While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. In this work, overfitting control (or generalization property) in machine learning is explained using analogies from physics and biology. For stochastic gradient Langevin dynamics, we show that the Eyring formula of kinetic theory allows to control overfitting in the algorithmic stability approach-when wide minima of the risk function with low free energy correspond to low overfitting.
View Article and Find Full Text PDFBackground: The Nova Scotia Duck Tolling Retriever (NSDTR) has previously been highlighted as a breed at risk for developing immune mediated diseases and cancer. The immune response is of great importance for the development of neoplastic disease and a dysregulated immune response may predispose to cancer. Two of the commonly seen immune mediated diseases in NSDTRs are immune mediated rheumatic disease (IMRD), which bears similarities to systemic lupus erythematosus (SLE) affecting humans, and steroid-responsive meningitis-arteritis (SRMA), which is a non-infectious inflammation of the meninges and the leptomeningeal vessels.
View Article and Find Full Text PDFThe Nova Scotia Duck Tolling Retriever (NSDTR) is predisposed to immune mediated rheumatic disease (IMRD), steroid-responsive meningitis-arteritis (SRMA) and certain forms of cancer. Cytokines are the main regulators of the immune system. Interleukin 2 is a cytokine involved in activation of T regulatory cells, playing a role in central tolerance and tumor immunity.
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