- talks - lists.cs.princeton.edu

TODAY: Physics Colloquium, 4:30 p.m. Thurs. Feb. 11; Sebastian Seung (Princeton), “Structure and function of the retina,” Jadwin A-10
by Nicole E. Wagenblast 11 Feb '16

11 Feb '16

Princeton University Department of Physics Hamilton Colloquium Series Speaker: Sebastian Seung Neuroscience Institute and Computer Science Department Princeton University “Structure and function of the retina” The importance of determining and interpreting the structures of atoms, crystals, and proteins was established long ago. I will discuss current efforts to extend this paradigm to the brain, focusing on new discoveries concerning the structure of the retina gained through 3D electron microscopy, computer vision, and crowdsourcing. I will also describe how such structural information is helpful for understanding how the retina carries out the first steps of visual perception. 4:30 p.m., Thursday, February 11, 2016 Jadwin Hall, Room A-10 Host: N. Phuan Ong , Princeton University Tea at 4 p.m., Lounge Area outside Jadwin A-10

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Katherine Edwards will present her Pre-FPO on Tuesday, February 16, 2016 at 11am in CS 302.
by Nicki Gotsis 10 Feb '16

10 Feb '16

Katherine Edwards will present her Pre-FPO on Tuesday, February 16, 2016 at 11am in CS 302. The members of her committee are: Paul Seymour (advisor), Maria Chudnovsky and Chun-Hung Liu; (readers - Math Dept) and Bernard Chazelle and Bob Tarjan (non-readers). Everyone is invited to attend her talk. The talk title and abstract follow below: Title: A collection of results in graph theory In this thesis, we present several results in graph theory. In this talk, we will highlight four of them. First, we discuss two results about edge colouring. In 1966 Tutte conjectured that every two-connected cubic graph without the Petersen graph as a minor is three-edge colourable. This statement generalizes the celebrated four-colour theorem. In previous work, Seymour et al. showed that to prove Tutte’s conjecture, it is sufficient to prove it for apex graphs (graphs which can be made planar by deleting a vertex) and doublecross graphs (graphs which can be drawn in the plane with just two crossings, in the infinite region). Sanders and Thomas recently solved the apex case. With Sanders, Seymour and Thomas, we give a proof for the doublecross case. Seymour conjectured in 1973 that every k-regular planar graph can be k-edge-coloured if and only if for every odd subset X of vertices, there are at least k edges between X and its complement. When k=3, this is the statement of the Four Colour theorem, and the conjecture had been proved for values of k up to 7. In joint work with Chudnovsky, Kawarabayashi and Seymour we give a proof for the case k=8 and a new, simpler proof for the case k=7. Next, we will consider fractional vertex colourings of graphs not containing large cliques. Fractional colouring is a generalization of graph colouring in which vertices are assigned sets of colours so that every pair of adjacent vertices receive disjoint sets. The fractional chromatic number is bounded above by the maximum degree \Delta, but in graphs which do not contain a clique of size Delta we can generally find stronger upper bounds. We will discuss some progress, joint with King, in bounding the fractional chromatic number of K_\Delta -free graphs away from \Delta. Our method relies on a local strengthening of the fractional relaxation of Reed's \omega, \Delta, \chi conjecture which roughly stipulates that if a graph has high fractional chromatic number then it must contain a vertex of high degree and a large clique in the same local area of the graph. We have also obtained new, stronger local bounds on the fractional chromatic number. Finally, we discuss some work with applications in large graph analytics. The hyperbolicity of a graph is an invariant which roughly measures the hyperbolicity of the metric space induced by its associated distance metric. Many real-world networks have been shown experimentally to have a small hyperbolicity constant. Such graphs also tend to be so large that superlinear-time algorithms are highly impractical, so it is interesting to design fast approximation algorithms for optimization problems on graphs with a fixed hyperbolicity. We investigate the $p$-centre problem, a popular clustering technique wherein we seek to locate $p$ centres on the graph for which the maximum distance from a vertex to its closest centre (this distance is called the $p$-radius $r_p$) is minimized. With Kennedy and Saniee, we give a combinatorial linear-time approximation algorithm to determine $p$-centers with an additive error on $r_p$ of at most 3 times its hyperbolic constant. This improves on the previously best-known quadratic time algorithm.

