- talks - lists.cs.princeton.edu

Colloquium Speaker-Danqi Chen, TODAY at 12:30
by Emily C. Lawrence 26 Mar '18

26 Mar '18

CS Department Colloquium Speaker Danqi Chen from Stanford University Monday, March 26, 2018 - 12:30pm Computer Science - Room 105 Host: Barbara Engelhardt “Knowledge from Language via Deep Understanding” Abstract: Almost all of human knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network approaches that open up opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster and more accurate dependency parser for finding the structure of sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don't need this intermediate step and can directly gain knowledge and answer people's questions from large textbases ? In the third part, I explore this possibility by directly reading text with a simple yet highly effective neural architecture for question answering. Bio: Danqi Chen is a PhD student in Computer Science at Stanford University, working with Christopher Manning on deep learning approaches to natural language processing. Her research centers on how computers can achieve a deep understanding of human language and the information it contains. Danqi received Outstanding Paper Awards at ACL 2016 and EMNLP 2017, a Facebook Fellowship, a Microsoft Research Women’s Fellowship and an Outstanding Course Assistant Award from Stanford. Previously, she received her B.E. in Computer Science from Tsinghua University.

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Reminder: Colloquium Speaker-Cathy Wu, tomorrow at 4:30PM
by Emily C. Lawrence 25 Mar '18

25 Mar '18

***PLEASE NOTE THE DIFFERENT TIME AND LOCATION CS and EE Department Colloquium Speaker Cathy Wu, from the University of California at Berkeley Monday, March 26, 2018 - 4:30PM Bowen Hall - Room 222 Host: Prof. Prateek Mittal (EE) & Szymon Rusinkiewicz (CS) “Mixed-autonomy mobility: scalable learning and optimization” Abstract: How will self-driving cars change urban mobility? This talk describes contributions in machine learning and optimization critical for enabling mixed-autonomy mobility, the gradual and complex integration of automated vehicles into the existing transportation system. The talk first explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics such as traffic congestion, using novel techniques in model-free deep reinforcement learning. Second, the talk presents generic reinforcement learning techniques for improved variance reduction, developed for large-scale control systems such as traffic networks and robotic manipulation. To anchor this work in a broader mobility planning context, the coordination of automated vehicles relies on accurate traffic flow sensing. To this end, a new convex optimization method for cellular network measurements from AT&T for all of California is introduced to address a flow estimation problem previously believed to be intractable. Finally, automated vehicles are expected to increase transportation demand through a phenomenon called induced demand. To address this, joint work with Microsoft Research is presented, which provides theoretical justification for the application of widely used clustering algorithms to ridesharing problems, designed to mitigate the strain on existing infrastructure. Together, these contributions demonstrate, through principled learning and optimization methods, that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility. Bio: Cathy Wu is a PhD candidate in machine learning in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, the Berkeley Artificial Intelligence Research lab, Berkeley DeepDrive , California PATH, and the Berkeley RISELab . She is interested in developing principled computational tools to enable reliable and complex decision-making for critical societal infrastructure, such as transportation systems. Cathy received her Masters and Bachelors degrees in EECS from MIT. She is the recipient of several fellowships including the NSF graduate fellowship, the Berkeley Chancellor's fellowship, the NDSEG fellowship, and the Dwight David Eisenhower graduate fellowship. Her work was acknowledged by several awards, including the 2016 IEEE ITSC Best Paper Award and the 2017 ITS Outstanding Graduate Student Award. Her leadership, in particular as the Research Lead of the Learning Traffic Team at Berkeley, was recognized by numerous awards and invitations, such as the 2017 IEEE Leaders Summit and multiple NSF early-career investigator workshops on cyber-physical systems. Throughout her career, Cathy has collaborated or interned broadly across fields, including civil engineering, mechanical engineering, urban planning, and public policy, and institutions, including OpenAI , Microsoft Research, the Google Self-Driving Car Team, AT&T, Facebook, and Dropbox. As the founder and Chair of the Interdisciplinary Research Initiative within the ACM Future of Computing Academy, she is actively working to create international programs to further enable and support interdisciplinary research in computing.

