|
Transformers for Vision
|
|
0
|
2025
|
August 14, 2023
|
|
The Transformer Architecture
|
|
0
|
2015
|
August 14, 2023
|
|
Self-Attention and Positional Encoding
|
|
0
|
2039
|
August 14, 2023
|
|
Multi-Head Attention
|
|
0
|
1984
|
August 14, 2023
|
|
The Bahdanau Attention Mechanism
|
|
0
|
1858
|
August 14, 2023
|
|
Attention Scoring Functions
|
|
0
|
1411
|
August 14, 2023
|
|
Attention Pooling by Similarity
|
|
0
|
1706
|
August 14, 2023
|
|
Queries, Keys, and Values
|
|
0
|
2099
|
August 14, 2023
|
|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
1492
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
1941
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
1421
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
1476
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
1814
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
1648
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
|
|
0
|
2013
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
|
|
0
|
1384
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
2057
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
860
|
August 14, 2023
|
|
Language Models
|
|
0
|
1508
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
|
|
0
|
1498
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
2046
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
1488
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
1470
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
1983
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
1707
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
1857
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
1562
|
August 14, 2023
|
|
Networks Using Blocks (VGG)
|
|
0
|
1914
|
August 14, 2023
|
|
Deep Convolutional Neural Networks (AlexNet)
|
|
0
|
1429
|
August 14, 2023
|
|
Convolutional Neural Networks (LeNet)
|
|
0
|
2179
|
August 14, 2023
|