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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/google-research/google-research

공식 코드

How Good is the Bayes Posterior in Deep Neural Networks Really?

https://github.com/google-research/google-research/tree/master/mentormix

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Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

https://github.com/google-research/google-research/tree/master/scalable_shampoo/jax

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Scalable Second Order Optimization for Deep Learning

https://github.com/google-research/sputnik

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Sparse GPU Kernels for Deep Learning

https://github.com/google-research/tensorflow-coder

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TF-Coder: Program Synthesis for Tensor Manipulations

https://github.com/google/learned_optimization/tree/main/learned_optimization/research/general_lopt

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VeLO: Training Versatile Learned Optimizers by Scaling Up

https://github.com/google/uncertainty-baselines

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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning

https://github.com/googlecreativelab/quickdraw-dataset

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Deep Learning for Free-Hand Sketch: A Survey

https://github.com/gpitsilis/hate-speech

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Detecting Offensive Language in Tweets Using Deep Learning

https://github.com/gradhep/neos

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neos: End-to-End-Optimised Summary Statistics for High Energy Physics

https://github.com/graph-com/lri

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Interpretable Geometric Deep Learning via Learnable Randomness Injection

https://github.com/graphpku/disgnn

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Is Distance Matrix Enough for Geometric Deep Learning?

https://github.com/grasp-lyrl/sloppy

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Does the Data Induce Capacity Control in Deep Learning?

https://github.com/grassknoted/Digital-Image-Forensics

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Digital Image Forensics using Deep Learning

https://github.com/gravins/dynamic_graph_benchmark

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Deep learning for dynamic graphs: models and benchmarks

https://github.com/greydanus/mnist1d

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Scaling Down Deep Learning with MNIST-1D

https://github.com/grigorisg9gr/polynomial_nets

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$Π-$nets: Deep Polynomial Neural Networks

https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation

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CoPE: Conditional image generation using Polynomial Expansions

https://github.com/grigorisg9gr/polynomials-for-augmenting-nns

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Augmenting Deep Classifiers with Polynomial Neural Networks

https://github.com/grill-lab/DL-Hard

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How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

https://github.com/guanyingc/SDPS-Net

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Self-calibrating Deep Photometric Stereo Networks

https://github.com/guanyu0316/Probabilistic-Forecast-Reconciliation-with-DL

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Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization

https://github.com/gugarosa/learnergy

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Learnergy: Energy-based Machine Learners

https://github.com/gugas81/pr-dad

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PR-DAD: Phase Retrieval Using Deep Auto-Decoders

https://github.com/guicho271828/latplan

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Classical Planning in Deep Latent Space

https://github.com/guolanqing/awesome-shadow-removal

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Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey

https://github.com/guotong1988/Automatic-Label-Error-Correction

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Learning From Human Correction

https://github.com/guoxs/xtp-llm

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Towards Explainable Traffic Flow Prediction with Large Language Models

https://github.com/guoyaoshen/k-sapcecolddiffusion

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Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise

https://github.com/gwastro/ml-training-strategies

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Training Strategies for Deep Learning Gravitational-Wave Searches

https://github.com/gylx/GTNRL-Trading

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Multi-Graph Tensor Networks

https://github.com/haczqyf/gcn-data-alignment

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Quantifying the Alignment of Graph and Features in Deep Learning

https://github.com/hadi-ghnd/accidentdetection

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Real-Time Accident Detection in Traffic Surveillance Using Deep Learning

https://github.com/hadi-ghnd/scrap-classification

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Ammunition Component Classification Using Deep Learning

https://github.com/haiphanNJIT/PrivateDeepLearning

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Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

https://github.com/hamzaamrani/Deep-Inertial-Sensory-Clustering

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Unsupervised Deep Learning-based clustering for Human Activity Recognition

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