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EE Seminar - May 15, 2017, 11:00 am, E-Quad B205 - Prof. Veljko Milutinovic
by Nicole E. Wagenblast 12 May '17

12 May '17

Department of Electrical Engineering Seminar Speaker: Prof. Veljko Milutinovic University of Belgrade Title: DataFlow SuperComputing for BigData Date: Monday, May 15, 2017 Time: 11:00 am – 12 pm, Hands-on Tutorial 1 – 3 pm Room: E -Quad, Room B205 Host: Prof. Ruby Lee Abstract: This presentation analyses the essence of DataFlow SuperComputing, defines its advantages and sheds light on the related programming model. DataFlow computers, compared to ControlFlow computers, offer speedups of 20 to 200 (even 2000 for some applications), power reductions of about 20, and size reductions of also about 20. However, the programming paradigm is different, and has to be mastered. The talk explains the paradigm, using Maxeler as an example, and sheds light on the ongoing research in the field. Examples include: Trading and Finances, CreditDerivatives and numerous related BankingApplications, SignalProcessing, GeoPhysics, WeatherForecast, OilGas, DataEngineering, DataMining, SmartGrid, ScientificSimulations, BrainResearch, Genomics, etc. Also, a recent study from Tsinghua University in China is presented, which reveals that, for Shallow Water Weather Forecast (a BigData problem), on the 1U level, the Maxeler DataFlow machine is 14 times faster than the Tianhe machine, at the time of the study, rated #1 on the Top 500 list (based on Linpack, which is a smalldata benchmark). Given enough time, the talk also gives a tutorial about the programming in space, which is the programming paradigm used for the Maxeler dataflow machines (established in 2014 by Stanford, Imperial, Tsinghua, and the University of Tokyo). The talk concludes with selected examples and a tool overview ( appgallery.maxeler.com and webIDE.maxeler.com ). A more detailed tutorial on programming in space will be available after the talk, including the information on MaxGenFD. Related hands-on activities will be performed by remote login ( maxeler.mi.sanu.ac.rs ). Since December 2016, Maxeler is also available via Amazon AWS. In December 2016, Hitachi of Japan announced its partnership with Maxeler (also available via Amazon AWS), stating that, for their finance and cryptography applications, Maxeler was orders of magnitude faster than any other ControlFlow platform (i.e., CPU or GPU). Hands-on Tutorial : 1 – 3 pm in B205 Bio: Prof. Veljko Milutinovic (1951) received his PhD from the University of Belgrade, spent about a decade on various faculty positions in the USA (mostly at Purdue University), and was a co-designer of the DARPAs first GaAs RISC microprocessor. Later, for almost 3 decades, he taught and conducted research at the University of Belgrade, in EE, BA, MATH, and PHYS/CHEM. Now he serves as the Chairman of the Board for the Maxeler operation in Belgrade, Serbia. His research is mostly in datamining algorithms and dataflow computing, with the emphasis on mapping of data analytics algorithms onto fast energy efficient architectures. For 7 of his books, forewords were written by 7 different Nobel Laureates with whom he cooperated on his past industry sponsored projects. He has over 40 IEEE journal papers, over 40 papers in other SCI journals (4 in ACM journals), over 400 Thomson-Reuters citations, and about 4000 Google Scholar citations. Short courses on the subject he delivered so far in a number of universities worldwide: MIT, Harvard, Boston, NEU, Columbia, NYU, Princeton, Temple, Purdue, IU, UIUC, Michigan, EPFL, ETH, Karlsruhe, Heidelberg, etc. Also at the World Bank in Washington DC, BNL, IBM TJ Watson, ABB, Oracle, Yahoo, etc. Save

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Muneeb Ali will present his FPO on Friday, May 19, 2017 at 1:30pm in CS 105.
by Nicki Gotsis 12 May '17

