April 2016 - talks - lists.cs.princeton.edu

Colloquium Speaker: Andrej Karpathy Monday April 11, 12:30pm-reminder
by Nicole E. Wagenblast 10 Apr '16

10 Apr '16

Colloquium Speaker Andrej Karpathy, Stanford University Monday, April 11- 12:30pm Computer Science 105 Connecting Images and Natural Language Intelligent agents require the ability to perceive their environments, understand their high-level semantics, and communicate with humans. While computer vision has recently made great strides on visual recognition tasks, the predominant paradigm is to predict one or more fixed visual categories for each image. I will describe a line of work that significantly expands the vocabulary of our computer vision systems and allows them to express visual concepts in natural language, such as “a picture of a girl playing with a stack of legos”, or “a couple holding hands and walking on a beach”. In particular, the final model can take an image and both detect and describe in natural language all of its salient regions. My modeling techniques draw on recent advances in Deep Learning that allow us to construct and train neural networks with hundreds of millions of neurons that take raw images and map them directly to natural language sentences. I will show that the model generates qualitatively compelling results and quantitative evaluation and control experiments demonstrate the strength of this approach with respect to simpler baselines and previous methods. Andrej Karpathy is a Computer Science Ph.D. candidate at Stanford University working with Prof. Fei-Fei Li. He received his M.S. from University of British Columbia in Computer Science and his B.S from University of Toronto in Computer Science and Physics. He is interested in the intersection of computer vision, natural language processing and reinforcement learning with the aim of developing agents who can intelligently interact with humans in dynamic environments. His work was featured in the New York Times and MIT Technology Review. He helped develop and instruct a new Computer Science class at Stanford on Convolutional Neural Networks for Visual Recognition. In his spare time he develops Deep Learning libraries in Javascript and maintains websites that support more efficient meta-research.

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Colloquium Speaker Robert West, Friday April 15- 12:30pm
by Mitra D. Kelly 08 Apr '16

08 Apr '16

Colloquium Speaker Robert West, Infolab at Stanford University Friday April 15- 12:30pm Computer Science 105 Human Behavior in Networks Humans as well as information are organized in networks. Interacting with these networks is part of our daily lives: we talk to friends in our social network; we find information by navigating the Web; and we form opinions by listening to others and to the media. Thus, understanding, predicting, and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact. In this talk I will present some of my work in this area, focusing on (1) human navigation of information networks and (2) person-to-person opinions in social networks. Network navigation constitutes a fundamental human behavior: in order to make use of the information and resources around us, we constantly explore, disentangle, and navigate networks such as the Web. Studying navigation patterns lets us understand better how humans reason about complex networks and lets us build more human-friendly information systems. As an example, I will present an algorithm for improving website hyperlink structure by mining raw web server logs. The resulting system is being deployed on Wikipedia's full server logs at terabyte scale, producing links that are clicked 10 times as frequently as the average link added by human Wikipedia editors. Communication and coordination through natural language is another prominent human network behavior. Studying the interplay of social network structure and language has the potential to benefit both sociolinguistics and natural language processing. Intriguing opportunities and challenges have arisen recently with the advent of online social media, which produce large amounts of both network and natural language data. As an example, I will discuss my work on person-to-person sentiment analysis in social networks, which combines the sociological theory of structural balance with techniques from natural language processing, resulting in a machine learning model for sentiment prediction that clearly outperforms both text-only and network-only versions. I will conclude the talk by sketching interesting future directions for computational approaches to studying and enhancing human behavior in networks. Bio: Robert West is a sixth-year Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Previously, he obtained a Master's degree from McGill University in 2010 and a Diplom degree from Technische Universität München in 2007. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker: Olga Russakovsky, Thursday, April 14, 12:30pm
by Nicole E. Wagenblast 08 Apr '16

