Dynamic Graph Architecture to Enable Composite Networks

I had been thinking for a while about the most flexible way to add Convolutional layers to my existing API when I read an article about the so-called Deep Residual Learning. Roughly speaking Residual Learning requires a network architecture in which each layer consists of two layers, duplicates of the same amount of neurons, and […]

Artificial Intelligence Fight VI. – Some Unexpected Improvements

Since the last AI Fight article I did not make conscious efforts to improve the performance of the algorithms, I have been busy with updating the existing interfaces/application, and with creating new interfaces. I have rewritten all test harnesses from scratch, and I have added a new WebAPI interface. Also I started experimenting with an […]

New XNNS file format to save and load neural networks

I have been reading about the ONNX file format recently that has been created internally by Google, but before I delve myself into protocol buffers I still needed an easily readable (read: debuggable) file format to exchange neural network states. I know that eventually I will have to end up supporting ONNX so I did […]

Why DeepTrainer?

Recently I’ve been reading quite a lot about activation functions and Neural Networks in general and I think I found a good answer to a question that has been bugging me (and others who know what I am working on) ever since I started working on my own deep learning framework. I’ve had conversations with […]

Artificial Intelligence Fight V. – Playing with activation functions, introducing CUDA C/C++, and thoughts about SGI, Nvidia and Intel.

Positive results My marketing department that’s just around in the bedroom (where dreams come t̶r̶u̶e̶  and go) have been bugging me to continue the AI Fight sequel so here it is. When I reach #XVI someone please warn me diplomatically to stop otherwise it will gain consciousness and start its own Netflix pilot. There is […]