github.com/minseon-gwak/speech-enhancement-zoom-phone

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Speech Enhancement for Virtual Meetings on Cellular Networks

https://github.com/minyoungkim21/bayesdll

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BayesDLL: Bayesian Deep Learning Library

https://github.com/mipt-oulu/oaprogressionmr

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Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

https://github.com/mir-group/nequip

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Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

https://github.com/mirfaridmusavian/deepcsp

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Graph Convolutional Neural Networks for analysis of EEG signals, BCI application

https://github.com/mit-futuretech/thecomputationallimitsofdeeplearning

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The Computational Limits of Deep Learning

https://github.com/mit-han-lab/mcunet

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Tiny Machine Learning: Progress and Futures

https://github.com/mit-han-lab/pvcnn

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Point-Voxel CNN for Efficient 3D Deep Learning

https://github.com/mit-ll-responsible-ai/equine-webapp

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ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems

https://github.com/mitmedialab/sherlock-project

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Sherlock: A Deep Learning Approach to Semantic Data Type Detection

https://github.com/mitscha/strassennets

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StrassenNets: Deep Learning with a Multiplication Budget

https://github.com/mkhodak/FMRL

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Provable Guarantees for Gradient-Based Meta-Learning

https://github.com/mkirchler/deep-2-sample-test

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Two-sample Testing Using Deep Learning

https://github.com/mkjuergens/epistemicuncertaintyanalysis

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Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

https://github.com/ml-jku/hopular

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Hopular: Modern Hopfield Networks for Tabular Data

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