Tree LSTM implementation in PyTorch

Tensorflow based solution for Assignment-3 (Recursive Neural Nets) from CS224d: Deep learning for Natural Language Processing.

Combining Symbolic and Function Evaluation Expressions In Neural Programs

Recursive Neural Networks for PyTorch

[CVPR'19] Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction

Tree Stack Memory Units

A Tree-LSTM-based dependency tree sentiment labeler

Exploiting Multi-Layer Features Using a CNN-RNN Approach for RGB-D Object Recognition (ECCV 2018 workshops)

When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition (CVIU 2022)

Project of Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature

Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)

hippocampus volume quantification

Sentiment Analysis using Recursive Neural Network

The Deep Learning exercises provided in DataCamp

Ideology Detection in the Indian Mass Media

Character-level RNN for text generation. Trained on Anna Karenina (included in /data folder)

Implementation of Recursive Neural Tensor Network as described in https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf

In this project I generated my own Seinfeld TV scripts using RNNs using part of the Seinfeld dataset of scripts from 9 seasons.