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Sibi Venkatesan

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3 papers

2 author rows

AAAI Conference 2020 Short Paper

Robust Multi-View Representation Learning (Student Abstract)

Sibi Venkatesan
James K. Miller
Artur Dubrawski

Multi-view data has become ubiquitous, especially with multi-sensor systems like self-driving cars or medical patientside monitors. We propose two methods to approach robust multi-view representation learning with the aim of leveraging local relationships between views. The ﬁrst is an extension of Canonical Correlation Analysis (CCA) where we consider multiple one-vs-rest CCA problems, one for each view. We use a group-sparsity penalty to encourage ﬁnding local relationships. The second method is a straightforward extension of a multi-view AutoEncoder with view-level drop-out. We demonstrate the effectiveness of these methods in simple synthetic experiments. We also describe heuristics and extensions to improve and/or expand on these methods.

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IJCAI Conference 2017 Conference Paper

Scaling Active Search using Linear Similarity Functions

Sibi Venkatesan
James K. Miller
Jeff Schneider
Artur Dubrawski

Active Search has become an increasingly useful tool in information retrieval problems where the goal is to discover as many target elements as possible using only limited label queries. With the advent of big data, there is a growing emphasis on the scalability of such techniques to handle very large and very complex datasets. In this paper, we consider the problem of Active Search where we are given a similarity function between data points. We look at an algorithm introduced by Wang et al. [Wang et al. , 2013] known as Active Search on Graphs and propose crucial modifications which allow it to scale significantly. Their approach selects points by minimizing an energy function over the graph induced by the similarity function on the data. Our modifications require the similarity function to be a dot-product between feature vectors of data points, equivalent to having a linear kernel for the adjacency matrix. With this, we are able to scale tremendously: for n data points, the original algorithm runs in O(n^2) time per iteration while ours runs in only O(nr + r^2) given r-dimensional features. We also describe a simple alternate approach using a weighted-neighbor predictor which also scales well. In our experiments, we show that our method is competitive with existing semi-supervised approaches. We also briefly discuss conditions under which our algorithm performs well.

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IROS Conference 2013 Conference Paper

A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario

John Schulman
Ankush Gupta
Sibi Venkatesan
Mallory Tayson-Frederick
Pieter Abbeel

Suturing is an important yet time-consuming part of surgery. A fast and robust autonomous procedure could reduce surgeon fatigue, and shorten operation times. It could also be of particular importance for suturing in remote tele-surgery settings where latency can complicate the master-slave mode control that is the current practice for robotic surgery with systems like the da Vinci®. We study the applicability of the trajectory transfer algorithm proposed in [12] to the automation of suturing. The core idea of this procedure is to first use non-rigid registration to find a 3D warping function which maps the demonstration scene onto the test scene, then use this warping function to transform the robot end-effector trajectory. Finally a robot joint trajectory is generated by solving a trajectory optimization problem that attempts to find the closest feasible trajectory, accounting for external constraints, such as joint limits and obstacles. Our experiments investigate generalization from a single demonstration to differing initial conditions. A first set of experiments considers the problem of having a simulated Raven II system [5] suture two flaps of tissue together. A second set of experiments considers a PR2 robot performing sutures in a scaled-up experimental setup. The simulation experiments were fully autonomous. For the real-world experiments we provided human input to assist with the detection of landmarks to be fed into the registration algorithm. The success rate for learning from a single demonstration is high for moderate perturbations from the demonstration's initial conditions, and it gradually decreases for larger perturbations.

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