Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem. Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…Continue
Added by Jonathan Symonds on August 30, 2018 at 7:00am — No Comments
In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned.
The significance of this work can be summarized as follows:
Added by Jonathan Symonds on August 9, 2018 at 11:30am — No Comments
TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.
Neural networks have demonstrated a great…
Added by Jonathan Symonds on June 21, 2018 at 9:30am — No Comments