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Reminder: Colloquium Speaker-Danqi Chen, tomorrow at 12:30
by Emily C. Lawrence 25 Mar '18

25 Mar '18

CS Department Colloquium Speaker Danqi Chen from Stanford University Monday, March 26, 2018 - 12:30pm Computer Science - Room 105 Host: Barbara Engelhardt “Knowledge from Language via Deep Understanding” Abstract: Almost all of human knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network approaches that open up opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster and more accurate dependency parser for finding the structure of sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don't need this intermediate step and can directly gain knowledge and answer people's questions from large textbases ? In the third part, I explore this possibility by directly reading text with a simple yet highly effective neural architecture for question answering. Bio: Danqi Chen is a PhD student in Computer Science at Stanford University, working with Christopher Manning on deep learning approaches to natural language processing. Her research centers on how computers can achieve a deep understanding of human language and the information it contains. Danqi received Outstanding Paper Awards at ACL 2016 and EMNLP 2017, a Facebook Fellowship, a Microsoft Research Women’s Fellowship and an Outstanding Course Assistant Award from Stanford. Previously, she received her B.E. in Computer Science from Tsinghua University.

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Colloquium Speaker-Justin Johnson, Thursday, March 29
by Emily C. Lawrence 23 Mar '18

23 Mar '18

CS Department Colloquium Speaker Justin Johnson from Stanford University Thursday, March 29, 2018 - 12:30pm Computer Science - Room 105 Host: Olga Russakovsky “Compositional Visual Intelligence” Abstract: The field of computer vision has made enormous progress in the last few years, largely due to convolutional neural networks. Despite success on traditional computer vision tasks, our systems are still a long way from the general visual intelligence of people. I will argue that an important facet of visual intelligence is composition - understanding of the whole derives from an understanding of the parts. To achieve the goal of compositional visual intelligence, we must explore new computer vision tasks, create new datasets, and develop new models that exploit compositionality. I will discuss the Visual Genome dataset which we created in service of these goals, and three research directions enabled by this new data where incorporating compositionality results in systems with richer visual intelligence. I will first discuss image captioning: traditional systems generate short sentences describing images, but by decomposing images into regions and descriptions into phrases we can that generate two types of richer descriptions: dense captions and paragraphs. Second, I will discuss visual question answering: existing datasets (including Visual Genome) consist primarily of short, simple questions; to study more complex questions requiring compositional reasoning, we built the CLEVR dataset and show that existing methods fall short on this new benchmark. We then propose an explicitly compositional model for visual question answering that internally converts questions to functional programs, and executes these programs by composing neural modules. Third, I will discuss text-to-image synthesis: existing systems can generate simple images of a single object conditioned on text descriptions, but struggle with more complex descriptions. By replacing freeform natural language with compositional scene graphs of objects and relationships, we can generate complex images containing multiple objects. I will conclude by discussing future areas where compositionality can be used to enrich visual intelligence. Bio: Justin is a PhD candidate in Computer Science at Stanford University, advised by Fei-Fei Li. His research interests lie at the intersection of computer vision and machine learning. Since 2015 he has co-taught a Stanford course on convolutional neural networks and deep learning with Andrej Karpathy , Serena Yeung, and Fei-Fei Li which has been viewed hundreds of thousands of times online. He received his BS in Mathematics and Computer Science at the California Institute of Technology, and during his PhD he has spent time at Google Cloud AI, Facebook AI Research, and Yahoo Research.

