We develop a principled strategy to sample a function optimally for function approximation tasks within a Bayesian framework. Using ideas from optimal experiment design, we introduce an objective function (incorporating both bias and variance) to measure the degree of approximation, and the potential utility of the data points towards optimizing this objective. We show how the general strategy ...

— We prove that rationally connected varieties over the function field of a complex curve satisfy weak approximation for places of good reduction.

For diophantine approximation of algebraic real numbers by rationals, Roth’s celebrated theorem settles the issue of the approximation exponent completely in the number field case. But in spite of strong analogies between number fields and function fields, its naive analogue fails in function fields of finite characteristic, and the situation is not even conjecturally understood. In this short ...

This paper combines two different styles of asynchronous circuitry used in de-synchronization flow. We offer 20% power savings using approximation functions over previously known architecture while preserving the ability to measure the speed of the circuit at fabrication time or run time. The overhead introduced by additional circuitry is minimal when compared with the baseline dual-rail design.

Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Qlearning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple problems, given proper initialization of the reh...

In performance-optimized artificial neural networks, such as convolutional networks, each neuron makes excitatory connections with some of its targets and inhibitory connections with others. In contrast, physiological neurons are typically either excitatory or inhibitory, not both. This is a puzzle, because it seems to constrain computation, and because there are several counter-examples that s...

In this study, we considered an inflationary inventory control model under non-deterministic conditions. We assumed the inflation rate as a normal distribution, with any arbitrary probability density function (pdf). The objective function was to minimize the total discount cost of the inventory system. We used two methods to solve this problem. One was the classic numerical approach which turne...

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank rea...