Machine Learning In Genomes
MIT Summer Research. Supervised by Manolis kellis
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Variational Autoencoder | 1,114 | 3 years ago | n,ull | mit | Python | |||||
| Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow) | ||||||||||
| Variational Autoencoder | 1,049 | 3 years ago | 1 | mit | Python | |||||
| Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow) | ||||||||||
| Disentangling Vae | 668 | a year ago | 7 | other | Python | |||||
| Experiments for understanding disentanglement in VAE latent representations | ||||||||||
| Awesome Vaes | 448 | 3 years ago | ||||||||
| A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models. | ||||||||||
| Numalogic | 156 | 4 months ago | 50 | November 30, 2023 | 47 | apache-2.0 | Python | |||
| Collection of operational time series ML models and tools | ||||||||||
| Vae Lstm For Anomaly Detection | 128 | 3 years ago | 6 | Jupyter Notebook | ||||||
| We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series. | ||||||||||
| Unsupervised_anomaly_detection_brain_mri | 123 | 2 years ago | 1 | gpl-3.0 | Python | |||||
| Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study | ||||||||||
| Tybalt | 107 | 5 years ago | 1 | bsd-3-clause | HTML | |||||
| Training and evaluating a variational autoencoder for pan-cancer gene expression data | ||||||||||
| Variational Sparse Coding | 47 | a year ago | 2 | mit | Jupyter Notebook | |||||
| Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge | ||||||||||
| Ladder Vae Pytorch | 31 | 4 years ago | mit | Python | ||||||
| Ladder Variational Autoencoders (LVAE) in PyTorch | ||||||||||
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