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Poster
Hamiltonian Neural Networks
Samuel Greydanus · Misko Dzamba · Jason Yosinski

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #67

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. We evaluate our models on problems where conservation of energy is important, including the two-body problem and pixel observations of a pendulum. Our model trains faster and generalizes better than a regular neural network. An interesting side effect is that our model is perfectly reversible in time.

Author Information

Sam Greydanus (Oregon State University)

I am a recent graduate of Dartmouth College, where I majored in physics and dabbled in everything else. I have interned at CERN, Microsoft Azure, and the DARPA Explainable AI Project. I like to use memory-based models to generate sequences and policies. So far, I have used them to approximate the Enigma cipher, generate realistic handwriting, and visualize how reinforcement-learning agents play Atari games. One of my priorities as a scientist is to explain my work clearly and make it easy to replicate.

Misko Dzamba (Freenome)
Jason Yosinski (Uber AI; Recursion)

Dr. Jason Yosinski is a machine learning researcher, was a founding member of Uber AI Labs, and is scientific adviser to Recursion Pharmaceuticals and several other companies. His work focuses on building more capable and more understandable AI. As scientists and engineers build increasingly powerful AI systems, the abilities of these systems increase faster than does our understanding of them, motivating much of his work on AI Neuroscience: an emerging field of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski completed his PhD as a NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, XKCD, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches students algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, motorcycling, reading, paragliding, and sometimes pretending he's an artist.

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