ML Papers Explained

Explanations to key concepts in ML

Full Index

• AlexNet
• BART
• BEiT
• BERT
• ConvMixer
• DenseNet
• DistilBERT
• DiT
• DocFormer
• Donut
• EfficientNet
• ERNIE-Layout
• Fast RCNN
• Faster RCNN
• Feature Pyramid Network
• Focal Loss (RetinaNet)
• InceptionNet
• InceptionNetV2 and InceptionNetV3
• InceptionNetV4 and InceptionResNet
• Layout LM
• Layout LM v2
• Layout LM v3
• Lenet
• LiLT
• Mask RCNN
• Masked Autoencoder
• MobileBERT
• MobileNetV1
• MobileNetV2
• MobileNetV3
• RCNN
• ResNet
• ResNext
• SentenceBERT
• Single Shot MultiBox Detector (SSD)
• StructuralLM
• Swin Transformer
• TableNet
• TinyBERT
• Transformer
• VGG
• Vision Transformer
• Xception
• XLNet

By Category

Convolutional Neural Networks

• Lenet
• AlexNet
• VGG
• InceptionNet
• InceptionNetV2 and InceptionNetV3
• ResNet
• InceptionNetV4 and InceptionResNet
• ResNext
• Xception
• DenseNet
• MobileNetV1
• MobileNetV2
• MobileNetV3
• EfficientNet

Region-based Convolutional Neural Networks

• RCNN
• Fast RCNN
• Faster RCNN
• Mask RCNN

Transformers

• BART
• BERT
• DistilBERT
• MobileBERT
• SentenceBERT
• TinyBERT
• Transformer
• XLNet

Layout Transformers

• DocFormer
• ERNIE-Layout
• Layout LM
• Layout LM v2
• Layout LM v3
• LiLT
• StructuralLM

Document Information Processing

• TableNet
• DiT
• Donut

Vision Transformers

• BEiT
• Masked Autoencoder
• Swin Transformer
• Vision Transformer

Single Stage Object Detectors

• Feature Pyramid Network
• Focal Loss (RetinaNet)
• Single Shot MultiBox Detector (SSD)

Literature Reviewed

• Convolutional Neural Networks
• Layout Transformers
• Region-based Convolutional Neural Networks

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