A couple of years ago a new paradigm, based on the quantum
adiabatic theorem, was introduced for the design of quantum
algorithms for solving optimization problems.
We present a computational framework for image-based statistical
analysis of anatomical image data in different populations.
Applications of such analysis include understanding developmental and
anatomical aspects of disorders when comparing patients vs.
Microarray analysis allows for large-scale exploration of gene expression by taking a snap shot of the cell at a specific point in time. Such data sets may provide insight into fundamental biological questions as well as address clinical issues such as diagnosis and therapy selection.
Learning to act in a multiagent environment is a challenging problem.
Optimal behavior for one agent depends upon the behavior of the other
agents, which may be learning as well.
Exponential weighting techniques have had a big impact on learning, in theory and practice. First, they provide a general theoretical approach for online (adaptive) learning, where one must adapt to an unknown future.
Many real-world tasks require multiple agents to coordinate in order to
achieve a common goal. Examples include multi-robot systems, network
routing and supply chain management. Unfortunately, these large-scale
problems are often quite complex, involving many states and multiple
decision makers.
This talk presents the Neptune project that addresses the system and
programming-level issues in building high performance and reliable runtime
support for online services and data-intensive applications.
Online applications differ from their offline counterpart in the performance
sensitivity to hi
I will present an approach to computer vision research motivated by the 19th C. constructivist theory of Hermann von Helmholtz as well as the 20th C. ecological theories of James Gibson and Egon Brunswik.