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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/amazon-research/long-short-term-transformer

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://github.com/amazon-science/intent-aware-encoder

Benchmarking Natural Language Understanding Services for building Conversational Agents

https://github.com/amazon-science/long-short-term-transformer

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://github.com/ambityga/memsem

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ambujojha/SemiSupervisedLearning

Temporal Ensembling for Semi-Supervised Learning

https://github.com/amdegroot/ssd.pytorch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/aminekha/AI-For-2022

YOLO9000: Better, Faster, Stronger

https://github.com/amiratag/multiaccuracyboost

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

https://github.com/amirhfarzaneh/lsoftmax-pytorch

Large-Margin Softmax Loss for Convolutional Neural Networks

https://github.com/ammesatyajit/VideoBERT

VideoBERT: A Joint Model for Video and Language Representation Learning

https://github.com/amobiny/Fast_CapsNet

Fast CapsNet for Lung Cancer Screening

https://github.com/amogh7joshi/engagement-detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amogh7joshi/fer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amritasaha1812/pytorch-faster-rcnn

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/amurthy1/dagan

Sampling Generative Networks

https://github.com/amymariaparker2401/new

Bag of Tricks for Efficient Text Classification

https://github.com/analogdevicesinc/ai8x-training

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/anantgupta129/CatBoost-in-Python-ML

CatBoost: unbiased boosting with categorical features

https://github.com/anas-r-dev/video-mix-tensorflow

VideoMix: Rethinking Data Augmentation for Video Classification

https://github.com/anastasialavrova/bmstu_bachelor_qualification_work

A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine

https://github.com/anbai106/mlni

Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease

https://github.com/andersoncarlosfs/image_classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/andi611/Mockingjay-Speech-Representation

An Unsupervised Autoregressive Model for Speech Representation Learning

https://github.com/andre-juan/good_quantum_kernels

Supervised learning with quantum enhanced feature spaces

https://github.com/andreapdr/gfun

Word-Class Embeddings for Multiclass Text Classification

https://github.com/andreblumenau/Contemporary-Neural-Network

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

https://github.com/andresmanzalini/ClasificacionGeneroMusical_DL

Convolutional Recurrent Neural Networks for Music Classification

https://github.com/andrewbespaly/canvas_character_recognition

EMNIST: an extension of MNIST to handwritten letters

https://github.com/andrewbo29/mtm-meta-learning-sa

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/andrewfwalters/w266-final-project

Convolutional Neural Networks for Sentence Classification

https://github.com/andyjzhao/wsdm23-gsr

Data Augmentation for Graph Neural Networks

https://github.com/andyweizhao/NLP-Capsule

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

https://github.com/andyweizhao/capsule

Investigating Capsule Networks with Dynamic Routing for Text Classification

https://github.com/anglihust/Unsupervised-vessel-segmentation

Adversarial Discriminative Domain Adaptation

https://github.com/anguyen8/deepface-emd

DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

https://github.com/anguyen8/face-vit

DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

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