PHOTONAI is a high level python API for designing and optimizing machine learning pipelines. We’ve created a system in which you can easily select and combine both pre-processing and learning algorithms fromstate-of-the-art machine learning toolboxes,and arrange them in simple or parallel pipeline data streams. In addition, you can parametrize your training and testingworkflow choosing cross-validation schemes, performance metrics and hyperparameteroptimization metrics from a list of pre-registered options. Importantly, you can integrate custom solutions into your data processing pipeline,but also for […]

This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta recurrent NNs. Our Variable Shared Meta Learning (VSML) unifies the above and demonstrates that simple weight-sharing and sparsity in an NN is sufficient to express powerful learning algorithms (LAs) in a reusable fashion. A simple implementation of VSML where the […]

This project was part of my Master’s degree Thesis at Sharif university of Technology A risk assessment study and repair time proposal has been performed for a very sensitive and complex process unit. I put parts of the project here. Predictive maintenance study for Complex case study. we’ve obtained failure causes was basically an operation fault and more basically plant conceptual design Error. Obviously, the use of code and content needs to cite the source. For more you can check […]

ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more → ONNX Runtime training can accelerate the […]

Trevor Ablett*, Bryan Chan*, Jonathan Kelly (*equal contribution) Poster at Neurips 2021 Deep Reinforcement Learning Workshop Adversarial Imitation Learning (AIL) is a technique for learning from demonstrations that helps remedy the distribution shift problem that occurs with Behavioural Cloning. Empirically, we found that for manipulation tasks, off-policy AIL can suffer from inefficient or stagnated learning. In this work, we resolve this by enforcing exploration of a set of easy-to-define auxiliary tasks, in addition to a main task. This repository contains […]

This repo contains a barebones implementation for the attack detailed in the paper: Fowl L, Geiping J, Czaja W, Goldblum M, Goldstein T. Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. arXiv preprint arXiv:2110.13057. 2021 Oct 25. Left: batch of 64 ImageNet images. Right: Images reconstructed with imprint module containing 128 bins placed in front of a ResNet-18. Average PSNR: 70.94. Abstract: Federated learning has quickly gained popularity with its promises of increaseduser privacy   […]