12 May '17

Muneeb Ali will present his FPO on Friday, May 19, 2017 at 1:30pm in CS 105. The members of his committee are: Andrea LaPaugh (adviser); Readers: Mike Freedman and Arvind Narayanan; Nonreaders: Jennifer Rexford and Brian Kernighan All are welcome to attend. The talk title and abstract follow below. Trust-to-Trust Design of a New Internet The internet’s original design, guided by the end-to-end design principle, pushed all application-specific logic and complexity to the edges of the network and kept the core of the network focused on the simple task of delivering data. The original end-to-end principle, however, did not explicitly account for trust and security. There are several central points of trust and failure on the traditional internet. These include root servers for the Domain Name System (DNS) and public-key infrastructure like Certificate Authorities (CAs) that publish security certificates. Further, the success of cloud hosted services in the last decade means that most user data is stored on remote servers and end-users need to trust these remote servers for correct execution of their applications. In this thesis, we present a new internet architecture that explicitly follows the trust-totrust design principle, i.e., end-users don’t need to trust the core of the network for anything, and end-users can use applications and services in a fully decentralized way. We make the observation that cryptocurrency blockchains, like Bitcoin, can be used to bootstrap trust for new nodes joining a network. We identify the various limitations, like high latency and limited bandwidth, of contemporary blockchains and discuss how our architecture can scale by moving most operations outside of the blockchain layer. We detail our experience of running a large production system on top of a cryptocurrency blockchain and how that experience guided our design. We present the implementation of a new decentralized internet, called Blockstack, that takes the trust-to-trust architecture from a theoretical concept to a production system. Deploying new systems by modifying production blockchains is hard because it requires coordination and agreement from several parties. We introduce virtualchains, a virtual blockchain constructed by processing data from underlying blockchains, to enable the seamless introduction of new functionality on top of blockchains without requiring any consensus-breaking changes. Blockstack is already powering several fully decentralized applications, like OpenBazaar; it’s released as open-source software and, to date, more than 70,000 domains have been registered on it.

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Andrej Risteski will present his pre-FPO on Tuesday, May 16th, 2017 at 1pm in CS 302
by Nicki Gotsis 11 May '17

11 May '17

Andrej Risteski will present his pre-FPO on Tuesday, May 16th, 2017 at 1pm in CS 302. The members of his committee are: Readers: Sanjeev Arora (advisor), Rong Ge (Duke University), Elad Hazan Non-readers: Bernard Chazelle, Mark Braverman The talk title and abstract follow below. All are welcome to attend. Title : New techniques for learning and inference in probabilistic graphical models Abstract : A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the best fit graphical model from raw data, while inference is the task of answering probabilistic queries for a known graphical model (for example what is the marginal distribution of one of the variables or what is the distribution of a subset of variables, after conditioning on the values of some other subset of variables). Learning can be thought of as finding a graphical model that “explains” the raw data, while the inference queries extract the “knowledge” the graphical model contains. This talk will concern new provable techniques for performing both of these tasks. I will focus on a few vignettes: in the context of inference, I will talk about a new understanding of a class of algorithms for calculating partition functions, called variational methods through a combination of convex programming hierarchies — a recent area of interest in the area of combinatorial optimization — and entropy approximations based on low-order moments of a distribution. In the context of learning, I will talk about alternating-minimization and method-of-moments techniques for learning noisy-or Bayesian networks and topic modeling. The former are used for modeling the causal structure of diseases and symptoms, and the later for modeling the latent structure of text.

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Ohad Fried will present his FPO on Wednesday, May 17, 2017 at 1:30pm in CS 401.
by Nicki Gotsis 10 May '17

10 May '17

Ohad Fried will present his FPO on Wednesday, May 17, 2017 at 1:30pm in CS 401. The members of his committee are: Adam Finkelstein (adviser); readers: Tom Funkhouser and Eli Shechtman (Adobe); examiners: Szymon Rusinkiewicz and David Dobkin. A copy of his thesis is available in Room 310. Everyone is invited to attend his talk. The talk title and abstract follow below: PHOTO MANIPULATION, THE EASY WAY The typical smartphone user has many thousands of photos in their personal collection. Photo acquisition is effortless, and the next challenge is in devising methods to easily edit such large collections. Specifically, we need manipulation algorithms that are powerful enough for experts, yet simple for novices to master. We identified three key directions to empower novice users with expert-level editing capabilities while maintaining an overall simplicity in the process. Those directions are (1) better selection masks, (2) high-level goal-centric algorithms and (3) domain specific algorithms. In this thesis we give examples from each category. Given a photo, a novice user will typically either not edit it at all, or apply a simple global operation such as exposure correction. In contrast, a professional photo editor might perform local edits, specifying a selection mask to limit the operation to specific photo regions, or combining regions from several photos into a single composition. To ease selection mask creation we present a new patch embedding technique that allows for single-click selection masks. Novices often think in terms of goals (e.g. improve lighting, de-clutter photo) and less in technical terms such as color spaces and image layers. One example of a high-level goal is the removal of distracting elements from photos. The task is motivated by the way professional photographers operate. They carefully frame the scene and might move objects around in order to stage the perfect photo. We define “photo distractors” as the elements that, if removed, would improve the photo. Using a simple slider interaction we allow users to automatically remove such distractors from photos. It is at times useful to tailor solutions to specific photo types. As an example we show that, specifically for human heads, simple controls can induce sophisticated edits. Given a single portrait photo as input, we can change the pose of the head and the camera distance. This allows users to correct the “selfie effect”, i.e. big noses and small ears or to transform distant photos into selfies. We conclude by discussing how each of these directions can be further explored to enable better image editing tools for novices.