08 Apr '16

Colloquium Speaker Olga Russakovsky, Carnegie Mellon University Thursday, April 14- 12:30pm Computer Science 105 The human side of computer vision Intelligent agents acting in the real world need advanced vision capabilities to perceive, learn from, reason about and interact with their environment. In this talk, I will explore the role that humans play in the design and deployment of computer vision systems. Large-scale manually labeled datasets have proven instrumental for scaling up visual recognition, but they come at a substantial human cost. I will first briefly talk about strategies for making optimal use of human annotation effort for computer vision progress. However, no dataset can foresee all the visual scenarios that a real-world system might encounter. I will argue that seamlessly integrating human expertise at runtime will become increasingly important for open-world computer vision. I will introduce, and demonstrate the effectiveness of, a rigorous mathematical framework for human-machine collaboration. Looking ahead, in order for such collaborations to become practical, the computer vision algorithms we design will need to be both efficient and interpretable. I will conclude by presenting a new deep reinforcement learning model for human action detection in videos that is efficient, interpretable and more accurate than prior art, opening up new avenues for practical human-in-the-loop exploration. Olga Russakovsky recently completed her PhD in computer science at Stanford and is now a postdoctoral fellow at Carnegie Mellon University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. Her work was featured in the New York Times and MIT Technology Review. She served as a Senior Program Committee member for WACV’16, led the ImageNet Large Scale Visual Recognition Challenge effort for two years, and organized multiple workshops and tutorials on large-scale recognition at premier computer vision conferences ICCV’13, ECCV’14, CVPR’15, ICCV’15 and CVPR’16. In addition, she founded and directs the Stanford AI Laboratory’s outreach camp SAILORS (featured in Wired and published in SIGCSE’16) designed to expose high school students in underrepresented populations to the field of AI.

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Colloquium Speaker: Andrej Karpathy Monday April 11, 12:30pm
by Nicole E. Wagenblast 07 Apr '16

07 Apr '16

Colloquium Speaker Andrej Karpathy, Stanford University Monday, April 11- 12:30pm Computer Science 105 Connecting Images and Natural Language Intelligent agents require the ability to perceive their environments, understand their high-level semantics, and communicate with humans. While computer vision has recently made great strides on visual recognition tasks, the predominant paradigm is to predict one or more fixed visual categories for each image. I will describe a line of work that significantly expands the vocabulary of our computer vision systems and allows them to express visual concepts in natural language, such as “a picture of a girl playing with a stack of legos”, or “a couple holding hands and walking on a beach”. In particular, the final model can take an image and both detect and describe in natural language all of its salient regions. My modeling techniques draw on recent advances in Deep Learning that allow us to construct and train neural networks with hundreds of millions of neurons that take raw images and map them directly to natural language sentences. I will show that the model generates qualitatively compelling results and quantitative evaluation and control experiments demonstrate the strength of this approach with respect to simpler baselines and previous methods. Andrej Karpathy is a Computer Science Ph.D. candidate at Stanford University working with Prof. Fei-Fei Li. He received his M.S. from University of British Columbia in Computer Science and his B.S from University of Toronto in Computer Science and Physics. He is interested in the intersection of computer vision, natural language processing and reinforcement learning with the aim of developing agents who can intelligently interact with humans in dynamic environments. His work was featured in the New York Times and MIT Technology Review. He helped develop and instruct a new Computer Science class at Stanford on Convolutional Neural Networks for Visual Recognition. In his spare time he develops Deep Learning libraries in Javascript and maintains websites that support more efficient meta-research.

