Models, code, and papers for "Ryan C":

Consistent Map-based 3D Localization on Mobile Devices

Apr 27, 2016
Ryan C. DuToit, Joel A. Hesch, Esha D. Nerurkar, Stergios I. Roumeliotis

The objective of this paper is to provide consistent, real-time 3D localization capabilities to mobile devices navigating within previously mapped areas. To this end, we introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which explicitly considers the uncertainty of the prior map, by employing the sparse Cholesky factor of the map's Hessian, instead of its dense covariance--as is the case for the Schmidt-Kalman filter (SKF). By doing so, the C-SKF has memory requirements typically linear in the size of the map, as opposed to quadratic for storing the map's covariance. Moreover, and in order to bound the processing needs of the C-SKF (between linear and quadratic in the size of the map), we introduce a relaxation of the C-SKF algorithm, the sC-SKF, which operates on the Cholesky factors of independent sub-maps resulting from dividing the trajectory and observations used for constructing the map into overlapping segments. Lastly, we assess the processing and memory requirements of the proposed C-SKF and sC-SKF algorithms, and compare their positioning accuracy against other approximate map-based localization approaches that employ measurement-noise-covariance inflation to compensate for the map's uncertainty.

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The Challenge of Predicting Meal-to-meal Blood Glucose Concentrations for Patients with Type I Diabetes

Mar 29, 2019
Neil C. Borle, Edmond A. Ryan, Russell Greiner

Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia - high blood glucose (BG). Patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can potentially be fatal. Patients therefore follow a "regimen" that determines how much insulin to inject at certain times. Current methods for managing this disease require adjusting the patient's regimen over time based on the disease's behavior (recorded in the patient's diabetes diary). If we can accurately predict a patient's future BG values from his/her current features (e.g., predicting today's lunch BG value given today's diabetes diary entry for breakfast, including insulin injections, and perhaps earlier entries), then it is relatively easy to produce an effective regimen. This study explores the challenges of BG modeling by applying several machine learning algorithms and various data preprocessing variations (corresponding to 312 [learner, preprocessed-dataset] combinations), to a new T1D dataset containing 29 601 entries from 47 different patients. Our most accurate predictor is a weighted ensemble of two Gaussian Process Regression models, which achieved an errL1 loss of 2.70 mmol/L (48.65 mg/dl). This was an unexpectedly poor result given that one can obtain an errL1 of 2.91 mmol/L (52.43 mg/dl) using the naive approach of simply predicting the patient's average BG. For each of data-variant/model combination we report several evaluation metrics, including glucose-specific metrics, and find similarly disappointing results (the best model was only incrementally better than the simplest measure). These results suggest that the diabetes diary data that is typically collected may not be sufficient to produce accurate BG prediction models; additional data may be necessary to build accurate BG prediction models.

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A Classifying Variational Autoencoder with Application to Polyphonic Music Generation

Nov 19, 2017
Jay A. Hennig, Akash Umakantha, Ryan C. Williamson

The variational autoencoder (VAE) is a popular probabilistic generative model. However, one shortcoming of VAEs is that the latent variables cannot be discrete, which makes it difficult to generate data from different modes of a distribution. Here, we propose an extension of the VAE framework that incorporates a classifier to infer the discrete class of the modeled data. To model sequential data, we can combine our Classifying VAE with a recurrent neural network such as an LSTM. We apply this model to algorithmic music generation, where our model learns to generate musical sequences in different keys. Most previous work in this area avoids modeling key by transposing data into only one or two keys, as opposed to the 10+ different keys in the original music. We show that our Classifying VAE and Classifying VAE+LSTM models outperform the corresponding non-classifying models in generating musical samples that stay in key. This benefit is especially apparent when trained on untransposed music data in the original keys.

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Bayesian Online Changepoint Detection

Oct 19, 2007
Ryan Prescott Adams, David J. C. MacKay

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

* 7 pages, 4 figures, latex 

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Open-Sourced Reinforcement Learning Environments for Surgical Robotics

Mar 05, 2019
Florian Richter, Ryan K. Orosco, Michael C. Yip

Reinforcement Learning (RL) is a machine learning framework for artificially intelligent systems to solve a variety of complex problems. Recent years has seen a surge of successes solving challenging games and smaller domain problems, including simple though non-specific robotic manipulation and grasping tasks. Rapid successes in RL have come in part due to the strong collaborative effort by the RL community to work on common, open-sourced environment simulators such as OpenAI's Gym that allow for expedited development and valid comparisons between different, state-of-art strategies. In this paper, we aim to bridge the RL and the surgical robotics communities by presenting the first open-sourced reinforcement learning environments for surgical robotics, called dVRL. Through the proposed RL environment, which are functionally equivalent to Gym, we show that it is easy to prototype and implement state-of-art RL algorithms on surgical robotics problems that aim to introduce autonomous robotic precision and accuracy to assisting, collaborative, or repetitive tasks during surgery. Learned policies are furthermore successfully transferable to a real robot. Finally, combining dVRL with the over 40+ international network of da Vinci Surgical Research Kits in active use at academic institutions, we see dVRL as enabling the broad surgical robotics community to fully leverage the newest strategies in reinforcement learning, and for reinforcement learning scientists with no knowledge of surgical robotics to test and develop new algorithms that can solve the real-world, high-impact challenges in autonomous surgery.

