This is the Windows app named Automated Interpretability whose latest release can be downloaded as automated-interpretabilitysourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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- 7. I-download ang Wine mula sa iyong mga Linux distributions software repository. Kapag na-install na, maaari mong i-double click ang app upang patakbuhin ang mga ito gamit ang Wine. Maaari mo ring subukan ang PlayOnLinux, isang magarbong interface sa ibabaw ng Wine na tutulong sa iyong mag-install ng mga sikat na programa at laro sa Windows.
Ang alak ay isang paraan upang patakbuhin ang software ng Windows sa Linux, ngunit walang kinakailangang Windows. Ang alak ay isang open-source na layer ng compatibility ng Windows na maaaring direktang magpatakbo ng mga program sa Windows sa anumang desktop ng Linux. Sa totoo lang, sinusubukan ng Wine na muling ipatupad ang sapat na Windows mula sa simula upang mapatakbo nito ang lahat ng mga Windows application na iyon nang hindi talaga nangangailangan ng Windows.
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Automated Interpretability
DESCRIPTION
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature, proposes natural language explanations or heuristics (e.g. “this neuron activates when the input has property X”) and then simulates activation behavior across example inputs to test whether the explanation holds. The project also contains a “neuron viewer” web component for browsing neurons, explanations, and activation patterns, making it more interactive and exploratory.
Mga tampok
- A neuron explainer module that proposes natural language or rule-based explanations for neuron/latent feature behavior
- Simulation / scoring of explanations by comparing predicted activations vs true activations across inputs
- A neuron viewer UI to browse neurons, see activations, and inspect explanations
- Demo notebooks illustrating how explanations are generated and evaluated (e.g. explain_puzzles.ipynb)
- Infrastructure for activation capture and analysis (e.g. modules like activations.py)
- Ranking / scoring heuristics to decide which explanations are more faithful or useful
Wika ng Programming
Sawa
Kategorya
This is an application that can also be fetched from https://sourceforge.net/projects/automated-interpretab.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
