Representation Learning Basics and Feature Extraction in Text
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Updated
Jun 17, 2021 - Jupyter Notebook
Representation Learning Basics and Feature Extraction in Text
Pytorch implementation of the NLP experiment described in the original contrastive predictive coding paper (2018)
Repository for my MSc thesis on "Scene Representation and Pre-Tagging for Autonomous Systems"
Floral Classifier using Discriminative Feature Learning
Code for reproducing results in Representation Learning in Sequence to Sequence Tasks: Multi-filter Gaussian Mixture Autoencoder.
OhmNet: Representation learning in multi-layer graphs
One-Class Classification Ensembles with Unsupervised Representations to Detect Novelty
Source code for EvalNE, a Python library for evaluating Network Embedding methods. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711022000139
A course on representation learning for syntactic and semantic theory taught at the 2023 LSA Institute at UMass, Amherst.
PyTorch implementation of Combined Reinforcement Learning via Abstract Representations
Recurrent Detect-Infer-Repeat official repository
Source code and supplementary material for DIRAC: Diffusion-Based Representation Learning for Modality-Agnostic Compositionality
C++ implementation of the paper "Word-like n-gram embedding". EMNLP 2018 Workshop on Noisy User-generated Text.
A deep convolutional network made of stacked feature extractors
Single Shot Multi-Box Detect Infer & Repeat implementation in PyTorch
Experimenting with deep learning to implicitly represent images
Disentangled representation of voice data.
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