* 7 pages, 9 Figures, submitted to IROS 2019 

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Motion Scaling Solutions for Improved Performance in High Delay Surgical Teleoperation

Feb 08, 2019
Florian Richter, Ryan K. Orosco, Michael C. Yip

Robotic teleoperation brings great potential for advances within the field of surgery. The ability of a surgeon to reach patient remotely opens exciting opportunities. Early experience with telerobotic surgery has been interesting, but the clinical feasibility remains out of reach, largely due to the deleterious effects of communication delays. Teleoperation tasks are significantly impacted by unavoidable signal latency, which directly results in slower operations, less precision in movements, and increased human errors. Introducing significant changes to the surgical workflow, for example by introducing semi-automation or self-correction, present too significant a technological and ethical burden for commercial surgical robotic systems to adopt. In this paper, we present three simple and intuitive motion scaling solutions to combat teleoperated robotic systems under delay and help improve operator accuracy. Motion scaling offers potentially improved user performance and reduction in errors with minimal change to the underlying teleoperation architecture. To validate the use of motion scaling as a performance enhancer in telesurgery, we conducted a user study with 17 participants, and our results show that the proposed solutions do indeed reduce the error rate when operating under high delay.

* 6 pages, 6 figures, ICRA 2019 Accepted 

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Variational Boosting: Iteratively Refining Posterior Approximations

Feb 19, 2017
Andrew C. Miller, Nicholas Foti, Ryan P. Adams

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that resulting posterior inferences compare favorably to existing posterior approximation algorithms in both accuracy and efficiency.

* 25 pages, 9 figures, 2 tables 

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Mixtures of Shifted Asymmetric Laplace Distributions

Dec 21, 2012
Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas

A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian distribution. This approach is mathematically elegant and relatively computationally straightforward. Our novel mixture modelling approach is demonstrated on both simulated and real data to illustrate clustering and classification applications. In these analyses, our mixture of shifted asymmetric Laplace distributions performs favourably when compared to the popular Gaussian approach. This work, which marks an important step in the non-Gaussian model-based clustering and classification direction, concludes with discussion as well as suggestions for future work.

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Elliptical slice sampling

Mar 19, 2010
Iain Murray, Ryan Prescott Adams, David J. C. MacKay

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.

* 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6). Differences from first submission: some minor edits in response to feedback. 

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Compositional Temporal Visual Grounding of Natural Language Event Descriptions

Dec 04, 2019
Jonathan C. Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng, Olga Russakovsky

Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions. Compositional modeling becomes central: we first ground atomic descriptions "girl eating an apple," "batter hitting the ball" to short video segments, and then establish the temporal relationships between the segments. This compositional structure enables models to recognize a wider variety of events not seen during training through recognizing their atomic sub-events. Explicit temporal modeling accounts for a wide variety of temporal relationships that can be expressed in language: e.g., in the description "girl stands up from the table after eating an apple" the visual ordering of the events is reversed, with first "eating an apple" followed by "standing up from the table." We leverage these observations to develop a unified deep architecture, CTG-Net, to perform temporal grounding of natural language event descriptions to videos. We demonstrate that our system outperforms prior state-of-the-art methods on the DiDeMo, Tempo-TL, and Tempo-HL temporal grounding datasets.

* Project page: 

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Robust Incremental State Estimation through Covariance Adaptation

Oct 11, 2019
Ryan M. Watson, Jason N. Gross, Clark N. Taylor, Robert C. Leishman

Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.

* 8 pages, 4 figures, 2 tables, submitted to IEEE Robotics and Automation Letters 

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Enabling Robust State Estimation through Measurement Error Covariance Adaptation

Jun 10, 2019
Ryan M. Watson, Jason N. Gross, Clark N. Taylor, Robert C. Leishman

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose one such technique that enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.

* 13 pages, 11 figures, Submitted to IEEE Transactions on Aerospace And Electronic Systems 

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Reducing Reparameterization Gradient Variance

May 22, 2017
Andrew C. Miller, Nicholas J. Foti, Alexander D'Amour, Ryan P. Adams

Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the "reparameterization trick," represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to use more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on non-conjugate multi-level hierarchical models and a Bayesian neural net where we observed gradient variance reductions of multiple orders of magnitude (20-2,000x).