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Colloquium Speaker-Jia Deng, Tuesday, March 27
by Emily C. Lawrence 22 Mar '18

22 Mar '18

Jia Deng from the University of Michigan Tuesday, March 27, 2018 - 12:30pm Computer Science - Room 105 Host: Olga Russakovsky “Teaching Computers to See and Think” Abstract: In this talk I will present my research on making computers see and think, two abilities essential for deep understanding of pixels. In particular, I will focus on understanding not just visual objects but also their connections. First, I will describe how we built ImageNet, a large-scale visual knowledge base that led to a paradigm shift in computer vision research. Next, I will show the design of a reasoning engine that uses an object relation graph to perform probabilistic logical inference in visual recognition. Third, I will present a general and powerful method for extracting deep semantics from pixels, with state-of-the-art results on multiple challenging tasks. I will conclude my talk by describing ongoing efforts and future directions toward deeper semantics, richer knowledge, and more human-like reasoning. Bio: Jia Deng is an Assistant Professor of Computer Science and Engineering at the University of Michigan. His research focus is on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Sloan Research Fellowship, PAMI Mark Everingham Prize, Yahoo ACE Award, Google Faculty Research Award, Amazon Research Award, ICCV Marr Prize, and ECCV Best Paper Award.

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CISS Plenary Speaker Bin Yu, tomorrow at 1:20pm
by Emily Lawrence 22 Mar '18

22 Mar '18

Bin Yu, University of California, Berkeley Friday, March 23 - 1:20pm Friend Center 101 "Three Principles of Data Science: Predictability, Stability and Computability" This is part of the Conference on Information Sciences and Systems More info here: http://ee-ciss.princeton.edu/ In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title. The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive models used for reconstruction of movies from fMRI brain signals to gain interpretability of the predictive models. The second project uses predictive transfer learning and stable (manifold) deep dream images to characterize the difficult V4 neurons in primate visual cortex. Our results lend support, to a certain extent, to the resemblance to a primate brain of Convolutional Neural Networks (CNNs). Bio: Bin Yu is Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley. Her current research interests focus on statistics and machine learning theory, methodologies, & algorithms for solving high-dimensional data problems. Her lab is engaged in interdisciplinary research with scientists from genomics, neuroscience, precision medicine and political science. She obtained her B.S. degree in Mathematics from Peking University and her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley. She held faculty positions at the University of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent. She was Chair of Department of Statistics at UC Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Lab on Statistics and Information Technology at Peking University, China, & Chair of the Scientific Advisory Committee of the Statistical Science Center at Peking University. She is Member of the U.S. National Academy of Sciences & Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE. She served on the Board of Mathematics Sciences and Applications (BMSA) of NAS and as co-chair of SAMSI advisory committee, & on the Board of Trustees at ICERM and Scientific Advisory Board of IPAM. She has served or is serving on many editorial boards, including Journal of Machine Learning Research (JMLR), Annals of Statistics and American Statistical Association (JASA).

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Prof. Vikram Adve, U Illinois. March 21. 4pm. CS Small Auditorium
by Margaret Martonosi 20 Mar '18

