May 6, 2019

2984 words 15 mins read

Paper Group ANR 358

Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform. Coordination in Categorical Compositional Distributional Semantics. Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach. BrainFrame: A node-level heterogeneous accelerator platform for n …

Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform


Title	Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform
Authors	Bowen Zhou, Shahriar Shariat
Abstract	Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this paper we describe the mechanism that we invented for recovering the representative metrics and detecting the change in their behavior. We show that this mechanism is able to detect the possible problems in time by describing some incident cases.
Tasks	Anomaly Detection
Published	2016-02-23
URL	http://arxiv.org/abs/1602.07057v1
PDF	http://arxiv.org/pdf/1602.07057v1.pdf
PWC	https://paperswithcode.com/paper/finding-needle-in-a-million-metrics-anomaly
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Framework

Coordination in Categorical Compositional Distributional Semantics


Title	Coordination in Categorical Compositional Distributional Semantics
Authors	Dimitri Kartsaklis
Abstract	An open problem with categorical compositional distributional semantics is the representation of words that are considered semantically vacuous from a distributional perspective, such as determiners, prepositions, relative pronouns or coordinators. This paper deals with the topic of coordination between identical syntactic types, which accounts for the majority of coordination cases in language. By exploiting the compact closed structure of the underlying category and Frobenius operators canonically induced over the fixed basis of finite-dimensional vector spaces, we provide a morphism as representation of a coordinator tensor, and we show how it lifts from atomic types to compound types. Linguistic intuitions are provided, and the importance of the Frobenius operators as an addition to the compact closed setting with regard to language is discussed.
Tasks
Published	2016-06-05
URL	http://arxiv.org/abs/1606.01515v2
PDF	http://arxiv.org/pdf/1606.01515v2.pdf
PWC	https://paperswithcode.com/paper/coordination-in-categorical-compositional
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Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach


Title	Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach
Authors	Zhi Lu, Gustavo Carneiro, Neeraj Dhungel, Andrew P. Bradley
Abstract	In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\mu$C). Currently, the most effective methods are based on three steps: 1) detection of individual $\mu$C candidates, 2) clustering of individual $\mu$C candidates, and 3) classification of $\mu$C clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends on a relatively large number of $\mu$C detections within a certain area. In this paper, we propose a novel approach to $\mu$C detection, consisting of the detection \emph{and} classification of individual $\mu$C candidates, using shape and appearance features, using a cascade of boosting classifiers. The final step in our approach then clusters the remaining individual $\mu$C candidates. The main advantage of this approach lies in its ability to reject a significant number of false positive $\mu$C candidates compared to previously proposed methods. Specifically, on the INbreast dataset, we show that our approach has a true positive rate (TPR) for individual $\mu$Cs of 40% at one false positive per image (FPI) and a TPR of 80% at 10 FPI. These results are significantly more accurate than the current state of the art, which has a TPR of less than 1% at one FPI and a TPR of 10% at 10 FPI. Our results are competitive with the state of the art at the subsequent stage of detecting clusters of $\mu$Cs.
Tasks
Published	2016-10-07
URL	http://arxiv.org/abs/1610.02251v1
PDF	http://arxiv.org/pdf/1610.02251v1.pdf
PWC	https://paperswithcode.com/paper/automated-detection-of-individual-micro
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BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations


Title	BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Authors	Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harry Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis
Abstract	Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a single acceleration (or homogeneous) platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different instances of a state-of-the-art neuron model, modeling the Inferior- Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal- network dimensions but also different network-connectivity circumstances that can drastically change application workload characteristics. Main results: The synthetic approach of three HPC technologies demonstrated that BrainFrame is better able to cope with the modeling diversity encountered. Our performance analysis shows clearly that the model directly affect performance and all three technologies are required to cope with all the model use cases.
Tasks
Published	2016-12-05
URL	http://arxiv.org/abs/1612.01501v4
PDF	http://arxiv.org/pdf/1612.01501v4.pdf
PWC	https://paperswithcode.com/paper/brainframe-a-node-level-heterogeneous
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Large Scale SfM with the Distributed Camera Model


