The AI seminar will meet weekly for lectures by graduate students,
faculty, and researchers emphasizing work-in-progress and recent
results in AI research. Lunch will be served starting at noon, with
the talks running between 12:15 and 1:15. The new format is designed
to allow AI chit-chat before the talks begin. Also, we're trying to
make some of the presentations less formal so that students and
faculty will feel comfortable using the seminar to give presentations
about work in progress or practice talks for conferences.
|
Date |
Title/Speaker/Abstract/Host |
| September 3rd | Speaker: Chris J. Burges, Microsoft Host: Thorsten Joachims Title : Ranking Research Abstract : Algorithms that learn to rank a large corpus of documents, given a query, form a core methodology in today's major Search Engines. I will describe the approach that won Track 1 of Yahoo!'s recent Learning to Rank Challenge (ranking for Web search). Our system is based on a general approach for learning arbitrary information retrieval measures. I'll then briefly touch on some desiderata for our near term work on learning to rank, which suggest new research directions. Finally I will describe a process that the Machine Learning Group at MSR Redmond is currently exploring in an attempt to identify long term, collaborative research ventures. “The AI-Seminar is sponsored by Yahoo!” |
| September 10th |
Speaker: Doug Turnbull , Assistant Professor at Ithaca College Host: Thorsten Joachims Bio : Doug Turnbull is currently a new assistant professor in the Department of Computer Science at Ithaca College. His main research interests are multimedia information retrieval, computer audition, machine learning, and human computation. Title : Semantic Music Discovery Engine Abstract : Most commercial music discovery engines (including Apple iTunes Genius and Last.fm) rely on the analysis of social information (e.g., user preferences, blogs, or social tagging data, etc.) to help people find music. These systems are not ideal in that they suffer from popularity bias and the “cold start” problem. To remedy these problems, researchers have been exploring content-based audio analysis as an alternative. However, state-of-the-art content-based systems often produce less-than-accurate annotations of music. It seems natural that we can improve music discovery by combining social and acoustic sources of music information. “The AI-Seminar is sponsored by Yahoo!” |
| September 17th |
Speaker: Ashutosh Saxena, Cornell University Title : "Make3D: Single Image Depth Perception and its applications to Robotics" Abstract : In this talk, I will talk about some of my recent learning algorithms that enable a robot to perceive its environment. In particular, we will first consider the problem of converting standard digital pictures into 3D models. This is a challenging problem, since an image is formed by a projection of the 3D scene onto two dimensions, thus losing the depth information. We take a supervised learning approach to this problem, and model the scene depth as a function of the image features. We show that, even on unstructured scenes of a large variety of environments, our algorithm is frequently able to recover accurate 3D models. (See http://make3d.cs.cornell.edu ) We then look at the problem of combining our learning algorithm of single image depth estimation with other related sub-tasks in scene understanding (such as scene categorization, object detection). These sub-tasks operate on the same raw data and provide correlated outputs. The last few decades have seen great progress in tackling each of these problems in isolation. Only, recently have researchers returned to the difficult task of considering them jointly. We consider learning a set of related models in such that they both solve their own problem and help each other. Our method requires only a limited “black box” interface with the models, allowing us to use very sophisticated, state-of-the-art classifiers without having to look under the hood. We then apply our methods to robotics applications: (a) obstacle avoidance for autonomously driving a small electric car at high speeds through cluttered environments, and (b) robot manipulation, where we develop learning algorithms for grasping novel objects. This enables our robot to perform tasks such as open new doors, clear up cluttered tables, and unload items from a dishwasher. “The AI-Seminar is sponsored by Yahoo!” |
| September 24th |
