Paper Dreams is a web-based multimodal creative platform for drawing that com-bines human-drawn sketches and a variety of machine learning algorithms to facilitate user expression.
Over 200 figures and diagrams of the most popular deep learning ...
Over 200 figures and diagrams of the most popular deep learning architectures and layers FREE TO USE in your blog posts, slides, presentations, or papers.
How to draw Deep learning network architecture diagrams?
Nov 3, 2016 · I've been working on a python project for drawing various network architectures here: PyDrawNet. I recently found this online tool that produces publication-ready NN …
Tools to Design or Visualize Architecture of Neural Network
TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. By applying TensorSpace API, it is more intuitive to …
How to Easily Draw Neural Network Architecture Diagrams
Aug 23, 2021 · Instead of explaining the model in words, diagram visualizations are way more effective in presenting and describing a neural network’s architecture. We have probably …
[D] Research paper figure drawing : r/MachineLearning - Reddit
Dec 26, 2021 · A little unconventional, but I prefer IPE. It's a vector graphics tool that exports to PDF, SVG, or PNG. The tools are fairly easy to learn, yet can produce useful figures - …
Which software is appropriate for drawing complicated block diagrams …
Jan 24, 2022 · What sort of software can I use to make block diagrams, such as those used in machine learning papers? For example, this graph: From the paper: …
[D] Diagrams and graphs in papers : r/MachineLearning - Reddit
Apr 27, 2017 · Specifically how to generate a 3D diagram of convolutional layer (e.g. https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf). and aesthetically pleasing …
Interactive and interpretable machine learning models for human machine …
In this thesis, I bridge this gap by building human-in-the-loop machine learning models and systems that compute and communicate machine learning results in ways that are compatible …
Preface tically intriguing and useful in practice. This dissertation aims to establish two aspect of sta-tistical inference in such models. Those lead to con dence sets for un-known parameters …
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