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…
MMDS 2016 Video Presentation
Why Deep Learning Works: Perspectives from Theoretical Chemistry:
on Cheap Learning: Partition Functions and RBMs
“Why does deep and cheap learning work so well?“ This is the question posed by a recent article. Deep Learning…
Why Deep Learning Works III: a preview
A preview of my upcoming talk at mmds this week: CC mmds talk 2106 from Charles Martin Stay tuned for…
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