There are no reviews yet. Be the first to send feedback to the community and the maintainers!
dpsgd-optimizer
Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enforces privacy by clipping and sanitising the gradients with Gaussian noise during training.pytorch-lmdb
A simple Lightning Memory-Mapped Database (LMDB) converter for ImageFolder datasets in PyTorch. Using LMDB over a regular file structure improves I/O performance significantly. Works on both Windows and Linux. Comes with latest Python support.thesis-cloudksvd
Master's thesis about sparse approximation and dictionary learning using Cloud K-SVD for image denoising. Results show that the algorithm is able to learn sparse representations of signal vectors from distributed data samples in a heterogeneous network setup.ball-bearing-survival
Implementation of Predicting Survival Time of Ball Bearings in the Presence of Censoring (AAAI Fall Symposium 2023)rulsurv
Code for: A probabilistic estimation of remaining useful life from censored time-to-event data (2024)lego-keras-gan
A GAN to create images of LEGO figures.ertms-modelling-vdm
An Overture project that models ERTMS level 2 and interlocking properties in VDM++.UE-BNNSurv
Official TensorFlow implementation of Uncertainty Estimation in Deep Bayesian Survival Models (BHI 2023)mensa
MENSA: A Multi-Event Network for Survival Analysis under Informative Censoring (2024)kubernetes-exercises
Hands-on exercises for Docker and Kubernetes.fedasync-with-fairness
FedAsync with proposed fairness improvementsbaysurv-wip
cloud-ksvd
Cloud K-SVD for Image Denoisingneural_embedder
A small library that can encode categorical variables to entity embeddings using a TensorFlow 2.0 neural network. Supports classification and regression problems. Network parameters are adjustable.csgo-ai-challenge
Skybox presented an AI challenge that ran from Spring to Fall 2020, where teams had to predict the winner of rounds in the tactical shooter CS:GO. This project uses TensorFlow, Scikit-learn and CatBoost to train robust model ensembles using the most relevant features.Love Open Source and this site? Check out how you can help us