20 Mar '18

Wednesday March 21, 4pm CS Small Auditorium TITLE: HPVM: Simplifying Heterogeneous Parallel Programming Prof. Vikram Adve, University of Illinois, Urbana-Champaign ABSTRACT: Heterogeneous parallel systems are attractive because they use specialized computing elements, including GPUs, vector hardware, FPGAs, and domain-specific accelerators, that can greatly improve energy efficiency, performance, or both, compared with traditional homogeneous systems. A major drawback, however, is that programming and reasoning about heterogeneous parallel systems is highly challenging for application developers, language and compiler designers, and hardware engineers. In the HPVM project, we are developing a unified parallel program representation, Heterogeneous Parallel Virtual Machine, that aims to capture the wide range of parallelism available in such systems. We use the HPVM representation for three different purposes: (1) a compiler intermediate representation (IR), (2) a virtual ISA for shipping (virtual) object code, and (3) a representation for runtime scheduling. We have implemented the HPVM IR and associated compiler tools as an extension of the LLVM compiler infrastructure. In this talk, I will describe the HPVM representation, experimental results demonstrating the ability of HPVM to achieve performance comparable to separately hand-tuned code for GPUs and vector hardware, and to enable highly flexible scheduling policies. I will conclude with a brief discussion of how well-chosen system interfaces can enable highly programmable, specialized hardware, and the influence of such interfaces on key layers of the system stack. BIO: Vikram Adve is the Donald B. Gillies Professor and Interim Head of Computer Science at the University of Illinois at Urbana-Champaign, and has affiliate appointments with the Coordinated Sciences Laboratory and the Information Trust Institute. Adve's research interests lie in developing and using compiler techniques to improve the performance, programmability and reliability of computer systems. Adve and his Ph.D. student, Chris Lattner, co-designed the LLVM Compiler Infrastructure, which enabled novel compilation capabilities for a wide range of languages. LLVM is widely used in industry today, ranging from mobile devices (e.g., in iOS and Android) to supercomputers (e.g., at Cray and NVIDIA) to data centers (e.g., at Google). Adve, Lattner and Evan Cheng received the ACM Software System Award in 2012 for co-developing LLVM (making UIUC the only University to win this Award twice: the Mosaic Web Browser won it in 1995). Adve has won a ten-year-retrospective Most Influential Paper award at CGO 2004, and distinguished paper awards at several conferences including PLDI 2005, SOSP 2007 and ICSE 2011. Adve's Ph.D. student, Robert Bocchino, won the 2010 ACM SIGPLAN Outstanding Dissertation Award and another Ph.D. student, John Criswell, won Honorable Mentions for both the 2014 ACM SIGOPS Dissertation Award and the 2014 ACM Doctoral Dissertation Award. Adve is a Fellow of the ACM and was named a University Scholar at the University of Illinois in 2015. Margaret Martonosi H. T. Adams '35 Professor of Computer Science. Director, Keller Center for Innovation in Engineering Education Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm

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Fisher Yu FPO - March 27, 2018 at 10:30 am Room CS 402
by Barbara A. Mooring 20 Mar '18

20 Mar '18

Good afternoon, Fisher Yu will present his FPO on, March 27, 2018 at 10:30 am in Room CS 402. The members of his Committee are: Thomas Funkhouser - Princeton University Adam Funkelstein - Princeton University Olga Russakovsky - Princeton University All are welcomed to attend. Title: Pixel-Level Prediction: From Geometry to Semantics Abstract: Pixel-level prediction generalizes a wide range of computer vision tasks including semantic image segmentation and dense depth prediction. They are fundamental for image recognition, receiving continual attention from the community. However, although they share common traits that may admit a general solution, they are usually studied in isolation because of different domain characteristics. This thesis aims to study the essential problems behind those tasks and shed light on a general framework. This thesis starts with an algorithm that can predict plausible depth from almost identical images based on geometric optimization. The motion between those images is called ``Accidental Motion''. The analysis of accidental motion shows that motion optimization has special convexity properties. It leads to a reconstruction pipeline that can produce a plausible dense depth map for the reference image, which is shown to enable depth based camera effects. The second part then studies learning pixel representation to predict semantic properties based on the single reference image. Previous works usually use learned upsampling to recover the pixel-level information. This work proposes to use Dilated Convolution to transform the classification networks such that high-resolution prediction is achieved without upsampling. Dilated Convolution can also render an exponential increase in receptive field, which is ideal for learning global context. A context module is proposed based on this property that can improve the network performance significantly and consistently. Dilation is still a standard component in the state-of-the-art method for semantic image segmentation. The further study of dilated residual networks shows that same high-resolution prediction can also improve image classification results. This indicates no essential network architecture difference exists between image classification and segmentation. Further inspection of class activation maps and layer responses uncover peculiar gridding patterns and their cause. This finding leads to new designs of convolutional networks that can remove the gridding artifacts and produce activations with better spatial consistency. The new networks can improve the performance of both image classification and semantic segmentation. The presented method and results may inspire new research in building a unified framework for image recognition of geometry and semantics. Barbara Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Cathy Wu, Monday, March 26
by Emily C. Lawrence 20 Mar '18

