From macglashan at tti-c.org Wed Jul 8 13:26:01 2009 From: macglashan at tti-c.org (Julia MacGlashan) Date: Wed Jul 8 13:26:12 2009 Subject: [TTIC Colloquium] TTI-C Talk: David Sontag, MIT Message-ID: <871D196C2CE84D319923E4DB9CCEDB3F@jmacglDPLFYD1> When: Monday, July 13 @ 11:00 AM Where: TTI-C Conference Room #526, 6045 S Kenwood Ave, 5th Floor Who: David Sontag, MIT Title: Approximate Inference in Graphical Models using LP Relaxations Graphical models such as Markov random fields have been successfully applied to a wide variety of fields, from computer vision and natural language processing, to computational biology. Exact probabilistic inference is generally intractable in complex models having any dependencies between the variables. In this talk, I will discuss recent work on using linear programming relaxations to perform approximate inference. By solving the LP relaxations in the dual, we obtain efficient message-passing algorithms that, when the relaxations are tight, can provably find the most likely (MAP) configuration. Our algorithms succeed at finding the MAP configuration in protein side-chain placement, protein design, and stereo problems. Finally, many interesting questions arise when we attempt to do learning using approximate inference algorithms. I will discuss recent results on learning for structured prediction using LP relaxations. Joint work with Tommi Jaakkola, Amir Globerson, Yair Weiss, and Talya Meltzer. Contact: David McAllester, TTI-C mcallester@tti-c.org 702-5562 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://ttic.uchicago.edu/pipermail/colloquium/attachments/20090708/f81ec1c6/attachment.htm From macglashan at tti-c.org Thu Jul 16 10:28:39 2009 From: macglashan at tti-c.org (Julia MacGlashan) Date: Thu Jul 16 10:28:53 2009 Subject: [TTIC Colloquium] TTI-C Colloquium: Konrad Koerding, Northwestern Message-ID: <2868B9FA45F148E4A683A547D6D44863@jmacglDPLFYD1> When: Monday, July 20th @ 2:00pm Where: 6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor) Who: Konrad Koerding, Rehabilitation Institute of Chicago, Northwestern University Title: Perception as Bayesian inference about causes Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception. Contact: Greg Shakhnarovich, TTI-C greg@tti-c.org 834-2572 Schedule: http://www.tti-c.org/colloquium.php -------------- next part -------------- An HTML attachment was scrubbed... URL: http://ttic.uchicago.edu/pipermail/colloquium/attachments/20090716/491e0124/attachment.htm From macglashan at tti-c.org Mon Jul 20 12:47:03 2009 From: macglashan at tti-c.org (Julia MacGlashan) Date: Mon Jul 20 12:47:19 2009 Subject: [TTIC Colloquium] TTI-C Colloquium: Konrad Koerding, Northwestern Message-ID: <41A3AE856AA74AF4A1E8922489248C8B@jmacglDPLFYD1> REMINDER When: Monday, July 20th @ 2:00pm Where: 6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor) Who: Konrad Koerding, Rehabilitation Institute of Chicago, Northwestern University Title: Perception as Bayesian inference about causes Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception. Contact: Greg Shakhnarovich, TTI-C greg@tti-c.org 834-2572 Schedule: http://www.tti-c.org/colloquium.php -------------- next part -------------- An HTML attachment was scrubbed... URL: http://ttic.uchicago.edu/pipermail/colloquium/attachments/20090720/8a59d9aa/attachment-0001.htm From macglashan at tti-c.org Mon Jul 27 14:46:10 2009 From: macglashan at tti-c.org (Julia MacGlashan) Date: Mon Jul 27 14:46:22 2009 Subject: [TTIC Colloquium] Wonseok Dissertation Defense, 8-10-09 Message-ID: <08FA6770129C4D4D8A1D38C765A78458@jmacglDPLFYD1> *** Toyota Technological Institute at Chicago *** *** Dissertation Defense *** Candidate: Wonseok Chae Date: Monday, August 10, 2009 Time and Location: 10:30 a.m. in TTI-C Conference Room #526 Title: Type Safe Extensible Programming Abstract: Software products evolve over time. Sometimes they evolve by adding new features, and sometimes by either fixing bugs or replacing outdated implementations with new ones. When software engineers fail to anticipate such evolution during development, they will eventually be forced to re-architect or re-build from scratch. Therefore, it has been common practice to prepare for changes so that software products are extensible over their lifetimes. However, making software extensible is challenging because it is difficult to anticipate successive changes and to provide adequate abstraction mechanisms over potential changes. Such extensibility mechanisms, furthermore, should not compromise any existing functionality during extension. Software engineers would benefit from a tool that provides a way to add extensions in a reliable way. It is natural to expect programming languages to serve this role. Extensible programming is one effort to address these issues. In my work, I present type safe extensible programming using the MLPolyR language. MLPolyR is an ML-like functional language whose type system provides type-safe extensibility mechanisms at several levels. After presenting the language, I will show how these extensibility mechanisms can be put to good use in the context of product line engineering. Product line engineering is an emerging software engineering paradigm that aims to manage variations, which originate from successive changes in software. Candidate's Advisor: Prof. Matthias Blume A draft copy of Wonseok Chae' dissertation will be available soon in TTI-C library From macglashan at tti-c.org Tue Jul 28 08:57:23 2009 From: macglashan at tti-c.org (Julia MacGlashan) Date: Tue Jul 28 08:57:42 2009 Subject: [TTIC Colloquium] TTI-C Colloquium: Jacob Goldberger (Bar-Ilan University, Israel) References: <9BFA4FFE1ACE407581765CA2326E1547@jmacglDPLFYD1> Message-ID: <456CC05C980543228A65DD1BE77ECE35@jmacglDPLFYD1> When: Monday, Aug 3 @ 2:00pm Where: 6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor) Who: Jacob Goldberger (Bar-Ilan University, Israel) Title: An effective belief-propagation algorithm for constrained linear least-squares problems In this talk we propose a new algorithm for the linear least squares problem where the unknown variables are constrained to be in a finite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, applying the Belief Propagation algorithm yields very poor results. The algorithm described here is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the modified loop-free factor graph outperforms current methods in terms of both performance and complexity. Joint work with Amir Leshem. Contact: Nati Srebro, TTI-C nati@tti-c.org 834-7493 Speaker Schedule: http://www.tti-c.org/colloquium.php -------------- next part -------------- A non-text attachment was scrubbed... Name: winmail.dat Type: application/ms-tnef Size: 12798 bytes Desc: not available Url : http://ttic.uchicago.edu/pipermail/colloquium/attachments/20090728/c70295d5/winmail.bin