Visualizing Neural Thinking

Visualizing Weight spaces of Neural Nets

Deep learning has made great progress in Artificial Intelligence. The weight structure of the net captures the aggregated knowledge that the various learning algorithms have accumulated. Recently, researchers have started exploring this weight-space, to see if visualization can yield insights into the “thinking of the computer” for lack of a better term.

In this project you will analyze a few simple neural nets that are trained on standard problems, build a visualizer for the weights, and see if the structure of the weights can tell us something about how your network architecture evolves its knowledge.


LIACS intern

Student Profile
Machine learning, game playing concepts, artificial intelligence, statistics

Time frame

Scientific Challenge
Combinatorial Optimization  algorithms are a core field of artificial intelligence and operations research. Better algorithms are key to important application areas ranging from oil-refineries to web-search engines. No visualization for this topic exists yet.

Walter Kosters
Michael Lew
Fons Verbeek
Wojtek Kowalczyk
Aske Plaat

Information: aske.plaat at

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