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GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection

Neural Information Processing Systems

Learning procedures that measure how random perturbations of unit activities correlatewith changes in reinforcement are inefficient but simple to implement in hardware. Procedures like back-propagation (Rumelhart, Hinton and Williams, 1986) which compute how changes in activities affect theoutput error are much more efficient, but require more complex hardware. GEMINI is a hybrid procedure for multilayer networks, which shares many of the implementation advantages of correlational reinforcement proceduresbut is more efficient. GEMINI injects noise only at the first hidden layer and measures the resultant effect on the output error. A linear network associated with each hidden layer iteratively inverts the matrix which relates the noise to the error change, thereby obtaining the error-derivatives. No back-propagation is involved, thus allowing unknown non-linearitiesin the system. Two simulations demonstrate the effectiveness of GEMINI.


GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection

Neural Information Processing Systems

Learning procedures that measure how random perturbations of unit activities correlate with changes in reinforcement are inefficient but simple to implement in hardware. Procedures like back-propagation (Rumelhart, Hinton and Williams, 1986) which compute how changes in activities affect the output error are much more efficient, but require more complex hardware. GEMINI is a hybrid procedure for multilayer networks, which shares many of the implementation advantages of correlational reinforcement procedures but is more efficient. GEMINI injects noise only at the first hidden layer and measures the resultant effect on the output error. A linear network associated with each hidden layer iteratively inverts the matrix which relates the noise to the error change, thereby obtaining the error-derivatives. No back-propagation is involved, thus allowing unknown non-linearities in the system. Two simulations demonstrate the effectiveness of GEMINI.


Gemini Data

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Gemini Data

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Cryptocurrency exchange Gemini launches mobile trading app

Mashable

Users of U.S.-based cryptocurrency exchange Gemini can now trade on the go with a new mobile app, launched for iOS and Android on Tuesday. The app lets you buy and sell cryptocurrencies, including bitcoin, ether, litecoin, bitcoin cash and Zcash. SEE ALSO: Crypto's black Wednesday: Where do we go from here? Besides buying, selling, sending, receiving and storing cryptocoins, the app comes with a couple of advanced features. These include setting recurring buy orders and buying a basket of cryptocurrencies (called Cryptoverse) with a single order.