Why Deep Learning Works: Self Regularization in DNNs An early talk describing details in this paper Implicit Self-Regularization in Deep…
Rethinking–or Remembering–Generalization in Neural Networks
I just got back from ICLR 2019 and presented 2 posters, (and Michael gave a great talk!) at the Theoretical…
Capsule Networks: A video presentation
Please enjoy my video presentation on Geoff Hinton’s Capsule Networks. What they are, why they are important, and how they…
Free Energies and Variational Inference
My Labor Day Holiday Blog: for those on email, I will add updates, answer questions, and make corrections over the…
What is Free Energy: Hinton, Helmholtz, and Legendre
Hinton introduced Free Energies in his 1994 paper, Autoencoders, minimum description length, and Helmholtz Free Energy This paper, along with…
Interview with a Data Scientist
It’s always fun to be interviewed. Check out my recent chat with Max Mautner, the Accidental Engineer http://theaccidentalengineer.com/charles-martin-principal-consultant-calculation-consulting/
Normalization in Deep Learning
A few days ago (Jun 2017), a 100 page on Self-Normalizing Networks appeared. An amazing piece of theoretical work, it…
Why Deep Learning Works 3: BackProp minimizes the Free Energy ?
?Deep Learning is presented as Energy-Based Learning Indeed, we train a neural network by running BackProp, thereby minimizing the model error–which is…
Foundations: Mean Field Boltzmann Machines 1987
A friend from grad school pointed out a great foundational paper on Boltzmann Machines. It is a 1987 paper from…
Improving RBMs with physical chemistry
Restricted Boltzmann Machines (RBMs) are like the H atom of Deep Learning. They are basically a solved problem, and while of…
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