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Colloquium Speaker: Nima Anari, Tuesday, May 9th, 12:30pm
by Nicole E. Wagenblast 09 May '17

09 May '17

Colloquium Speaker Dr. Nima Anari, Stanford Univeristy Tuesday, May 9- 12:30pm Computer Science 105 Algorithm Design Using Polynomials In this talk I will present new methods for algorithm design and showcase results and breakthroughs obtained by looking through the lens of polynomials. I will start with the best known approximation algorithm for estimating the solution of the asymmetric traveling salesman problem, one of the oldest problems in computer science and optimization. Then, I will discuss a general framework based on computing inner products of polynomials that is used to design approximation algorithms for several important computational problems: volume maximization, counting matchings in bipartite graphs, Nash welfare maximization, and computing the permanent of positive semidefinite matrices. Nima Anari is a postdoctoral researcher at Stanford University working with Amin Saberi. His research interests are in the design and analysis of algorithms, with a recent focus on applications of polynomials in this endeavor. He recently completed his Ph.D. in computer science at UC Berkeley, where he was advised by Satish Rao and was part of the theory group. Prior to that he received his B.Sc. in computer engineering and mathematics from Sharif university of technology.

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Alexander Beatson will present his general exam on Wednesday, May 10, 2017 at 3pm in CS 301.
by Nicki Gotsis 09 May '17

09 May '17

Alexander Beatson will present his general exam on Wednesday, May 10 , 2017 at 3pm in CS 301. The members of his committee are: Han Liu (ORFE, adviser), Elad Hazan, and Arvind Narayanan. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Title: Deep learning in some non-iid settings In real-world settings observed data may often be non-iid. There might be differences between the training and deployment distributions, which we can model in the worst case as perturbation by an adversary [1], or the training data may be drawn from different distributions at different times or at different locations, and we might wish to learn from the global distribution without storing or communicating the data [12]. I will discuss some work on attacks on machine learners and on non-iid distributed deep learning with communication constraints. Attacks on machine learners: One threat model for machine learning involves an adversary perturbing the training set. We show that even a “blind” adversary with no knowledge of the training data can harm learning (meaningfully lower-bound the learner’s effective sample size) via simple data injection strategies. Another threat model involves an adversary perturbing the examples observed in deployment. Linear and deep models are particularly vulnerable to such adversarial examples [2-4]. We show that deep generative models can be used to correctly classify adversarial examples or detect them as being far from the manifold of real data. Non-iid distributed deep learning: “Federated learning” [12] features communication constraints and data distributed non-iid across nodes. We propose an asynchronous algorithm for this setting which uses the Hessian of the likelihood at each node to weight model averaging and compute proximal constraints. This may be derived as an iterated natural proximal method or an application of the Laplace approximation [9]. Experiments show this converges faster than other asynchronous algorithms when data are non-iid across nodes. We may approximate the algorithm by sending only a small fraction of the variable updates from each node, sampled according to diagonal of that node’s Hessian. This maintains improved convergence in the non-iid setting, and in both iid and non-iid settings reduces the communication volume needed to reach a given accuracy. Reading List: Attacks on machine learners: Overview of the field: [1] Concrete problems in AI safety . Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané. Adversarial examples in deep learning: [2] Explaining and harnessing adversarial examples . Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy. [3] Practical black-box attacks against deep learning systems using adversarial examples . Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami. [4] Towards Evaluating the Robustness of Neural Networks . Nicholas Carlini, David Wagner. Previously proposed defenses: [5] Biologically inspired protection of deep networks from adversarial attacks . Aran Nayebi, Surya Ganguli. [6] Parseval Networks: Improving Robustness to Adversarial Examples . Cisse Moustapha, Bojanowski Piotr, Grave Edouard, Dauphin Yann, Usunier Nicolas. Deep generative models: [7] Generative Adversarial Networks . Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. [8] Auto-Encoding Variational Bayes . Diederik P Kingma, Max Welling. Non-iid and distributed deep learning: The Laplace approximation: [9] A practical Bayesian framework for backpropogation networks . David J. C. MacKay. Using the Laplace approximation to estimating weight importances in deep learning: [10] Optimal Brain Damage . Yann LeCun, John S. Denker, Sara A. Solla. [11] Overcoming catastrophic forgetting in neural networks . James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell. Federated learning: [12] Communication-Efficient Learning of Deep Networks from Decentralized Data . H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas. [13] Federated Learning: Strategies for Improving Communication Efficiency . Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon. Textbook: [14] Deep Learning . Ian Goodfellow, Yoshua Bengio, Aaron Courville.

