This is the Windows app named Supervised Reptile whose latest release can be downloaded as supervised-reptilesourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Supervised Reptile with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
Supervised Reptile
DESCRIPTION
The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. The fundamental idea is: sample a task, train on that task (inner loop), and then move the initialization parameters toward the adapted parameters (outer loop). Because Reptile is a first-order algorithm, it avoids computing second derivatives or full meta-gradients, making it computationally simpler while retaining good performance. The repo includes training scripts, dataset fetchers (Omniglot, Mini-ImageNet), and modules for defining the Reptile update logic, variables, and hyperparameters.
Features
- Implementation of the Reptile algorithm for few-shot supervised meta-learning
- Support scripts for Omniglot and Mini-ImageNet experiment setups
- First-order meta-learning (no second derivatives) for computational simplicity
- Command-line interface for hyperparameter control (shots, inner/outer loops, meta steps)
- Dataset download / preprocessing utilities (e.g. fetch_data.sh)
- Modular structure: reptile.py, variables.py, dataset modules, experiment drivers
Programming Language
JavaScript
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/supervised-reptile.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
