86,890 research outputs found
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
A Bio-Logical Theory of Animal Learning
This article provides the foundation for a new predictive theory of animal learning that is based upon a simple logical model. The knowledge of experimental subjects at a given time is described using logical equations. These logical equations are then used to predict a subject’s response when presented with a known or a previously unknown situation. This new theory suc- cessfully anticipates phenomena that existing theories predict, as well as phenomena that they cannot. It provides a theoretical account for phenomena that are beyond the domain of existing models, such as extinction and the detection of novelty, from which “external inhibition” can be explained. Examples of the methods applied to make predictions are given using previously published results. The present theory proposes a new way to envision the minimal functions of the nervous system, and provides possible new insights into the way that brains ultimately create and use knowledge about the world
Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
This paper aims at the problem of link pattern prediction in collections of
objects connected by multiple relation types, where each type may play a
distinct role. While common link analysis models are limited to single-type
link prediction, we attempt here to capture the correlations among different
relation types and reveal the impact of various relation types on performance
quality. For that, we define the overall relations between object pairs as a
\textit{link pattern} which consists in interaction pattern and connection
structure in the network, and then use tensor formalization to jointly model
and predict the link patterns, which we refer to as \textit{Link Pattern
Prediction} (LPP) problem. To address the issue, we propose a Probabilistic
Latent Tensor Factorization (PLTF) model by introducing another latent factor
for multiple relation types and furnish the Hierarchical Bayesian treatment of
the proposed probabilistic model to avoid overfitting for solving the LPP
problem. To learn the proposed model we develop an efficient Markov Chain Monte
Carlo sampling method. Extensive experiments are conducted on several real
world datasets and demonstrate significant improvements over several existing
state-of-the-art methods.Comment: 19pages, 5 figure
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation
We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures
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