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Ghassen Jerfel will present his MSE talk on Monday, May 15th, 2017 at 8:15am in CS 302.
by Nicki Gotsis 08 May '17

08 May '17

Ghassen Jerfel will present his MSE talk on Monday, May 15th, 2017 at 8:15am in CS 302. The members of his committee are Barbara Engelhardt (adviser) and Elad Hazan. The title and abstract of his talk are below. All are welcome to attend. Title: Combining multi-modality and non-linearity for density estimation: Boosted Stochastic Backpropagation for Variational Inference Abstract: Deep Generative Models, such as Variational Autoencoders, couple the structured and regularized latent variable representations of probabilistic graphical models with nonlinear likelihoods to learn flexible representations of complex high-dimensional data. Variational inference provides fast approximation of the posterior for VAEs as it formulates the problem as an optimization problem to find the closest posterior over some parametric family of distributions. However, general VI theory and standard VAE literature restrict the approximation family such that it does not contain the true posterior regardless of how long VI is run (e.g. limited to unimodal diagonal Gaussians for VAEs). In this paper, we propose a model-free, simple to implement, and easily parallelizable meta-algorithm which may be used in conjunction with recent VI advances. Based on the functional gradient descent view of boosting, our algorithm iteratively improves the variational approximation of the posterior and is guaranteed to converge at a rate of O(1/k) to the true posterior. We derive boosted Gaussian stochastic backpropagation as a principled method to incorporate and mix new encoders into a VAE model to refine the estimation of the latent space. We study boosted stochastic backpropagation on standard benchmarks for unsupervised learning, applying it to density estimation tasks on synthetic data, the MNIST, NORB, and OMNIGLOT datasets. We demonstrate competitive results that emphasize the advantage of iterative refinement and multi-modality in DGMs.

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Riley Simmons-Edler will present his general exam on Friday, May 12, 2017 in CS 401 at 10am.
by Nicki Gotsis 08 May '17

08 May '17

Riley Simmons-Edler will present his general exam on Friday, May 12, 2017 in CS 401 at 10am. The members of his committee are: Tom Funkhouser, Sebastian Seung, and Szymon Rusinkiewicz. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Title: "Self-Supervised and Adversarial Image Intrinsic Decomposition" Abstract: Image decomposition into intrinsic components, such as albedo and lighting, is a highly underconstrained problem. For any given image, there are infinitely many possible predictions that satisfy the relationship albedo * lighting = color. However, as humans we can define a distribution of "plausible" predictions for a given image using a combination of our internal model of what a valid intrinsic looks like and the known content of the original image. It is therefore reasonable to think it is viable to train a deep neural network to do this task by constraining the output in a similar fashion. Here, I present my work on using self-supervision and adversarial neural networks to learn an albedo and lighting decomposition for real photographs, where no ground truth data exists nor can exist. By using an adversarial network trained on a distribution of synthetic albedo and lighting, I project a given color image into the space of possible intrinsics, while using self-supervision via a reconstruction loss from the intrinsics to the input image to constrain the predictions to reproduce the input image. This combination of loss terms makes it possible to learn an accurate transformation from color input to intrinsic factors without any paired training data, allowing for training directly on real images and thus improved performance compared to previous approaches which can only use synthetic training data. Textbook: "Deep Learning," Goodfellow, Bengio, and Courville, 2016 Papers: Directly Related to my project(methods and previous application works): "Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression," Narihira, Maire, and Yu, 2015 "DARN: a Deep Adversarial Residual Network for Intrinsic Image Decomposition," Lettry, Vanhoey, and Van Gool, 2016 "Wasserstein GAN," Arjovsky, Chintala, and Bottou, 2017 "Multi-Scale Context Aggregation by Dilated Convolutions," Yu and Koltun, 2016 Other Similar Influential Works(in image to image translation): "Learning from Simulated and Unsupervised Images through Adversarial Training," Shrivastava et al, 2016 "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks," Zhu, Park, Isola, and Efros, 2017 Classic Papers: "Generative Adversarial Nets," Goodfellow et al, 2014 "Imagenet Classification with Deep Convolutional Neural Networks," Krizhevsky, Sutskever, and Hinton, 2012 "Backpropagation Applied to Handwritten Zip Code Recognition," LeCun et al, 1989 (the original convolutional neural networks paper)