Title	Large Scale SfM with the Distributed Camera Model
Authors	Chris Sweeney, Victor Fragoso, Tobias Hollerer, Matthew Turk
Abstract	We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM). This model describes image observations in terms of light rays with ray origins and directions rather than pixels. As such, the proposed model is capable of describing a single camera or multiple cameras simultaneously as the collection of all light rays observed. We show how the distributed camera model is a generalization of the standard camera model and describe a general formulation and solution to the absolute camera pose problem that works for standard or distributed cameras. The proposed method computes a solution that is up to 8 times more efficient and robust to rotation singularities in comparison with gDLS. Finally, this method is used in an novel large-scale incremental SfM pipeline where distributed cameras are accurately and robustly merged together. This pipeline is a direct generalization of traditional incremental SfM; however, instead of incrementally adding one camera at a time to grow the reconstruction the reconstruction is grown by adding a distributed camera. Our pipeline produces highly accurate reconstructions efficiently by avoiding the need for many bundle adjustment iterations and is capable of computing a 3D model of Rome from over 15,000 images in just 22 minutes.
Tasks
Published	2016-07-13
URL	http://arxiv.org/abs/1607.03949v2
PDF	http://arxiv.org/pdf/1607.03949v2.pdf
PWC	https://paperswithcode.com/paper/large-scale-sfm-with-the-distributed-camera
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Framework

End-to-End Learning for Image Burst Deblurring


Title	End-to-End Learning for Image Burst Deblurring
Authors	Patrick Wieschollek, Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch
Abstract	We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.
Tasks	Deblurring
Published	2016-07-15
URL	http://arxiv.org/abs/1607.04433v2
PDF	http://arxiv.org/pdf/1607.04433v2.pdf
PWC	https://paperswithcode.com/paper/end-to-end-learning-for-image-burst
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Framework

Richardson-Lucy Deblurring for Moving Light Field Cameras


Title	Richardson-Lucy Deblurring for Moving Light Field Cameras
Authors	Donald G. Dansereau, Anders Eriksson, Jürgen Leitner
Abstract	We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation. We introduce a novel regularization term that maintains parallax information in the light field while reducing noise and ringing. We demonstrate the method operating effectively on rendered scenes and scenes captured using an off-the-shelf light field camera. An industrial robot arm provides repeatable and known trajectories, allowing us to establish quantitative performance in complex 3-D scenes. Qualitative and quantitative results confirm the effectiveness of the method, including commonly occurring cases for which previously published methods fail. We include mathematical proof that the algorithm converges to the maximum-likelihood estimate of the unblurred scene under Poisson noise. We expect extension to blind methods to be possible following the generalization of 2-D Richardson-Lucy to blind deconvolution.
Tasks	Deblurring, Depth Estimation
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04308v2
PDF	http://arxiv.org/pdf/1606.04308v2.pdf
PWC	https://paperswithcode.com/paper/richardson-lucy-deblurring-for-moving-light
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Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems


Title	Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems
Authors	Thomas Möllenhoff, Daniel Cremers
Abstract	In this work we show how sublabel-accurate multilabeling approaches can be derived by approximating a classical label-continuous convex relaxation of nonconvex free-discontinuity problems. This insight allows to extend these sublabel-accurate approaches from total variation to general convex and nonconvex regularizations. Furthermore, it leads to a systematic approach to the discretization of continuous convex relaxations. We study the relationship to existing discretizations and to discrete-continuous MRFs. Finally, we apply the proposed approach to obtain a sublabel-accurate and convex solution to the vectorial Mumford-Shah functional and show in several experiments that it leads to more precise solutions using fewer labels.
Tasks
Published	2016-11-21
URL	http://arxiv.org/abs/1611.06987v2
PDF	http://arxiv.org/pdf/1611.06987v2.pdf
PWC	https://paperswithcode.com/paper/sublabel-accurate-discretization-of-nonconvex
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Why is Posterior Sampling Better than Optimism for Reinforcement Learning?


