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# Browsing MIT Open Access Articles by Title

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• (National Academy of Sciences, 2009-04)
The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing ...
• (Robotics: Science and Systems, 2013-06)
This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches augment metric maps with higher-level ...
• (The Academy of Management, 2012-06)
Much is known about the importance of learning and some of the distinct learning processes that organizations use (e.g., trial-and-error learning, vicarious learning, experimental learning, and improvisational learning). ...
• (Mary Ann Liebert, Inc., 2009-02)
Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a ...
• (Mary Ann Liebert, Inc., 2009-02)
Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a ...
• (Elsevier, 2009-07)
Learning from experience requires knowing whether a past action resulted in a desired outcome. The prefrontal cortex and basal ganglia are thought to play key roles in such learning of arbitrary stimulus-response associations. ...
• (Elsevier Inc., 2009-07)
Learning from experience requires knowing whether a past action resulted in a desired outcome. The prefrontal cortex and basal ganglia are thought to play key roles in such learning of arbitrary stimulus-response associations. ...
• (Institute of Electrical and Electronics Engineers, 2010-06)
Image registration is typically formulated as an optimization problem with multiple tunable, manually set parameters. We present a principled framework for learning thousands of parameters of registration cost functions, ...
• (Association for Computing Machinery (ACM), 2013-11)
We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction." In ...
• (Oxford University Press, 2011-04)
This study explores why international joint ventures (IJVs) based on the global South may meet with only partial success in nurturing local technological capability. The experience of China’s passenger-vehicle sector ...
• (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013, 2013-08-21)
Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ...
• (Institute of Electrical and Electronics Engineers, 2010-05)
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to ...
• (Association for Computing Machinery, 2011-06)
This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with ...
• (Institute of Electrical and Electronics Engineers, 2009-08)
We present a novel method for modeling dynamic visual phenomena, which consists of two key aspects. First, the integral motion of constituent elements in a dynamic scene is captured by a common underlying geometric transform ...
• (Institute of Electrical and Electronics Engineers (IEEE), 2013-08)
We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a ...
• (Bernoulli Society for Mathematical Statistics and Probability, )
We study post-model selection estimators which apply ordinary least squares (ols) to the model selected by first-step penalized estimators. It is well known that lasso can estimate the nonparametric regression function at ...
• (Society for Industrial and Applied Mathematics, 2012-12)
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) with the least squares temporal difference (LSTD) algorithm, LSTD($\lambda$), in an exploration-enhanced learning context, ...
• (Springer-Verlag, 2014-07)
The s-lecture hall polytopes P [subscript s] are a class of integer polytopes defined by Savage and Schuster which are closely related to the lecture hall partitions of Eriksson and Bousquet-Mélou. We define a half-open ...
• (Oxford University Press, 2010-03)
Using Lefschetz fibrations, we construct nonstandard symplectic structures on cotangent bundles of spheres. These structures are of Liouville type, which means exact and convex at infinity.
• (University of California Press, 2012-06)
Legacy effects of past land use and disturbance are increasingly recognized, yet consistent definitions of and criteria for defining them do not exist. To address this gap in biological- and ecosystem-assessment frameworks, ...

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