Monday, March 6, 2023
3:00PM - 3:36PM
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D07.00001: Equilibrium Propagation: A Physics-Grounded Theory of Computation and Learning
Invited Speaker:
Benjamin Scellier
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Monday, March 6, 2023
3:36PM - 3:48PM
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D07.00002: Frequency propagation: Multi-mechanism learning in nonlinear physical networks
Vidyesh Rao Anisetti
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Monday, March 6, 2023
3:48PM - 4:00PM
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D07.00003: Physical learning of energy-efficient solutions
Menachem Stern, Sam J Dillavou, Douglas J Durian, Andrea J Liu
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Monday, March 6, 2023
4:00PM - 4:12PM
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D07.00004: Persistent Homology Analysis of Learned Tasks in Physical Learning Systems
Felipe Martins, Andrea J Liu
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Monday, March 6, 2023
4:12PM - 4:24PM
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D07.00005: The effect of learning on information content in learning machines
Ben Pisanty, Menachem Stern, Andrea J Liu
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Monday, March 6, 2023
4:24PM - 4:36PM
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D07.00006: Transistor-Based Self-Learning Networks
Sam J Dillavou, Benjamin Beyer, Menachem Stern, Marc Z Miskin, Andrea J Liu, Douglas J Durian
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Monday, March 6, 2023
4:36PM - 4:48PM
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D07.00007: Physical learning at nonzero temperatures
Jovana Andrejevic, Purba Chatterjee, Sidney R Nagel, Andrea J Liu
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Monday, March 6, 2023
4:48PM - 5:00PM
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D07.00008: Electrochemical potential enables dormant spores to integrate environmental signals
Leticia Galera-Laporta, Kaito Kikuchi, Colleen Weatherwax, Jamie Y Lam, Eun Chae Moon, Emmanuel A Theodorakis, Jordi Garcia-Ojalvo, Gürol M Süel
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Monday, March 6, 2023
5:00PM - 5:12PM
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D07.00009: Bounds on Predictive Capabilities of Driven Markov Systems
Ugur Cetiner, Lisa Duan, Jeremy Gunawardena
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Monday, March 6, 2023
5:12PM - 5:24PM
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D07.00010: Driving the most marginally stable variables as a paradigm for learning, memory, and optimization
Stefan Boettcher
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Monday, March 6, 2023
5:24PM - 5:36PM
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D07.00011: A local learning rule for training precise stress patterns
Daniel Hexner
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Monday, March 6, 2023
5:36PM - 5:48PM
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D07.00012: Self-learning mechanical circuits
Vishal P Patil, Ian Ho, Manu Prakash
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