Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior

Abstract: At the level of individual neurons, catecholamine release increases the responsivity of cells to excitatory and inhibitory inputs. A model of catecholamine effects in a network of neural-like elements is presented, which shows that (i) changes in the responsivity of individual elements do not affect their ability to detect a signal and ignore noise but (ii) the same changes in cell responsivity in a network of such elements do improve the signal detection performance of the network as a whole. The second result is used in a computer simulation based on principles of parallel distributed processing to account for the effect of central nervous system stimulants on the signal detection performance of human subjects.

Space-efficient static trees and graphs

Abstract: Data structures that represent static unlabeled trees and planar graphs are developed. The structures are more space efficient than conventional pointer-based representations, but (to within a constant factor) they are just as time efficient for traversal operations. For trees, the data structures described are asymptotically optimal: there is no other structure that encodes n-node trees with fewer bits per node, as N grows without bound. For planar graphs (and for all graphs of bounded page number), the data structure described uses linear space: it is within a constant factor of the most succinct representation. >

Entry by Spinoffs

Abstract: Entry by spinoffs from incumbent firms is investigated for the laser industry. A model in which spinoffs exploit knowledge from their parents is constructed to explain the market conditions conducive to spinoffs, the types of firms that spawn spinoffs, and the relationship of spinoffs to their parents. The model is tested using detailed data on all laser entrants from the start of the industry through 1994. Our findings support the basic premise of the model that spinoffs inherit knowledge from their parents that shapes their nature at birth. Implications of our findings for organizational behavior, business strategy, entry and industry evolution, and technological change are discussed.

Variational Dropout and the Local Reparameterization Trick.

Abstract: We explore an as yet unexploited opportunity for drastically improving the efficiency of stochastic gradient variational Bayes (SGVB) with global model parameters. Regular SGVB estimators rely on sampling of parameters once per minibatch of data, and have variance that is constant w.r.t. the minibatch size. The efficiency of such estimators can be drastically improved upon by translating uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such reparameterizations with local noise can be trivially parallelized and have variance that is inversely proportional to the minibatch size, generally leading to much faster convergence.We find an important connection with regularization by dropout: the original Gaussian dropout objective corresponds to SGVB with local noise, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose \emph{variational dropout}, a generalization of Gaussian dropout, but with a more flexibly parameterized posterior, often leading to better generalization. The method is demonstrated through several experiments.

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.

Abstract: The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual’s covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.