Title	Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Authors	Ian Osband, Benjamin Van Roy
Abstract	Computational results demonstrate that posterior sampling for reinforcement learning (PSRL) dramatically outperforms algorithms driven by optimism, such as UCRL2. We provide insight into the extent of this performance boost and the phenomenon that drives it. We leverage this insight to establish an $\tilde{O}(H\sqrt{SAT})$ Bayesian expected regret bound for PSRL in finite-horizon episodic Markov decision processes, where $H$ is the horizon, $S$ is the number of states, $A$ is the number of actions and $T$ is the time elapsed. This improves upon the best previous bound of $\tilde{O}(H S \sqrt{AT})$ for any reinforcement learning algorithm.
Tasks
Published	2016-07-01
URL	http://arxiv.org/abs/1607.00215v3
PDF	http://arxiv.org/pdf/1607.00215v3.pdf
PWC	https://paperswithcode.com/paper/why-is-posterior-sampling-better-than
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Training Recurrent Neural Networks by Diffusion


Title	Training Recurrent Neural Networks by Diffusion
Authors	Hossein Mobahi
Abstract	This work presents a new algorithm for training recurrent neural networks (although ideas are applicable to feedforward networks as well). The algorithm is derived from a theory in nonconvex optimization related to the diffusion equation. The contributions made in this work are two fold. First, we show how some seemingly disconnected mechanisms used in deep learning such as smart initialization, annealed learning rate, layerwise pretraining, and noise injection (as done in dropout and SGD) arise naturally and automatically from this framework, without manually crafting them into the algorithms. Second, we present some preliminary results on comparing the proposed method against SGD. It turns out that the new algorithm can achieve similar level of generalization accuracy of SGD in much fewer number of epochs.
Tasks
Published	2016-01-16
URL	http://arxiv.org/abs/1601.04114v2
PDF	http://arxiv.org/pdf/1601.04114v2.pdf
PWC	https://paperswithcode.com/paper/training-recurrent-neural-networks-by
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Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives


Title	Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives
Authors	Yusuke Uchida, Shigeyuki Sakazawa, Shin’ichi Satoh
Abstract	In this paper, we propose a stand-alone mobile visual search system based on binary features and the bag-of-visual words framework. The contribution of this study is three-fold: (1) We propose an adaptive substring extraction method that adaptively extracts informative bits from the original binary vector and stores them in the inverted index. These substrings are used to refine visual word-based matching. (2) A modified local NBNN scoring method is proposed in the context of image retrieval, which considers the density of binary features in scoring each feature matching. (3) In order to suppress false positives, we introduce a convexity check step that imposes a convexity constraint on the configuration of a transformed reference image. The proposed system improves retrieval accuracy by 11% compared with a conventional method without increasing the database size. Furthermore, our system with the convexity check does not lead to false positive results.
Tasks	Image Retrieval
Published	2016-10-20
URL	http://arxiv.org/abs/1610.06266v1
PDF	http://arxiv.org/pdf/1610.06266v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-substring-extraction-and-modified
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Framework

Limits to Verification and Validation of Agentic Behavior


Title	Limits to Verification and Validation of Agentic Behavior
Authors	David J. Jilk
Abstract	Verification and validation of agentic behavior have been suggested as important research priorities in efforts to reduce risks associated with the creation of general artificial intelligence (Russell et al 2015). In this paper we question the appropriateness of using language of certainty with respect to efforts to manage that risk. We begin by establishing a very general formalism to characterize agentic behavior and to describe standards of acceptable behavior. We show that determination of whether an agent meets any particular standard is not computable. We discuss the extent of the burden associated with verification by manual proof and by automated behavioral governance. We show that to ensure decidability of the behavioral standard itself, one must further limit the capabilities of the agent. We then demonstrate that if our concerns relate to outcomes in the physical world, attempts at validation are futile. Finally, we show that layered architectures aimed at making these challenges tractable mistakenly equate intentions with actions or outcomes, thereby failing to provide any guarantees. We conclude with a discussion of why language of certainty should be eradicated from the conversation about the safety of general artificial intelligence.
Tasks
Published	2016-04-23
URL	http://arxiv.org/abs/1604.06963v2
PDF	http://arxiv.org/pdf/1604.06963v2.pdf
PWC	https://paperswithcode.com/paper/limits-to-verification-and-validation-of
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Framework