Speaker: Alyosha Efros, CMU Title : Are Categories Necessary for Recognition? Abstract : The use of categories to represent concepts (e.g. visual objects) is so prevalent in computer vision and machine learning that most researchers don't give it a second thought. Faced with a new task, one simply carves up the solution space into classes (e.g. cars, people, buildings), assigns class labels to training examples and applies one of the many popular classifiers to arrive at a solution. In this talk, I will discuss a different way of thinking about object recognition -- not as object naming, but rather as object association. Instead than asking "What is it?", a better question might be "What is it like?"[M. Bar]. The etymology of the very word "re-cognize" (to know again) supports the view that association plays a key role in recognition. Under this model, when faced with a novel object, the task is to associate it with the most similar objects in one's memory which can then be used directly for knowledge transfer, bypassing the categorization step all-together. I will present some very preliminary results on our new model, termed "The Visual Memex", which aims to use object associations (in terms of visual similarity and spatial context) to reason about and parse visual scenes. We show that our model offers better performance at certain tasks than standard category-driven approaches. Joint work with Tomasz Malisiewicz. “The AI-Seminar is sponsored by Yahoo!” |
| October 1st |
Speaker: Ruslan Salakhutdinov, MIT Host: Ping Li Bio: Ruslan Salakhutdinov received his PhD from University of Toronto, and is now a postdoctoral fellow at CSAIL and the department of Brain and Cognitive Sciences at MIT. His broad research interests involve developing flexible large-scale probabilistic models that contain deep hierarchical structure. Much of his current research concentrates on learning expressive structured representations using hierarchical models with applications to transfer learning. His other interests include Bayesian inference, matrix factorization, and approximate inference and learning of large-scale graphical models. Title : Learning Probabilistic Models with Deep Hierarchical Structures Abstract : Building intelligent systems that are capable of extracting higher-order knowledge from high-dimensional data and successfully transferring that knowledge to learning new concepts lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. In this talk I will first introduce a broad class of probabilistic generative models called Deep Boltzmann Machines (DBMs) that contain many layers of latent variables. I will describe a new learning algorithm for this class of models that uses variational methods and Markov chain Monte Carlo (MCMC). This new learning algorithm, in addition to a bottom-up pass, can incorporate top-down feedback, which allows DBMs to better propagate uncertainty about ambiguous inputs. I will further show that these deep models can learn interesting representations and can be successfully applied in many application domains, including information retrieval, object recognition, and nonlinear dimensionality reduction. In the second part of the talk, I will describe new ways of developing more complex systems that combine Deep Boltzmann Machines with more structured hierarchical Bayesian models. I will show how these hybrid models can learn a deep hierarchical structure for sharing knowledge across hundreds of visual categories, which allows efficient learning of new categories from few, even just one, examples -- a problem known as 'one-shot learning'. “The AI-Seminar is sponsored by Yahoo!” |
| October 8th | Speaker: Jieping Ye, Arizona State University Host: Ping Li Date Change :*** WEDNESDAY October 6, 2010, 400pm - 5:00pm: Location: G01 Biotechnology Building Bio : Jieping Ye is an Associate Professor of the Department of Computer Science and Engineering at Arizona State University. He received his Ph.D. in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He won the outstanding student paper award at ICML in 2004, the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award at ASU in 2009, the NSF CAREER Award in 2010, and the KDD best research paper award honorable mention in 2010. Title : Large-Scale Structured Sparse Learning Abstract : Recent advances in high-throughput technologies have unleashed a torrent of data with a large number of dimensions. Examples include gene expression pattern images, microarray gene expression data, and neuroimages. Variable selection is crucial for the analysis of these data. In this talk, we consider the structured sparse learning for variable selection where the structure over the features can be represented as a hierarchical tree, an undirected graph, or a collection of disjoint or overlapping groups. We show that the proximal operator associated with these structures can be computed efficiently, thus accelerated gradient techniques can be applied to scale structured sparse learning to large-size problems. We demonstrate the efficiency and effectiveness of the presented algorithms using synthetic and real data. “The AI-Seminar is sponsored by Yahoo!” |
