作为图神经网络的变压器

TL; DRChaitanya Joshi的文章Transformers are Graph Neural Networks ”的翻译:图表,公式,思想,重要链接。经作者的允许发布。

数据中心的朋友经常问同样的问题:Graph神经网络是个好主意,但是他们有没有真正的成功案例?它们有实际用途吗?



Pinterest, Alibaba Twitter. : Transformer.


(Transformers). NLP- GNN-, , "" , .


(representation learning).


NLP


, "" . () (latent/hidden) - . , . , (error/loss functions).


, (natural language processing; NLP), (recurrent neural networks; RNN) — , . RNN , . , RNN, .


, , RNN ( ) .


RNN NLP. : , , (attention mechanism; attention), . , — , "".


2017 , NLP — — RNN. , , !
, Yannic Kilcher.


. , hi- Sll+1:


hi+1=Attention(Qhi ,Khj ,Vhj),


i.e., hi+1=jSwij(Vhj),


 wij=softmaxj(QhiKhj),


jS, Q,K,V— ( Query, Key Value). , . ! — RNN, .


, :



hihj; jSwij(i,j), softmax j. , hi+1i, hj, wij. .

(Multi-Head Attention)


- --- (dot product attention): . , "" (attention heads) () ( "" ):


hi+1=Concat(head1,,headK)O,


headk=Attention(Qk,hi ,Kk,hj ,Vk,hj),


Qk,,Kk,,Vk,k- "" , O— , hi+1hi.


, "" " ", . .


""


Scale issues and the Feed-forward sub-layer


, - , , : . - (1), - " " , , wij. - (2), , "". hi+1. , (normalization layer).


(2) LayerNorm, . , --- (1).


, , : . , hi+1() , ReLU, , :


hi+1=LN(MLP(LN(hi+1)))


, , , . ! , LayerNorm . — , .
, !

:



"" , NLP- , . , ("") , (residual connections) "" "". .


GNN


NLP.


(GNN) (GCN) . (, , ). , . GNN (propagate) — — .



, , : , GNN, : "" .

GNN hihi, hjjN(i):


hi+1=σ(Uhi+jN(i)(Vhj)),


U,V— GNN, σ— (, , ReLU). —


jN(i), , , / - — , .


, ?


, :



j, , Graph Attention Network (GAT). , — — "" !

— , —


, , — , . GNN, (.. ) (.. ) , .



, GNN . — jN(i), NLP S, jS.


, , , , , , — . GNN-. , GNN, .


?


, , , .


— , NLP?


( , ) : , . TreeLSTM, , , /GNN NLP?



(long-term dependencies)?


: . , , , nGNN n2. - n.


NLP-, . , "" LSH (Locality-Sensitive Hashing) . .


, , GNN. , (Binary Partitioning) " ".



" "?


NLP- , , , . , — , , — - " ".


, , " ".


"" — GNN , ( , ) , GNN ? .



? ?


. , , - "" . , , .


"" GNN, GAT , MoNet (Gaussian kernels) . . "" ?


, GNN (, ) . " " !


- , ? Yann Dauphin (ConvNet). , , !



?


, , , , (learning rate schedule), "" (warmup strategy) (decay settings). , , — , — .


, , , .



DeepMind- , - ? " 16 000 "" (warmup), 500 000 "" (decay), 9 000 ".

, , , : , " " ?


" "?


, , (inductive bias), ?



, : The Illustrated Transformer The Annotated Transformer.


GNN : Arthur Szlam Attention/Memory Networks, GNN . - (position paper) DeepMind, — "" — . , , DGL. seq2seq "" "" GNN.


, , GNN NLP ( HuggingFace: Transformers).


最后,我们最近写了一篇文章,其中我们将转换器应用于具有QuickDraw sketches的数据集一探究竟!


加成


该帖子还被翻译成中文加入他关于redditTwitter的讨论


英语翻译:Anton Alekseev
(人工智能实验室,POMI RAS以V.A. Steklov命名)

宝贵的意见,翻译感谢Denis Kiryanov柯丁 和米哈伊尔·埃夫蒂希耶夫(Mikhail Evtikhiev) aspr_spb

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