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Articles 1 - 5 of 5
Full-Text Articles in Artificial Intelligence and Robotics
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Doctoral Dissertations
In the past decades, the computing capability has shown an exponential growth trend, which is observed as Moore’s law. However, this growth speed is slowing down in recent years mostly because the down-scaled size of transistors is approaching their physical limit. On the other hand, recent advances in software, especially in big data analysis and artificial intelligence, call for a break-through in computing hardware. The memristor, or the resistive switching device, is believed to be a potential building block of the future generation of integrated circuits. The underlying mechanism of this device is different from that of complementary metal-oxide-semiconductor (CMOS) …
Integration Of Robotic Perception, Action, And Memory, Li Yang Ku
Integration Of Robotic Perception, Action, And Memory, Li Yang Ku
Doctoral Dissertations
In the book "On Intelligence", Hawkins states that intelligence should be measured by the capacity to memorize and predict patterns. I further suggest that the ability to predict action consequences based on perception and memory is essential for robots to demonstrate intelligent behaviors in unstructured environments. However, traditional approaches generally represent action and perception separately---as computer vision modules that recognize objects and as planners that execute actions based on labels and poses. I propose here a more integrated approach where action and perception are combined in a memory model, in which a sequence of actions can be planned based on …
Machine Learning Methods For Activity Detection In Wearable Sensor Data Streams, Roy Adams
Machine Learning Methods For Activity Detection In Wearable Sensor Data Streams, Roy Adams
Doctoral Dissertations
Wearable wireless sensors have the potential for transformative impact on the fields of health and behavioral science. Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals in natural environments; however, extracting reliable high-level inferences from these raw data streams remains a key data analysis challenge. In this dissertation, we address three challenges that arise when trying to perform activity detection from wearable sensor streams. First, we address the challenge of learning from small amounts of noisy data by proposing a class of conditional random field models for …
Transfer Learning With Mixtures Of Manifolds, Thomas Boucher
Transfer Learning With Mixtures Of Manifolds, Thomas Boucher
Doctoral Dissertations
Advances in scientific instrumentation technology have increased the speed of data acquisition and the precision of sampling, creating an abundance of high-dimensional data sets. The ability to combine these disparate data sets and to transfer information between them is critical to accurate scientific analysis. Many modern-day instruments can record data at many thousands of channels, far greater than the actual degrees of freedom in the sample data. This makes manifold learning, a class of methods that exploit the observation that high-dimensional data tend to lie on lower-dimensional manifolds, especially well-suited to this transfer learning task. Existing manifold-based transfer learning methods …
Using Latent Variable Models To Improve Causal Estimation, Huseyin Oktay
Using Latent Variable Models To Improve Causal Estimation, Huseyin Oktay
Doctoral Dissertations
Estimating the causal effect of a treatment from data has been a key goal for a large number of studies in many domains. Traditionally, researchers use carefully designed randomized experiments for causal inference. However, such experiments can not only be costly in terms of time and money but also infeasible for some causal questions. To overcome these challenges, causal estimation methods from observational data have been developed by researchers from diverse disciplines and increasingly studies using such methods account for a large share in empirical work. Such growing interest has also brought together two arguably separate fields: machine learning and …