20 Mar '18

***PLEASE NOTE THE DIFFERENT TIME AND LOCATION CS and EE Department Colloquium Speaker Cathy Wu, from the University of California at Berkeley Monday, March 26, 2018 - 4:30pm Bowen Hall - Room 222 Host: Prof. Prateek Mittal (EE) & Szymon Rusinkiewicz (CS) “Mixed-autonomy mobility: scalable learning and optimization” Abstract: How will self-driving cars change urban mobility? This talk describes contributions in machine learning and optimization critical for enabling mixed-autonomy mobility, the gradual and complex integration of automated vehicles into the existing transportation system. The talk first explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics such as traffic congestion, using novel techniques in model-free deep reinforcement learning. Second, the talk presents generic reinforcement learning techniques for improved variance reduction, developed for large-scale control systems such as traffic networks and robotic manipulation. To anchor this work in a broader mobility planning context, the coordination of automated vehicles relies on accurate traffic flow sensing. To this end, a new convex optimization method for cellular network measurements from AT&T for all of California is introduced to address a flow estimation problem previously believed to be intractable. Finally, automated vehicles are expected to increase transportation demand through a phenomenon called induced demand. To address this, joint work with Microsoft Research is presented, which provides theoretical justification for the application of widely used clustering algorithms to ridesharing problems, designed to mitigate the strain on existing infrastructure. Together, these contributions demonstrate, through principled learning and optimization methods, that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility. Bio: Cathy Wu is a PhD candidate in machine learning in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, the Berkeley Artificial Intelligence Research lab, Berkeley DeepDrive , California PATH, and the Berkeley RISELab . She is interested in developing principled computational tools to enable reliable and complex decision-making for critical societal infrastructure, such as transportation systems. Cathy received her Masters and Bachelors degrees in EECS from MIT. She is the recipient of several fellowships including the NSF graduate fellowship, the Berkeley Chancellor's fellowship, the NDSEG fellowship, and the Dwight David Eisenhower graduate fellowship. Her work was acknowledged by several awards, including the 2016 IEEE ITSC Best Paper Award and the 2017 ITS Outstanding Graduate Student Award. Her leadership, in particular as the Research Lead of the Learning Traffic Team at Berkeley, was recognized by numerous awards and invitations, such as the 2017 IEEE Leaders Summit and multiple NSF early-career investigator workshops on cyber-physical systems. Throughout her career, Cathy has collaborated or interned broadly across fields, including civil engineering, mechanical engineering, urban planning, and public policy, and institutions, including OpenAI , Microsoft Research, the Google Self-Driving Car Team, AT&T, Facebook, and Dropbox. As the founder and Chair of the Interdisciplinary Research Initiative within the ACM Future of Computing Academy, she is actively working to create international programs to further enable and support interdisciplinary research in computing.

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Colloquium Speaker-Danqi Chen, Monday, March 26
by Emily C. Lawrence 20 Mar '18

20 Mar '18

CS Department Colloquium Speaker Danqi Chen from Stanford University Monday, March 26, 2018 - 12:30pm Computer Science - Room 105 Host: Barbara Engelhardt “Knowledge from Language via Deep Understanding” Abstract: Almost all of human knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network approaches that open up opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster and more accurate dependency parser for finding the structure of sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don't need this intermediate step and can directly gain knowledge and answer people's questions from large textbases ? In the third part, I explore this possibility by directly reading text with a simple yet highly effective neural architecture for question answering. Bio: Danqi Chen is a PhD student in Computer Science at Stanford University, working with Christopher Manning on deep learning approaches to natural language processing. Her research centers on how computers can achieve a deep understanding of human language and the information it contains. Danqi received Outstanding Paper Awards at ACL 2016 and EMNLP 2017, a Facebook Fellowship, a Microsoft Research Women’s Fellowship and an Outstanding Course Assistant Award from Stanford. Previously, she received her B.E. in Computer Science from Tsinghua University.

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