Posts
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Transformer explanations: a collection
Transformer is a powerful architecture that can be difficult to understand. There are many great explanations on the web, each approaching the subject in a different way. Here I link the explanations I liked, and mention who I believe the target audience is for each one.
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PBT-NAS: mix architectures during training
Based on our paper “Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search” (ECAI 2023)
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Install nvcc without root
I needed to install
nvccon our group server, where I lack root privileges. I found a nice guide, in this post I will slightly expand on it by explicitly mentioning every step I had to take. -
ENCAS: Search cascades of neural networks in any model pool
Based on our paper “Evolutionary Neural Cascade Search across Supernetworks” (Best Paper award @ GECCO 2022)
TL;DR Search for cascades of neural networks in model pools of hundreds of models, optionally using Neural Architecture Search to efficiently generate many diverse task-specific neural networks. Get dominating trade-off fronts on ImageNet and CIFAR-10/100, improved ImageNet SOTA (of publicly available models).
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Document recognition with Python, OpenCV and Tesseract
Recently I’ve conducted my own little experiment with the document recognition technology: I’ve successfully went from an image to the recognized editable text.
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