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Name: Jannes Gladrow
Type: User
Bio: Physics, software, and ML.
Twitter: JannesGlad
Location: London, UK
Name: Jannes Gladrow
Type: User
Bio: Physics, software, and ML.
Twitter: JannesGlad
Location: London, UK
Optimise a matrix multiplication up to CUBLAS performance, following https://siboehm.com/articles/22/CUDA-MMM
Command line program for guitar chords and scales.
Holographic wave-shaping has found numerous applications across the physical sciences, especially since the development of digital spatial-light modulators (SLMs). A key challenge in digital holog- raphy consists in finding optimal hologram patterns which transform the incoming laser beam into desired shapes in a conjugate optical plane. The existing repertoire of approaches to solve this inverse problem is built on iterative phase-retrieval algorithms, which do not take optical aberrations and deviations from theoretical models into account. Here, we adopt a physics-free, data-driven, and probabilistic approach to the problem. Using deep conditional Generative-Adversarial-Networks (cGAN) and conditional Variational Autoencoder (cVAE) architectures, we approximate posterior distributions of holograms for a given target laser intensity pattern. In order to reduce the cardinality of the problem, we train our models on a proxy mapping relating an 8 × 8-matrix of complex-valued spatial-frequency coefficients to the ensuing 100 × 100-shaped intensity distribution recorded on a camera. We discuss the degree of ’ill-posedness’ that remains in this reduced problem and challenge our generative models to find holograms that reconstruct given intensity patterns. Finally, we study the ability of the models to generalise to synthetic target intensities, where the existence of matching holograms cannot be guaranteed. We devise a forward-interpolating training scheme aimed at provid- ing models the ability to interpolate in laser intensity space, rather than hologram space and show that this indeed enhances model performance on synthetic data sets.
[ImageJ plugin] Multiplanefitter for multifocus fluorescence microscopy
Deep Learning library in Labview. C++-based implementation of a feed-forward neural network. Compilation requires version 3.3.5. of the Eigen library. Additional layer-sharing, GAN and Mixture Density Capability to deal with ill-posed inverse problems. Currently applied to inverse-holography (infer back on the hologram from the light field it creates). Compiled with Visual Studio C++ 2015.
Spike-time based simulation of a network of neurons. I used this to investigate synchronization and non-monotonicity of Leaky/Quadratic-Integrate-and-fire.
An implementation of WaveNet with fast generation
Minimal vscode torch 1.5 C++ build example with unittests.
following the online tutorial https://raytracing.github.io/books/RayTracingInOneWeekend.html
Me following Tim McNamara's book.
A simple example of a graph convolutional network. A GCN is used here to predict positions of nodes in a graph.
Webpage for our upcoming workshop
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.