You Are What You Eat… Listen to, Watch, and Read


Title	You Are What You Eat… Listen to, Watch, and Read
Authors	Mason Bretan
Abstract	This article describes a data driven method for deriving the relationship between personality and media preferences. A qunatifiable representation of such a relationship can be leveraged for use in recommendation systems and ameliorate the “cold start” problem. Here, the data is comprised of an original collection of 1,316 Okcupid dating profiles. Of these profiles, 800 are labeled with one of 16 possible Myers-Briggs Type Indicators (MBTI). A personality specific topic model describing a person’s favorite books, movies, shows, music, and food was generated using latent Dirichlet allocation (LDA). There were several significant findings, for example, intuitive thinking types preferred sci-fi/fantasy entertainment, extraversion correlated positively with upbeat dance music, and jazz, folk, and international cuisine correlated positively with those characterized by openness to experience. Many other correlations confirmed previous findings describing the relationship among personality, writing style, and personal preferences. (For complete word/personality type assocations see the Appendix).
Tasks	Recommendation Systems
Published	2016-12-13
URL	http://arxiv.org/abs/1612.04403v1
PDF	http://arxiv.org/pdf/1612.04403v1.pdf
PWC	https://paperswithcode.com/paper/you-are-what-you-eat-listen-to-watch-and-read
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Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies


Title	Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Authors	Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Abstract	Planning plays an important role in the broad class of decision theory. Planning has drawn much attention in recent work in the robotics and sequential decision making areas. Recently, Reinforcement Learning (RL), as an agent-environment interaction problem, has brought further attention to planning methods. Generally in RL, one can assume a generative model, e.g. graphical models, for the environment, and then the task for the RL agent is to learn the model parameters and find the optimal strategy based on these learnt parameters. Based on environment behavior, the agent can assume various types of generative models, e.g. Multi Armed Bandit for a static environment, or Markov Decision Process (MDP) for a dynamic environment. The advantage of these popular models is their simplicity, which results in tractable methods of learning the parameters and finding the optimal policy. The drawback of these models is again their simplicity: these models usually underfit and underestimate the actual environment behavior. For example, in robotics, the agent usually has noisy observations of the environment inner state and MDP is not a suitable model. More complex models like Partially Observable Markov Decision Process (POMDP) can compensate for this drawback. Fitting this model to the environment, where the partial observation is given to the agent, generally gives dramatic performance improvement, sometimes unbounded improvement, compared to MDP. In general, finding the optimal policy for the POMDP model is computationally intractable and fully non convex, even for the class of memoryless policies. The open problem is to come up with a method to find an exact or an approximate optimal stochastic memoryless policy for POMDP models.
Tasks	Decision Making
Published	2016-08-17
URL	http://arxiv.org/abs/1608.04996v1
PDF	http://arxiv.org/pdf/1608.04996v1.pdf
PWC	https://paperswithcode.com/paper/open-problem-approximate-planning-of-pomdps
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Reversible Image Merging for Low-level Machine Vision


Title	Reversible Image Merging for Low-level Machine Vision
Authors	Mikhail Kharinov
Abstract	In this paper a hierarchical model for pixel clustering and image segmentation is developed. In the model an image is hierarchically structured. The original image is treated as a set of nested images, which are capable to reversibly merge with each other. An object is defined as a structural element of an image, so that, an image is regarded as a maximal object. The simulating of none-hierarchical optimal pixel clustering by hierarchical clustering is studied. To generate a hierarchy of optimized piecewise constant image approximations, estimated by the standard deviation of approximation from the image, the conversion of any hierarchy of approximations into the hierarchy described in relation to the number of intensity levels by convex sequence of total squared errors is proposed.
Tasks	Semantic Segmentation
Published	2016-04-13
URL	http://arxiv.org/abs/1604.03832v1
PDF	http://arxiv.org/pdf/1604.03832v1.pdf
PWC	https://paperswithcode.com/paper/reversible-image-merging-for-low-level
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