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CSML Seminar: Tong Zhang, Tuesday, February 16, 2016 at 12:30pm-Computer Science Building, Room 105
by Nicole E. Wagenblast 10 Feb '16

10 Feb '16

Tong Zhang-Rutgers University CSML Seminar Tuesday, February 16, 2016 12:30pm-1:30pm Computer Science Building, Room 105 * Lunch will be provided * Title : Statistical Thinking for Modern Big Data Optimization Abstract : Many modern big-data machine learning problems encountered in the industry involve optimization problems so large that traditional methods are difficult to handle. The complex issues in solving these large scale problems have stimulated fast development of a new set of optimization techniques in recent years. In particular, I will demonstrate my work in the past few years on novel applications of statistical thinking to efficiently solve these large scale optimization problems, including statistical sampling for optimization, modern stochastic optimization algorithms, and large scale distributed computing. If you would like to be added to the CSML listserv, please email capizzi(a)princeton.edu Thanks, Joseph D. Capizzi Jr. Administrative Assistant to the Director Center for Statistics and Machine Learning Green Hall, 3-C-5 Princeton University Princeton, NJ 08544 capizzi(a)princeton.edu 609-258-9862

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Reminder: Colloquium Speaker Samy Bengio, Wed Feb 9, 4:30pm
by Nicole E. Wagenblast 10 Feb '16

10 Feb '16

Colloquium Speaker Samy Bengio, Google Wednesday, February 10, 4:30 pm Computer Science 105 Neural Image Captioning Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this talk, I'll present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. If time permits, I'll also describe an improvement on our basic image captioning approach that considers the discrepancy between how we train these models and how we actually use them at inference time, and how adding some exploration during training mitigates this problem. Joint work with Oriol Vinyals, Alex Toshev, Dumitru Erhan, Navdeep Jaitly and Noam Shazeer. Samy Bengio (PhD in computer science, University of Montreal, 1993) is a research scientist at Google since 2007. Before that, he was senior researcher in statistical machine learning at IDIAP Research Institute since 1999. His most recent research interests are in machine learning, in particular deep learning, large scale online learning, image ranking and annotation, music and speech processing. He is action editor of the Journal of Machine Learning Research and on the editorial board of the Machine Learning Journal. He was associate editor of the journal of computational statistics, general chair of the Workshops on Machine Learning for Multimodal Interactions (MLMI'2004-2006), programme chair of the International Conference on Learning Representations (ICLR'2015-2016), programme chair of the IEEE Workshop on Neural Networks for Signal Processing (NNSP'2002), chair of BayLearn (2012-2015), and several times on the programme committee of several international conferences such as NIPS, ICML, ECML and ICLR. More information can be found on his website: http://bengio.abracadoudou.com .

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Colloquium Speaker David Chu, Monday, Feb 15, 12:30pm
by Joseph Crouthamel 09 Feb '16

09 Feb '16

Colloquium Speaker David Chu, MSR Monday, Feb 15th, 12:30pm Room CS 105 Title: In Pursuit of Low-Latency Interactions on Mobile Devices Abstract: Human attention is a scarce resource, but when available, it can also be wonderfully perceptive. My research seeks to understand: what does it take for mobile devices --- power-constrained as they are --- to operate at the speed of human perception? And what new opportunities emerge as a result? Via a pair of vignettes, I illustrate two such low-latency mobile systems. The first focuses on app streaming, an emerging app execution model in which remote servers execute logic and rendering on behalf of thin clients. App streaming promises any device access to any app at any time. Unfortunately, the reality is that wide-area network latencies often exceed thresholds above which many interactive apps such as games tend to be deemed too slow. In response, I describe Outatime, a speculative execution system for app streaming that masks network latency. In Outatime, the server renders speculative frames of future possible outcomes, delivering them to the client one entire roundtrip early, and recovers quickly from mis-speculations when they occur. Clients perceive little latency. Outatime has been implemented on two high-quality, commercially-released twitch-based games. Users report strongly preferring Outatime to standard streaming, since Outatime delivers real-time interactivity as fast as --- and in some cases, even faster than --- traditional local client-side execution. In a second example of low-latency interaction, I describe a Kinect-like device tracking system, FAR. Unlike Kinect, FAR is portable, requiring only the phones in our hands. Yet FAR performs continuous, fast and accurate phone-to-phone localization that matches the (often very fast) speed and sensitivity of human movement. In fact, FAR's accuracy is comparable to --- and in some cases, even superior to --- that of Kinect. Lab trials and many real world deployments indicate that FAR can fully support dynamic human motion in real-time. Bio: David Chu is a researcher in Microsoft's Mobility and Networking Research Group. His research interests are in mobile systems and applications, cyber-physical systems, sensing systems, ubiquitous computing and applied machine learning. The main thrust of David's current work is toward low-latency perception-aligned mobile systems. He received the Best Paper award in MobiSys 2015, the Best Paper nomination in MobiSys 2012, the Best Demo award in MobiSys 2014, and the Best Demo nomination in SenSys 2011. David's research has appeared on multiple occasions in tech news such as TechCrunch, PC Magazine, GameSpot, Ars Technia, Slashdot, The Verge, Engadget and Wired. At Microsoft, David has contributed to Windows and Windows Phone, Xbox and HoloLens. David received his B.S. from the University of Virginia in 2004; and his M.S. and Ph.D. from the University of California, Berkeley in 2005 and 2009, respectively, while an NSF Graduate Research Fellow.

