Tree LSTM implementation in PyTorch
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Updated
Sep 30, 2019 - Python
Tree LSTM implementation in PyTorch
Recursive Neural Networks for PyTorch
Tag-enhanced Tree-Structured NN for discourse relation classification.
Set of PyTorch modules for developing and evaluating different algorithms for embedding trees.
Tree Stack Memory Units
A Tree-LSTM-based dependency tree sentiment labeler
Neural-Network Guided Expression Transformation
This repository provides all the models that we use to solve the Context Independent Claim Detection Argumentation Mining sub-task.
THANOS is a modification in HAN (Hierarchical Attention Network) architecture. Here we use Tree LSTM to obtain the embeddings for each sentence.
An reimplementation of Makoto Miwa and Mohit Bansa. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. http://dx.doi.org/10.18653/v1/P16-1105)
Kistmat-AI is an advanced machine learning model designed to solve a wide range of mathematical problems, from elementary arithmetic to university-level calculus. This project demonstrates the application of curriculum learning in AI, allowing the model to progressively tackle more complex mathematical concepts.
Sentiment analysis using neural BoW and LSTM based models
Head-Lexicalized Bidirectional Tree LSTMs
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