18,408 research outputs found
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
We propose a new neurally-inspired model that can learn to encode the global
relationship context of visual events across time and space and to use the
contextual information to modulate the analysis by synthesis process in a
predictive coding framework. The model learns latent contextual representations
by maximizing the predictability of visual events based on local and global
contextual information through both top-down and bottom-up processes. In
contrast to standard predictive coding models, the prediction error in this
model is used to update the contextual representation but does not alter the
feedforward input for the next layer, and is thus more consistent with
neurophysiological observations. We establish the computational feasibility of
this model by demonstrating its ability in several aspects. We show that our
model can outperform state-of-art performances of gated Boltzmann machines
(GBM) in estimation of contextual information. Our model can also interpolate
missing events or predict future events in image sequences while simultaneously
estimating contextual information. We show it achieves state-of-art
performances in terms of prediction accuracy in a variety of tasks and
possesses the ability to interpolate missing frames, a function that is lacking
in GBM
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns "unknown"
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Memory Structure and Cognitive Maps
A common way to understand memory structures in the cognitive sciences is as a cognitive map​.
Cognitive maps are representational systems organized by dimensions shared with physical space. The
appeal to these maps begins literally: as an account of how spatial information is represented and used
to inform spatial navigation. Invocations of cognitive maps, however, are often more ambitious;
cognitive maps are meant to scale up and provide the basis for our more sophisticated memory
capacities. The extension is not meant to be metaphorical, but the way in which these richer mental
structures are supposed to remain map-like is rarely made explicit. Here we investigate this missing
link, asking: how do cognitive maps represent non-spatial information?​ We begin with a survey of
foundational work on spatial cognitive maps and then provide a comparative review of alternative,
non-spatial representational structures. We then turn to several cutting-edge projects that are engaged
in the task of scaling up cognitive maps so as to accommodate non-spatial information: first, on the
spatial-isometric approach​ , encoding content that is non-spatial but in some sense isomorphic to
spatial content; second, on the ​ abstraction approach​ , encoding content that is an abstraction over
first-order spatial information; and third, on the ​ embedding approach​ , embedding non-spatial
information within a spatial context, a prominent example being the Method-of-Loci. Putting these
cases alongside one another reveals the variety of options available for building cognitive maps, and the
distinctive limitations of each. We conclude by reflecting on where these results take us in terms of
understanding the place of cognitive maps in memory
Intervening to improve outcomes for vulnerable young people : a review of the evidence
Concerns about the number of young people who fail to reach their potential at school, or get into trouble, or are not in education, employment or training (NEET), underpin the continuing commitment to end child poverty in the UK by 2020, and the Coalition Government’s pledge to increase the focus on supporting the neediest families and those with multiple problems. A strong policy commitment to improving the life chances of vulnerable young people has in recent years led to the testing of a number of initiatives.
This review sought to identify: the common barriers to the effective implementation of new initiatives; elements of effective practice in the delivery of multi-agency services for vulnerable young people and their families; the costs associated with integrated service delivery; the outcomes that can be achieved; and whether fewer and more targeted initiatives might offer better value for money, particularly during a period of fiscal reform.
Includes:
•Introduction to the Review
•Identifying and Assessing Vulnerable Young People
•Multi-Agency Working: Innovations in the Delivery of Support Services
•Delivering Interventions and Improving Outcomes for Young People
•Assessing Value for Money in Interventions To Improve Outcomes for Young People
•Looking to the Future: Defining Elements of Effective Practic
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