Forward and inverse design of kirigami via supervised autoencoder

Semi-Supervised Recursive Autoencoder

In this project, we implement the semi-supervised Recursive Autoencoders (RAE), and achieve the result comparable with result in [1] on the Movie Review Polarity dataset1. We achieve 76.08% accuracy, which is slightly lower than [1] ’s result 76.8%, with less vector length. Experiments show that the model can learn sentiment and build reasonable structure from sentence.We find longer word vecto...

Sentiment Analysis Using Semi-Supervised Recursive Autoencoder

The aim of this project was to use semi-supervised recursive autoencoder provided by [2] and classify the english phrases from movie reviews into five sentiment classes; very positive, positive, neutral, negative and very negative by softmax regression classifier.

Variational Autoencoder for Semi-Supervised Text Classification

Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder’s capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) ...

Forward and Inverse

Robotics 3 FORWARD AND INVERSE