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Colloquium Speaker Roger Grosse, TODAY April 7th- 12:30pm
by Mitra D. Kelly 07 Apr '16

07 Apr '16

Colloquium Speaker Roger Grosse, the University of Toronto April 7th- 12:30pm Computer Science 105 Exploiting compositionality to explore a large space of model structures I will present flexible algorithms for model discovery and model fitting which apply to broad, open-ended classes of models, yet which also incorporate model-specific algorithmic insights. First, I will introduce a framework for building probabilistic models compositionally out of common modeling motifs, such as clustering, sparsity, and dimensionality reduction. This compositional framework yields a variety of existing models as special cases. We can flexibly perform posterior inference across this large, open-ended space of models by composing sophisticated inference algorithms carefully designed for the individual modeling motifs. An automatic structure search procedure over this space of models yields sensible analyses of datasets as diverse as motion capture, natural image patches, and Senate voting records, all using a single software package with no hand-tuned metaparameters. Applying a similar compositional structure search procedure to Gaussian Process models yields interpretable decompositions of diverse time series datasets and enables automatic generation of natural language reports. Finally, compositional structure search depends crucially on the estimation of intractable likelihoods. I will briefly outline an approach for obtaining precise likelihood estimates with rigorous tail bounds by sandwiching the true value between stochastic upper and lower bounds. BIO: Roger Grosse is a Postdoctoral Fellow in the University of Toronto machine learning group. He received his Ph.D. in computer science from MIT under the supervision of of Bill Freeman. He is a recipient of the NDSEG Graduate Fellowship, the Banting Postdoctoral Fellowship, and outstanding paper awards at the International Conference of Machine Learning (ICML) and the Conference for Uncertainty in AI (UAI). He is also a co-creator of Metacademy, an open-source web site for developing personalized learning plans in machine learning and related fields. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker Yin-Tat Lee, TODAY April 5th- 12:30pm
by Mitra D. Kelly 05 Apr '16

05 Apr '16

Colloquium Speaker Yin-Tat Lee, Massachusetts Institute of Technology April 5th, 12:30pm Computer Science 105 Title: Faster algorithms for fundamental convex problems and their applications in combinatorial optimization Abstract: Convex optimization has been studied extensively and is a prominent tool in various areas such as combinatorial optimization, data analysis, operations research, and scientific computing. Each field has developed specialized tools including data structures, sampling methods, and dimension reduction. In the past several years, I have been combining and improving the optimization techniques from different fields to design faster optimization algorithms. In this talk, I will discuss my work in this direction and illustrate it through my results on linear programming and general convex optimization. In particular, I will present a new algorithm for solving linear programs, which gives the first improvement to the running time for linear programming in 25 years. Then, I will present the first nearly cubic time algorithm for solving general convex optimization problems. Furthermore, I will discuss how these two results can be used to improve the running time of many classical combinatorial problems such as maximum flow and submodular function minimization. This talk will assume no prior knowledge of optimization. Bio: Yin Tat Lee is a Ph.D. candidate in the department of mathematics at the Massachusetts Institute of Technology. He is interested in designing faster algorithms, particularly for problems in optimization. Since he began his Ph.D. in 2012, he has combined ideas from continuous and discrete mathematics to substantially advance the state-of-the-art for solving many fundamental problems in computer science, such as linear programming, maximum flow, and submodular function minimization. He has received a variety of awards, including the Best Student Paper Award at FOCS 2015, Best Paper Award at SODA 2014, Best Paper Award and Best Student Paper Award at FOCS 2014, and Notable Article in Computing in 2014 by Computing Reviews. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker Yin-Tat Lee, Tuesday April 5th- 12:30pm
by Mitra D. Kelly 04 Apr '16

