March 2018 - talks - lists.cs.princeton.edu

Young Kun Ko PreFPO today, March 29, 2018 at 3:00 pm in CS402.
by Barbara A. Mooring 29 Mar '18

29 Mar '18

Young Kun Ko will present his PreFPO today, March 29, 2018 at 3:00 pm in CS402. Members of the Committee: Mark Braverman - Princeton University Omri Weinstein - Princeton University Gillat Kol - Princeton University Ran Raz - Princeton University Zeev Dvir - Princeton University All are welcomed to attend. Title : Hardness amplification for two prover game and communication complexity Abstract : Whether repeating a task n-times in parallel amplifies the complexity measure in question by n-times is a key technical challenge in complexity theory. >From computational perspective, we first investigate the power and limits of parallel repetition of two-prover games. We characterize the amortized behavior of the game via how much information one needs to win the game with good probability — providing an intuitive explanation on why odd-cycle game introduced by [Raz’07] serves as a counter-example to strong parallel repetition. As an alternative to parallel repetition, we investigate set-based repetition techniques. First we explore the applications of Birthday repetition, first introduced in [AIM’14] for quasi-polynomial time hardness results. Then as an alternative to parallel repetition, we introduce symmetric parallel repetition, that is parallel repetition without coordinates. We show that symmetric parallel repetition indeed beats parallel repetition in some regimes and odd-cycle game is not a counterexample for symmetric parallel repetition. This sheds some light on possible attack route for infamous Unique Games Conjecture. >From communication complexity perspective, we introduce semi-direct sum as a mean to amplify hardness for asymmetric communication. As an application, we reprove and improve the lower bound for \ell_\infty approximate nearest neighbor search. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Justin Johnson, TODAY at 12:30
by Emily C. Lawrence 29 Mar '18

29 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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Reminder: Colloquium Speaker-Justin Johnson, tomorrow at 12:30
by Emily C. Lawrence 28 Mar '18

28 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-Samira Khan, Tuesday, April 3
by Emily C. Lawrence 28 Mar '18

28 Mar '18

Samira Khan from the University of Virginia Tuesday, April 3, 2018 - 12:30pm Computer Science - Room 105 Host: Margaret Martonosi "Solving the DRAM Scaling Challenge" Abstract: Technology scaling of DRAM cells has enabled higher capacity memory for the last few decades. Unfortunately, DRAM cells become vulnerable to failure as they scale down to a smaller size. Enabling high-performance, energy-efficient, scalable memory systems without sacrificing the reliability is a major research challenge. My work focuses on designing a scalable memory system by rethinking the traditional assumptions in abstraction and separation of responsibilities across system layers. In this talk, I will discuss three fundamental ways to enable DRAM scaling. First, we can enable scaling by letting the manufacturers build smaller cells without providing any strict reliability guarantee. I envision manufacturers shipping DRAMs without fully ensuring correct operation, and the system being responsible for detecting and mitigating DRAM failures while operating in the field. However, designing such a system is difficult due to intermittent DRAM failures. In this talk, I will discuss a system design, capable of providing reliability guarantees even in the presence of intermittent failures. Second, we can enable high-capacity memory leveraging the emerging non-volatile memory technologies that are predicted to be more scalable. I will present my vision to redefine the hardware and operating system interface to unify memory and storage system with non-volatile memory and discuss the opportunities and challenges of such a system. Third, tolerating failures in the application can improve memory scalability. The fundamental challenge of such a system is how to assure, verify, and quantify the quality of the results. I envision a system that limits the impact of memory failures such that it is possible to statically determine the worst-case results from the maximum possible error in the input. Bio: Samira Khan is an Assistant Professor at the University of Virginia ( UVa ). Prior to joining UVa , she was a Post Doctoral Researcher at Carnegie Mellon University, funded by Intel Labs. Her research focuses on improving the performance, efficiency, and reliability of the memory system. She is the recipient of NSF CRII Award, NSF GOALI Award, and Rising Stars in EECS Award. She received her PhD from the University of Texas at San Antonio. During her graduate studies, she worked at Intel, AMD, and EPFL.

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Fisher Yu will present his FPO today at 10:30 am in Room CS 402.
by Barbara A. Mooring 27 Mar '18

27 Mar '18

Fisher Yu will present his FPO today 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 A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Jia Deng TODAY at 12:30
by Emily C. Lawrence 27 Mar '18

27 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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Young Kun Ko - PreFPO March 29, 2018 3:00 pm CS402
by Barbara A. Mooring 26 Mar '18

26 Mar '18

Good afternoon, Young Kun Ko will present his PreFPO on Thursday, March 29, 2018 at 3:00 pm in CS402. Members of the Committee: Mark Braverman - Princeton University Omri Weinstein - Princeton University Gillat Kol - Princeton University Ran Raz - Princeton University Zeev Dvir - Princeton University All are welcomed to attend. Title : Hardness amplification for two prover game and communication complexity Abstract : Whether repeating a task n-times in parallel amplifies the complexity measure in question by n-times is a key technical challenge in complexity theory. >From computational perspective, we first investigate the power and limits of parallel repetition of two-prover games. We characterize the amortized behavior of the game via how much information one needs to win the game with good probability — providing an intuitive explanation on why odd-cycle game introduced by [Raz’07] serves as a counter-example to strong parallel repetition. As an alternative to parallel repetition, we investigate set-based repetition techniques. First we explore the applications of Birthday repetition, first introduced in [AIM’14] for quasi-polynomial time hardness results. Then as an alternative to parallel repetition, we introduce symmetric parallel repetition, that is parallel repetition without coordinates. We show that symmetric parallel repetition indeed beats parallel repetition in some regimes and odd-cycle game is not a counterexample for symmetric parallel repetition. This sheds some light on possible attack route for infamous Unique Games Conjecture. >From communication complexity perspective, we introduce semi-direct sum as a mean to amplify hardness for asymmetric communication. As an application, we reprove and improve the lower bound for \ell_\infty approximate nearest neighbor search. -- Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Cathy Wu, TODAY at 4:30PM
by Emily C. Lawrence 26 Mar '18

26 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-Jia Deng tomorrow at 12:30
by Emily C. Lawrence 26 Mar '18

26 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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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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