- talks - lists.cs.princeton.edu

Colloquium Speaker-Saurabh Gupta, Thursday, April 12
by Emily C. Lawrence 06 Apr '18

06 Apr '18

Saurabh Gupta, University of California at Berkeley Thursday, April 12, 2018 - 12:30pm Computer Science - Room 105 Host: Szymon Rusinkiewicz “Visual Perception and Navigation in 3D Scenes” Abstract: In recent times, computer vision has made great leaps towards 2D understanding of sparse visual snapshots of the world. This is insufficient for robots that need to exist and act in the 3D world around them based on a continuous stream of multi-modal inputs. In this talk, I will present some of my efforts in bridging this gap between computer vision and robotics. I will show how thinking about computer vision and robotics together, brings out limitations of current computer vision tasks and techniques, and motivates joint study of perception and action for robotic tasks. I will showcase these aspects via three examples: 3D scene understanding, representation learning for varied modalities, and visual navigation. I will conclude by pointing out future research directions at the intersection of computer vision and robotics, thus showing how the two fields are ready to get back together. Bio: Saurabh Gupta is a Ph.D. student at UC Berkeley, where he is advised by Jitendra Malik. His research interests include computer vision, robotics and machine learning. His PhD work focuses on 3D scene understanding, and visual navigation. His work is supported by a Berkeley Fellowship and a Google Fellowship in Computer Vision.

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Colloquium Speaker-Amit Levy, Tuesday, April 10
by Emily C. Lawrence 04 Apr '18

04 Apr '18

Amit Levy from Stanford University Tuesday, April 10, 2018 - 12:30pm Computer Science - Room 105 Host: Mike Freedman "Closing the Loop on Secure System Design" Abstract: Despite decades of research building secure operating systems, many deployed systems must still choose between flexible application APIs and security. As a result, the vast majority of programmers are unable to improve these systems. This is not merely a result of poor system building. It is hard to design highly extensible systems that are both secure and useful. Moreover, evaluating novel designs with actual developers is critical in order to make sure system builders can adopt research systems in practice. Fortunately, in emerging application domains, such as the Internet of Things, there are no entrenched operating systems and application portability is less important. This makes it possible evaluate research techniques for building more secure and extensible systems with developers who are willing to adopt them. I'll describe Tock, an operating system for microcontrollers that enables third-party developers to extend the system. Tock uses the Rust type-system to isolate kernel extensions and the hardware to isolate applications. I'll discuss how we continuously evaluate Tock by engaging with practitioners, and how lessons from practitioners have fed back into the system's design. Bio: Amit Levy is a PhD candidate in Computer Science at Stanford University. He builds secure operating system kernels, web platforms, and network systems that help make computers more programmable by third-party application developers.

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Colloquium Speaker-Jose Faleiro, Monday, April 9
by Emily C. Lawrence 04 Apr '18

04 Apr '18

CS Department Colloquium Speaker Jose Faleiro from Yale University Monday, April 9, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd "High Performance Serializable Transactions via Deterministic Execution" Abstract: Concurrency, the processing of multiple requests simultaneously, is one of the most challenging problems large-scale server applications face in practice. Accordingly, database systems research has long made the case for automatically handling concurrency in the database by guaranteeing serializability . Serializability shields applications from reasoning about concurrency, and allows developers to focus entirely on implementing application logic. Unfortunately, in the 40-plus years since its inception, serializability has not seen wide adoption in practice. This is because weaker guarantees, which expose applications to concurrency and the inevitable bugs that arise, perform significantly better. In this talk, I will discuss my research on addressing the performance limitations of serializability via deterministic transaction execution. Deterministic transaction execution exploits the fact that a large class of modern server applications do not require the full generality of conventional database transactions. By tailoring transaction execution mechanisms for this class of applications, my research shows that it is possible to achieve serializability with minimal performance overhead. I will first describe a serializable multi-versioning mechanism that decouples conflicting reads and writes, and subsequently outperforms a state-of-the-art implementation of the weaker guarantee of snapshot isolation by over 3x. Next, I will describe piecewise visibility, a concurrency control mechanism that isolates requests at a finer granularity than entire transactions, which consequently permits aggressive serializable transaction interleavings and outperforms the weaker guarantee of read committed by over 3x. Finally, I will discuss ongoing work that applies deterministic transaction execution principles to address replication lag in Facebook’s production MySQL infrastructure. Bio: Jose Faleiro is a PhD candidate in computer science at Yale University. His research interests are in data management systems, multi-core systems, and distributed systems. His thesis research investigates the use of deterministic execution to enable scalable and efficient transaction processing on main-memory multi-core database systems. In addition to his academic research at Yale, Jose has worked on large-scale real world systems, including Microsoft’s Orleans cloud programming framework, and Facebook’s production MySQL infrastructure. He is the recipient of the Alan J. Perlis Fellowship at Yale, and a Microsoft Research Tech Transfer Award. He has an undergraduate degree in computer science from the Birla Institute of Technology and Science (BITS), Pilani , India.a

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Colloquium Speaker-Samira Khan, TODAY at 12:30pm
by Emily C. Lawrence 03 Apr '18

03 Apr '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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Reminder: Colloquium Speaker-Samira Khan, tomorrow at 12:30pm
by Emily C. Lawrence 02 Apr '18

02 Apr '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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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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