Biomedical subjects

K Y Michael Wong

Publications and source records attributed to K Y Michael Wong.

5 recordsLinked to original sources

PubMed2006

Equilibration through local information exchange in networks.

We study the equilibrium states of energy functions involving a large set of real variables, defined on the links of sparsely connected networks, and interacting at the network nodes, using the cavity and replica methods. When applied to the representative problem of network resource allocation, an efficient distributed algorithm is devised, with simulations showing full agreement with theory. Scaling properties with the network connectivity and the resource availability are found.

Journal Article↗

PubMed2005

Effects of diversity on multiagent systems: minority games.

We consider a version of large population games whose agents compete for resources using strategies with adaptable preferences. The games can be used to model economic markets, ecosystems, or distributed control. Diversity of initial preferences of strategies is introduced by randomly assigning biases to the strategies of different agents. We find that diversity among the agents reduces their maladaptive behavior. We find interesting scaling relations with diversity for the variance and other parameters such as the convergence time, the fraction of fickle agents, and the variance of wealth, illustrating their dynamical origin. When diversity increases, the scaling dynamics is modified by kinetic sampling and waiting effects. Analyses yield excellent agreement with simulations.

Journal Article↗

PubMed2004

Dynamical mechanisms of adaptation in multiagent systems.

We consider multiagent systems whose agents compete for resources using strategies with adaptable preferences. Diversity of initial preferences of strategies is introduced by randomly assigning virtual points to the strategies of each agent. When diversity increases, the successive appearance of scaling, kinetic sampling and waiting mechanisms shows that agent cooperation becomes increasingly important. Analyses yield excellent agreement with simulations over nine decades of data.

Journal Article↗

PubMed2003

Dynamical and stationary properties of on-line learning from finite training sets.

The dynamical and stationary properties of on-line learning from finite training sets are analyzed by using the cavity method. For large input dimensions, we derive equations for the macroscopic parameters, namely, the student-teacher correlation, the student-student autocorrelation and the learning force fluctuation. This enables us to provide analytical solutions to Adaline learning as a benchmark. Theoretical predictions of training errors in transient and stationary states are obtained by a Monte Carlo sampling procedure. Generalization and training errors are found to agree with simulations. The physical origin of the critical learning rate is presented. Comparison with batch learning is discussed throughout the paper.

Algorithms↗

PubMed2001

Cavity approach to noisy learning in nonlinear perceptrons.

We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation in the perceptron that learns without the example. Mean-field equations for the macroscopic parameters and the stability condition yield results consistent with the replica method. When a single value of the cavity activation maps to multiple values of the generic activation, there is a competition in learning strategy between preferentially learning an example and sacrificing it in favor of the background adjustment. We find parameter regimes in which examples are learned preferentially or sacrificially, leading to a gap in the activation distribution. Full phase diagrams of this complex system are presented, and the theory predicts the existence of a phase transition from poor to good generalization states in the system. Simulation results confirm the theoretical predictions.

Biophysical Phenomena↗