| October 15th | Speaker: Berkant Savas, University of Texas Host: Charles Van Loan Title : Tools for large scale social network and graph computations Abstract : In this talk we will present a few novel tools for the link prediction and group recommendation problems in social network analysis. In particular, our discussion will contain three specific topics. (1) We will describe a technique called clustered low rank approximation, that captures and maintains fundamental structure from the social network. The main advantage of this method is significantly better low rank approximations that are obtained in less (or equal) amount of computation time. The improvements in the low rank approximation translate to improvements in the main task at hand, which include link prediction or group recommendation. The memory usage in the clustered approach is the same as for corresponding standard low rank approximations. This procedure is an effective and highly scalable tool for various tasks in computational analysis of social networks. (2) Suppose we are given an affiliation network between a set of users and a set of communities or groups, i.e. certain users are connected to certain groups. The task is then to give group recommendations to users that they may be interested in joining. Often, in addition to the affiliation network between the users and communities, there is a separate social network between the users themselves. We will discuss a few methods on how to combine the social network with the affiliation network in order to improve the performance for group recommendation. (3) Finally, we will discuss supervised methods for link predictions that utilize multiple and heterogeneous sources of information. We will show experimental results with real-world and large scale data sets on all three topics in the discussion. "The AI-Seminar is sponsored by Yahoo!" |
| October 22nd | Speaker: Pannaga Shivaswamy, Post Doc Host: Thorsten Joachims Title : Large Relative Margin and Applications Abstract : Over the last decade or so, machine learning algorithms such as “The AI-Seminar is sponsored by Yahoo!” |
| October 29th | Speaker: Surya Singh, University of Sydney Host: Ashutosh Saxena Bio: Surya Singh is a Research Fellow at the Australian Centre for Field Robotics where he leads modeling and control efforts. His research interests in dynamic systems include: agile motion over terrain, motion analysis, and mechatronics design. Dr. Singh is a Fulbright Scholar and has studied at the Stanford University's Robotic Locomotion Lab, Tokyo Institute of Technology's Hirose Robotics Lab, and Carnegie Mellon University's Field Robotics Center. Title : Agile Robots: Learning to Steer Naturally Abstract : Human locomotion can be remarkably informative for robotics. When it comes to agility, it can not only guide the mechanics (the ``how''), but also the steering (the ``where''). While this seems deceivingly simple -- one just accelerates, decelerates, or turns -- for dynamic systems it is complicated by inertia, saturation, compliance, and even social bias (e.g., driving direction). Determining the motion involves solving a complex inverse control problem. Analytic solutions are difficult to characterize. An adaptive method is introduced that uses human navigation to inform the planning and subsequent control. This is then illustrated in the context of robots -- with applications from controlling mining robots to observing animals in the field. “The AI-Seminar is sponsored by Yahoo!” |
| November 5th | Speaker: Host: Bio : Title : Abstract : “The AI-Seminar is sponsored by Yahoo!” |
| November 12th | Speaker: Katharina Morik, Technical University Dortmund Host: Thorsten Joachims Title : Data Mining – Learning under Resource Constraints Abstract : Data Mining started in the nineties with the claim that real-world data collections as they are stored in data bases require less sophisticated and more scalable algorithms than the then dominating statistical routines. New tasks like frequent set mining occurred. At the same time, sophisticated pre-processing and sampling methods allowed data analysis to cope with large data sets. Currently, we are again challenged by data masses at an even larger scale, collected at distributed sites, in heterogeneous formats and by applications that demand real-time response. Storage, runtime, and execution time for real-time behavior are the constrained resources, which need to be handled by new learning methods. The talk will give an overview of learning under resource constraints and present applications that illustrate the new challenge, in more detail.
Implementing algorithms on GPGPUs is shortly discussed. “The AI-Seminar is sponsored by Yahoo!” |
| November 19th | Speaker: NO SEMINAR - ACSU lunch Host: Bio : Title : Abstract : “The AI-Seminar is sponsored by Yahoo!” |
| November 26th | Speaker: NO SEMINAR- Thanksgiving Break Host: Bio : Title : Abstract : “The AI-Seminar is sponsored by Yahoo!” |
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December 3rd
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Speaker: Host: Bio : Title : Abstract : “The AI-Seminar is sponsored by Yahoo!” |
| December 10th | Speaker: Host: Bio : Title : Abstract : “The AI-Seminar is sponsored by Yahoo!” |
See also the AI graduate study brochure.
Please contact any of the faculty below if you'd like to give a talk this semester. We especially encourage graduate students to sign up!
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