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Colloquium Speaker Samy Bengio, Wed Feb 9, 4:30pm
by Nicole E. Wagenblast 09 Feb '16

09 Feb '16

Colloquium Speaker Samy Bengio, Google Wednesday, February 10, 4:30 pm Computer Science 105 Neural Image Captioning Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this talk, I'll present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. If time permits, I'll also describe an improvement on our basic image captioning approach that considers the discrepancy between how we train these models and how we actually use them at inference time, and how adding some exploration during training mitigates this problem. Joint work with Oriol Vinyals, Alex Toshev, Dumitru Erhan, Navdeep Jaitly and Noam Shazeer. Samy Bengio (PhD in computer science, University of Montreal, 1993) is a research scientist at Google since 2007. Before that, he was senior researcher in statistical machine learning at IDIAP Research Institute since 1999. His most recent research interests are in machine learning, in particular deep learning, large scale online learning, image ranking and annotation, music and speech processing. He is action editor of the Journal of Machine Learning Research and on the editorial board of the Machine Learning Journal. He was associate editor of the journal of computational statistics, general chair of the Workshops on Machine Learning for Multimodal Interactions (MLMI'2004-2006), programme chair of the International Conference on Learning Representations (ICLR'2015-2016), programme chair of the IEEE Workshop on Neural Networks for Signal Processing (NNSP'2002), chair of BayLearn (2012-2015), and several times on the programme committee of several international conferences such as NIPS, ICML, ECML and ICLR. More information can be found on his website: http://bengio.abracadoudou.com .

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Physics Colloquium, Thurs. Feb. 11; Sebastian Seung (Princeton), “Structure and function of the retina,” 4:30 p.m., Jadwin A-10
by Nicole E. Wagenblast 05 Feb '16

05 Feb '16

Princeton University Department of Physics Hamilton Colloquium Series Sebastian Seung, Neuroscience Institute and Computer Science Department, Princeton University 4:30 p.m., Thursday, February 11, 2016 Jadwin Hall, Room A-10 Host: N. Phuan Ong , Princeton University Tea at 4 p.m., Lounge Area outside Jadwin A-10 Structure and function of the retina The importance of determining and interpreting the structures of atoms, crystals, and proteins was established long ago. I will discuss current efforts to extend this paradigm to the brain, focusing on new discoveries concerning the structure of the retina gained through 3D electron microscopy, computer vision, and crowdsourcing. I will also describe how such structural information is helpful for understanding how the retina carries out the first steps of visual perception.

