Graph Attention Networks (https://arxiv.org/abs/1710.10903)
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Apr 12, 2020 - Python
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self-attention
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Datasets, tools, and benchmarks for representation learning of code.
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machine-learning-on-source-code
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Jul 13, 2020 - Jupyter Notebook
Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN
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text-classification
question-answering
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language-model
textcnn
attention-is-all-you-need
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pre-training
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Jan 1, 2019 - Python
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).
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Jul 10, 2020 - Python
Text classification using deep learning models in Pytorch
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Nov 17, 2018 - Python
A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.
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May 7, 2020 - Python
A Structured Self-attentive Sentence Embedding
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Sep 22, 2019 - Python
Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP
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Mar 11, 2020 - Jupyter Notebook
An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.
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self-attention
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Dec 27, 2018 - Python
Implementing Stand-Alone Self-Attention in Vision Models using Pytorch
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Feb 13, 2020 - Python
Tensorflow implementation of "A Structured Self-Attentive Sentence Embedding"
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Jan 28, 2020 - Python
Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)
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attention-visualization
attention-scores
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May 4, 2018 - Vue
A Pytorch Implementation of "Attention is All You Need" and "Weighted Transformer Network for Machine Translation"
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Dec 10, 2018 - Python
Transformer for Graph Classification (in Pytorch and Tensorflow)
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graph-transformer
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Jul 5, 2020 - Python
A variant of the Self Attention GAN named: FAGAN (Full Attention GAN)
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Nov 21, 2018 - Python
Simple Tensorflow Implementation of "A Structured Self-attentive Sentence Embedding" (ICLR 2017)
text-classification
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Jun 25, 2018 - Python
Collection of important Question-Answering papers implemented in PyTorch
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Jun 25, 2020 - Jupyter Notebook
The implementation of DeBERTa
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Jul 8, 2020 - Python
Multi-turn dialogue baselines written in PyTorch
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Mar 10, 2020 - Python
Representation learning on dynamic graphs using self-attention networks
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Feb 11, 2020 - Python
Implementation from the paper Attention Augmented Convolutional Networks in Tensorflow (https://arxiv.org/pdf/1904.09925v1.pdf)
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May 5, 2019 - Python
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Apr 15, 2019 - Python
Code for Describing a Knowledge Base
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Feb 12, 2020 - Python
Sequence to Sequence Models in PyTorch
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Jul 14, 2020 - Python
PyTorch implementation of the model presented in "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention"
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Mar 30, 2020 - Python
Transformer-based Relational Memory for Knowledge Graph Embeddings (ACL 2020) (in Pytorch and Tensorflow)
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Jul 8, 2020 - Python
Chatbot using Tensorflow (Model is transformer) ko
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Dec 10, 2018 - Python
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In official documents, there is a notice 'Each function object is meant to be used only once (in the forward pass).' in subclass of torch.autograd.Function.
In model SpGraphAttentionLayer, you have use the object of SpecialSpmmFunction(self.special_spmm) twice, one for e_rowsum and one for h_prime.
Is it the right usage for subclass of torch.autograd.Function?