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REMINDER - ELE Seminar Today, 4:30 pm in B205
by Nicole E. Wagenblast 08 May '17

08 May '17

Speaker: Pramod Viswanath, University of Illinois Title: Geometries of Word Embeddings Day: Monday, May 8, 2017 Time: 4:30 pm Room: B205, E-Quad Host: Prof. Sergio Verdu Abstract: Real-valued word vectors have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities via simple geometrical operations. In this talk, we demonstrate further striking geometrical properties of the word vectors. First we show that a very simple, and yet counter-intuitive, postprocessing technique, which makes the vectors "more isotropic", renders off-the-shelf vectors even stronger. Second, we show that a sentence containing a target word is well represented by a low rank subspace; subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace). We harness this Grassmannian geometry to disambiguate (in an unsupervised way) multiple senses of words, specifically so on the most promiscuously polysemous of all words: prepositions. A surprising finding is that rare senses, including idiomatic/sarcastic/metaphorical usages, are efficiently captured. Our algorithms are all unsupervised and rely on no linguistic resources; we validate them by presenting new state-of-the-art results on a variety of multilingual benchmark datasets. References: 1. Geometry of Compositionality, AAAI '17, https://arxiv.org/abs/1611.09799 2. Geometry of Polysemy, ICLR, '17, https://arxiv.org/abs/1610.07569 3. Prepositions in Context, preprint, https://arxiv.org/abs/1702.01466 Bio: Pramod Viswanath received the Ph.D. degree in electrical engineering and computer science from University of California at Berkeley in 2000. From 2000 to 2001, he was a member of research staff at Flarion Technologies, NJ. Since 2001, he is on the faculty at University of Illinois at Urbana Champaign in Electrical and Computer Engineering, where he currently is a professor. Pramod has worked extensively on information theory and its applications, specifically as applied to wireless communication. His book on wireless communication, coauthored with David Tse, is used in more than 60 institutes around the world. His interest in natural language processing is recent, although he has long been interested in natural languages. Save

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Andy Zeng will present his generals exam Tuesday, May 16, 2017 in CS 302 at 11am.
by Nicki Gotsis 08 May '17

08 May '17

BQ_BEGIN Andy Zeng will present his generals exam Tuesday, May 16, 2017 in CS 302 at 11am. The members of his committee are: Tom Funkhouser (adviser), Szymon Rusinkiewicz, and Elad Hazan. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Title: Self-Supervised Local 3D Geometric Descriptors and RGB-D Object Segmentation Abstract: In this talk, I will present two projects where we proposed unique methods for labeling massive amounts of data automatically. These methods enabled us to train strong data-driven models for RGB-D object segmentation and local 3D descriptors. In the first project, we present a vision system to recognize objects and their poses under noisy and cluttered environments. More specifically, our approach first segments and labels multiple RGB-D views of a scene with a fully convolutional neural network, and then fits pre-scanned 3D object models to the resulting segmentation to get the 6D object poses. However, training a deep neural network for segmentation typically requires a large amount of training data with manual labels. To alleviate this difficulty, we present an automatic method to generate a large labeled segmentation dataset without tedious manual annotations that could be scaled up to more object categories easily. Our system is reliable under a variety of scenarios including warehouse automation, and is the 3rd place winning system in the world-wide Amazon Robotics Challenge 2016. In the second project, we present a data-driven local 3D volumetric patch descriptor for matching local geometric features on noisy, low-resolution, and incomplete depth images from commodity range sensors. To amass training data for our model, we present an automatic method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Textbook: Computer Vision: Algorithms and Applications [FPFH] Fast point feature histograms (fpfh) for 3d registration [SIFT] Object Recognition from Local Scale-Invariant Features. [SpinImage] Using spin images for efficient object recognition in cluttered 3d scenes [KinectFusion] KinectFusion: Real-Time Dense Surface Mapping and Tracking [PoseEstimation] Learning 6D Object Pose Estimation using 3D Object Coordinates [MatchNet] MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching [Egomotion] Learning to See by Moving [FCN] Fully Convolutional Networks for Semantic Segmentation [Survey3DDescriptors] Performance Evaluation of 3D Local Feature Descriptors [Grasping] Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours BQ_END

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