Jan 19
Recursive Neural Networks (RecNNs) redefine how we model language structure — by explicitly leveraging tree representations instead of linear sequences. This allows models to interpret meaning compositionally. Deep understanding yields better performance on syntax-sensitive tasks.
Problem:
Linear language models fail at compositional semantics — e.g., interpreting “old men and women” correctly without grammatical structure.
Solution:
RecNNs recursively merge child node representations following the parse tree, resulting in contextually rich embeddings that preserve hierarchical relationships crucial for accurate language understanding.
🔗 medium.com/betaflow/recursiv…
If you are interested in gaining deep insights into AI and how it can be applied to solving your technical and business problems, follow me.
If you want to learn more about how to apply AI to grow your business as a Tech Entrepreneur join my X community (AI for Entrepreneurs)
x.com/i/communities/19918901…
#NLP #AI #RecursiveNN #DeepLearning #LanguageTech #Entrepreneurship #AIforEntrepreneurs #TechLeadership #AICommunity
3
46
31 Jan 2018
RNNとRecursiveNNのいいところ取りをして、(構造情報を組み込んだ)よりリッチな潜在表現を得る手法であるSPINNのTensorFlow実装が公開(1.5でリリースされたEagerが使われている)。
29 Jan 2018
We just published an implementation of SPINN written with Eager execution, including model definition, tf.data pipeline, checkpointing, #TensorBoard summaries and more!
Check it out ↓ goo.gl/NQyiSr
12
28
13 Dec 2017
RNNとRecursiveNN双方のいいところどりをしてリッチな潜在表現を得る試み。シーケンシャルに処理できるShift-Reduce(入って来た単語をマージするか別のものとして積むか逐次判断する)をRNNで再現している。なおS/Rはパーサーの判断を模倣するよう学習させた別モデルを使用する
arxiv.org/abs/1603.06021
5
27
6 Sep 2017
Published Dynamic Batching for #PyTorch medium.com/@ilblackdragon/py… #MachineLearning #DeepLearning Nice speedups for #RecursiveNN
3
93
228