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CSML Seminar: Susan Murphy, Tuesday, February 9, 2016 at 12:30pm | Computer Science Building, Room 105
by Nicole E. Wagenblast 02 Feb '16

02 Feb '16

Susan Murphy-University of Michigan CSML Seminar Tuesday, February 9, 2016 12:30pm-1:30pm Computer Science Building, Room 105 * Lunch will be provided * Title: “Learning treatment policies in mobile health” Abstract: We describe a sequence of steps that facilitate effective learning of treatment policies in mobile health. These include a clinical trial with associated sample size calculator and data analytic methods. An off-policy Actor-Critic algorithm is developed for learning a treatment policy from this clinical trial data. Open problems abound in this area, including the development of a variety of online predictors of risk of health problems, missing data and disengagement. If you would like to be added to the CSML listserv, please email capizzi(a)princeton.edu . Thanks, Joseph D. Capizzi Jr. Administrative Assistant to the Director Center for Statistics and Machine Learning Green Hall, 3-C-5 Princeton University Princeton, NJ 08544 capizzi(a)princeton.edu 609-258-9862

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Ankit Garg will present his Pre-FPO on Thursday, February 4, 2016 at 1:30 pm in CS 402. (UPDATE: change of committee member)
by Nicki Gotsis 02 Feb '16

02 Feb '16

Ankit Garg will present his Pre-FPO on Thursday, February 4 at 1:30 pm in CS 402. The members of his committee are Mark Braverman (adviser), Bernard Chazelle, Zeev Dvir, Ran Raz (IAS) and Avi Wigderson (IAS). Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Information theoretic relaxations in complexity theory Abstract: Since Shannon’s “A Mathematical Theory of Communication”, information theory has found applicability in a wide range of scientific disciplines. Over the past two decades, information theory has reemerged in theoretical computer science as a mathematical tool with applications to streaming algorithms, data structures, communication complexity etc. In the first part of talk, I will talk about a new information theoretic framework for proving parallel repetition of 2-prover 1-round games. Using this framework, we can resolve the open question about parallel repetition of small value games. This is based on joint work with Mark Braverman. In the second part of the talk, I will talk about near optimal round-communication tradeoffs for quantum communication complexity of disjointness. The techniques involved in the proof include quantum information complexity and the self

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Sema Berkiten will present her Pre FPO on Monday, February 8, 2016 at 10:00am in CS 401
by Nicki Gotsis 02 Feb '16

02 Feb '16

Sema Berkiten will present her Pre FPO on Monday, February 8, 2016 at 10:00am in CS 401. The members of her committee are: Szymon Rusinkiewicz (adviser), Adam Finkelstein (reader), Hao Li (USC, reader), Thomas Funkhouser (non-reader) and David Dobkin (non-reader). Title: Capturing, Processing, and Synthesizing Surfaces with Details Abstract: Geometry acquisition has become increasingly popular in computer graphics and vision, with demand for high-quality models driven by advances in 3D printing, realistic real-time renderings of 3D characters in video games, digital libraries for historical objects etc. In this thesis, we focus on techniques to produce and process detailed geometry including acquisition of real world objects, processing and combining the captured data, and synthesizing new surfaces from existing ones. First, we summarize a 2D acquisition technique called photometric stereo to capture high resolution surface details. As a validation step, we solve a misalignment problem for photometric datasets which may be caused by perturbations to the camera or to the object, or the effect of optical image stabilization, especially in long photo shoots. After the dataset is validated, the surface normals can be computed using one of the photometric stereo algorithms. In order to decide which algorithm to use, we present a synthetic photometric benchmark to evaluate various algorithms for different scenarios. Next, we introduce a semi-automated system to convert photometric datasets into geometry-aware non-photorealistic illustrations of surface details that obey the common conventions of epigraphy (black-and-white archaeological drawings of inscriptions). This system is composed of rectification of the surface normals to correct camera perspective, segmentation of the inscriptions from the background, classification of the inscription based on carving technique (either slightly deeper grooves, or shallow pecked-out regions), and stylization of the inscriptions in various styles. To produce a detailed surface in 3D, we propose an approach to combine a rough 3D geometry with fine detailed normal maps obtained from different views. We begin with unaligned 2D normal maps and a rough 3D geometry, and automatically optimize the alignments through a two-step iterative registration algorithm to align each normal map to the 3D geometry. We then map the normals onto the surface, correct and seamlessly blend them together. At the end, we optimize the geometry to produce a high-quality 3D model that incorporates the high-frequency details from the normal maps. Finally, we present an algorithm for realistically transferring surface details (specifically, displacement maps) from existing high-quality 3D models to simple shapes that may be created with easy-to-learn modeling tools. Our key insight is to use metric learning to find a combination of geometric features that successfully predicts detail-map similarities on the source mesh, and use the learned feature combination to drive the detail transfer.

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