May 5, 2019

3196 words 16 mins read

Paper Group ANR 493

Machine Learning Techniques and Applications For Ground-based Image Analysis. Do Cascades Recur?. Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks. Learning in Games: Robustness of Fast Convergence. CoPaSul Manual - Contour-based parametric and superpositional intonation stylization. Deep-Lear …

Machine Learning Techniques and Applications For Ground-based Image Analysis


Title	Machine Learning Techniques and Applications For Ground-based Image Analysis
Authors	Soumyabrata Dev, Bihan Wen, Yee Hui Lee, Stefan Winkler
Abstract	Ground-based whole sky cameras have opened up new opportunities for monitoring the earth’s atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole sky imagers can have high spatial and temporal resolution, which is an important pre-requisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, etc. Extracting valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine learning techniques have become available to aid with the image analysis. This article provides a detailed walk-through of recent developments in these techniques and their applications in ground-based imaging. We aim to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applications – segmentation, classification, and denoising.
Tasks	Denoising
Published	2016-06-09
URL	http://arxiv.org/abs/1606.02811v1
PDF	http://arxiv.org/pdf/1606.02811v1.pdf
PWC	https://paperswithcode.com/paper/machine-learning-techniques-and-applications
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Do Cascades Recur?


Title	Do Cascades Recur?
Authors	Justin Cheng, Lada A Adamic, Jon Kleinberg, Jure Leskovec
Abstract	Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Facebook over significantly longer time scales, and find that a more complex picture emerges, in which many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade’s initial burst, we demonstrate strong performance in predicting whether it will recur in the future.
Tasks
Published	2016-02-02
URL	http://arxiv.org/abs/1602.01107v1
PDF	http://arxiv.org/pdf/1602.01107v1.pdf
PWC	https://paperswithcode.com/paper/do-cascades-recur
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Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks


Title	Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks
