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We compute the transition probability between two learning tasks, and show that it decomposes into two factors.
Brownian motion in a field of force and the diffusion model of chemical reactions
H.A. Kramers · 1940
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Diffusion in a bistable potential: The functional integral approach
B. Caroli, C. Caroli, and B. Roulet · 1981
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Path integral solutions of stochastic equations for nonlinear irreversible processes: the uniqueness of the thermodynamic lagrangian
Katharine LC Hunt and John Ross · 1981
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Kolmogorov’s structure functions and model selection
Nikolai K Vereshchagin and Paul MB Vitányi · 2004
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On asymmetric distances
Andrea Mennucci · 2007
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Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
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Brownian motion and stochastic calculus
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Stochastic modified equations and adaptive stochastic gradient algorithms
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Emergence of invariance and disentanglement in deep representations
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Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari and Stefano Soatto · 2018
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Task2Vec: Task Embedding for Meta-Learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, and Pietro Perona · 2019
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The Information Complexity of Learning Tasks, their Structure and their Distance
Alessandro Achille, Giovanni Paolini, Glen Mbeng, and Stefano Soatto · 2019
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Critical learning periods in deep networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2019
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