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Augmented Reality Predictive Displays to Help Mitigate the Effects of Delayed Telesurgery

Feb 21, 2019
Florian Richter, Yifei Zhang, Yuheng Zhi, Ryan K. Orosco, Michael C. Yip

Surgical robots offer the exciting potential for remote telesurgery, but advances are needed to make this technology efficient and accurate to ensure patient safety. Achieving these goals is hindered by the deleterious effects of latency between the remote operator and the bedside robot. Predictive displays have found success in overcoming these effects by giving the operator immediate visual feedback. However, previously developed predictive displays can not be directly applied to telesurgery due to the unique challenges in tracking the 3D geometry of the surgical environment. In this paper, we present the first predictive display for teleoperated surgical robots. The predicted display is stereoscopic, utilizes Augmented Reality (AR) to show the predicted motions alongside the complex tissue found in-situ within surgical environments, and overcomes the challenges in accurately tracking slave-tools in real-time. We call this a Stereoscopic AR Predictive Display (SARPD). To test the SARPD's performance, we conducted a user study with ten participants on the da Vinci\textregistered{} Surgical System. The results showed with statistical significance that using SARPD decreased time to complete task while having no effect on error rates when operating under delay.

* 7 pages, 8 Figures, Accepted ICRA 2019 

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Parsimonious Shifted Asymmetric Laplace Mixtures

Nov 01, 2013
Brian C. Franczak, Paul D. McNicholas, Ryan P. Browne, Paula M. Murray

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An explicit two-stage parameter estimation procedure is described, and the Bayesian information criterion and the integrated completed likelihood are compared for model selection. This novel family of models is applied to real data, where it is compared to its Gaussian analogue within clustering and classification paradigms.

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View management for lifelong visual maps

Aug 09, 2019
Nandan Banerjee, Ryan C. Connolly, Dimitri Lisin, Jimmy Briggs, Manjunath Narayana, Mario E. Munich

The time complexity of making observations and loop closures in a graph-based visual SLAM system is a function of the number of views stored. Clever algorithms, such as approximate nearest neighbor search, can make this function sub-linear. Despite this, over time the number of views can still grow to a point at which the speed and/or accuracy of the system becomes unacceptable, especially in computation- and memory-constrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low quality images, or in a location whose appearance has changed. These views can be removed to improve the overall performance of a SLAM system. In this paper, we propose a method for pruning views in a visual SLAM system to maintain its speed and accuracy for long term use.

* IEEE International Conference on Intelligent Robots and Systems (IROS), 2019 

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Approximate Inference for Constructing Astronomical Catalogs from Images

Oct 12, 2018
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a Poisson random variable with a rate parameter that depends on the latent properties of stars and galaxies. These latent properties are themselves random, with prior distributions fitted by empirical Bayes. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). We demonstrate that the MCMC procedure excels at quantifying uncertainty while the VI procedure is 1000x faster. For the error metric we consider, both procedures outperform the current state-of-the-art method for measuring the colors, shapes, and morphologies of stars and galaxies. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores (1.3 million hardware threads) to construct an astronomical catalog from 50 terabytes of images

* major revision for AoAS 

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SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics

Sep 11, 2019
Yang Li, Florian Richter, Jingpei Lu, Emily K. Funk, Ryan K. Orosco, Jianke Zhu, Michael C. Yip

Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information that allows for mapping of a deformable surgical field while tracking rigid instruments within the field. To achieve this, a model-based tracker is employed to localize the surgical tool with a kinematic prior in conjunction with a model-free tracker to reconstruct the deformable environment and provide an estimated point cloud as a mapping of the environment. The proposed framework was implemented on the da Vinci Surgical System in real-time with an end-effector controller where the target configurations are set and regulated through the framework. Our proposed framework successfully completed autonomous soft tissue manipulation tasks with high accuracy. The demonstration of this novel framework is promising for the future of surgical autonomy. In addition, we provide our dataset for further surgical research.

* The first two authors made equal contribution on this paper 

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Recurrent switching linear dynamical systems

Oct 26, 2016
Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dynamical systems (SLDS), we present a new model class that not only discovers these dynamical units, but also explains how their switching behavior depends on observations or continuous latent states. These "recurrent" switching linear dynamical systems provide further insight by discovering the conditions under which each unit is deployed, something that traditional SLDS models fail to do. We leverage recent algorithmic advances in approximate inference to make Bayesian inference in these models easy, fast, and scalable.

* 15 pages, 6 figures 

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Automating biomedical data science through tree-based pipeline optimization

Jan 28, 2016
Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

* 16 pages, 5 figures, to appear in EvoBIO 2016 proceedings 

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