04 Apr '16

Colloquium Speaker Yin-Tat Lee, Massachusetts Institute of Technology April 5th, 12:30pm Computer Science 105 Title: Faster algorithms for fundamental convex problems and their applications in combinatorial optimization Abstract: Convex optimization has been studied extensively and is a prominent tool in various areas such as combinatorial optimization, data analysis, operations research, and scientific computing. Each field has developed specialized tools including data structures, sampling methods, and dimension reduction. In the past several years, I have been combining and improving the optimization techniques from different fields to design faster optimization algorithms. In this talk, I will discuss my work in this direction and illustrate it through my results on linear programming and general convex optimization. In particular, I will present a new algorithm for solving linear programs, which gives the first improvement to the running time for linear programming in 25 years. Then, I will present the first nearly cubic time algorithm for solving general convex optimization problems. Furthermore, I will discuss how these two results can be used to improve the running time of many classical combinatorial problems such as maximum flow and submodular function minimization. This talk will assume no prior knowledge of optimization. Bio: Yin Tat Lee is a Ph.D. candidate in the department of mathematics at the Massachusetts Institute of Technology. He is interested in designing faster algorithms, particularly for problems in optimization. Since he began his Ph.D. in 2012, he has combined ideas from continuous and discrete mathematics to substantially advance the state-of-the-art for solving many fundamental problems in computer science, such as linear programming, maximum flow, and submodular function minimization. He has received a variety of awards, including the Best Student Paper Award at FOCS 2015, Best Paper Award at SODA 2014, Best Paper Award and Best Student Paper Award at FOCS 2014, and Notable Article in Computing in 2014 by Computing Reviews. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker Philipp Kräehenbüehl, today, 12:30pm
by Nicole E. Wagenblast 04 Apr '16

04 Apr '16

Colloquium Speaker Philipp Kräehenbüehl, University of California, Berkeley Monday, April 4, 2016 - 12:30pm Computer Science 105 The many ways to understand the pixels The field of computer vision is arguably seeing one of its most transformative changes in recent history. Convolutional neural networks (CNNs) have revolutionized the field, reaching super-human performance on some long-standing computer vision tasks, such as image classification. The success of these networks is fueled by massive amounts of human-labeled data. However this paradigm does not scale to a deeper and more detailed understanding of images, as it is simply too hard to collect enough human-labeled data. The issue is not that we humans don't understand the image, but we often struggle to convey enough information to successfully supervise a vision system. In this talk I show how computer vision can go beyond massive human supervision. This involves designing better models that deal with fewer labels, exploiting easier and more intuitive annotations, or coming up with novel optimizations to train deep architectures with far fewer human annotations, or even without any at all. I'll focus on three long standing computer vision problems: semantic segmentation, intrinsic image decomposition and dense semantic correspondences. Philipp Krähenbühl is a postdoctoral researcher at UC Berkeley. He received a B.S. in Computer Science from ETH Zurich in 2009, and a PhD in Computer Science from Stanford University in 2014. Philipp's research interests lie in Computer vision, Machine learning and Computer Graphics. He is particularly interested in deep learning, efficient optimization techniques, and structured output prediction.

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Colloquium Speaker Philipp Kräehenbüehl, Monday April 4, 12:30pm- reminder
by Nicole E. Wagenblast 03 Apr '16

03 Apr '16

Colloquium Speaker Philipp Kräehenbüehl, University of California, Berkeley Monday, April 4, 2016 - 12:30pm Computer Science 105 The many ways to understand the pixels The field of computer vision is arguably seeing one of its most transformative changes in recent history. Convolutional neural networks (CNNs) have revolutionized the field, reaching super-human performance on some long-standing computer vision tasks, such as image classification. The success of these networks is fueled by massive amounts of human-labeled data. However this paradigm does not scale to a deeper and more detailed understanding of images, as it is simply too hard to collect enough human-labeled data. The issue is not that we humans don't understand the image, but we often struggle to convey enough information to successfully supervise a vision system. In this talk I show how computer vision can go beyond massive human supervision. This involves designing better models that deal with fewer labels, exploiting easier and more intuitive annotations, or coming up with novel optimizations to train deep architectures with far fewer human annotations, or even without any at all. I'll focus on three long standing computer vision problems: semantic segmentation, intrinsic image decomposition and dense semantic correspondences. Philipp Krähenbühl is a postdoctoral researcher at UC Berkeley. He received a B.S. in Computer Science from ETH Zurich in 2009, and a PhD in Computer Science from Stanford University in 2014. Philipp's research interests lie in Computer vision, Machine learning and Computer Graphics. He is particularly interested in deep learning, efficient optimization techniques, and structured output prediction.

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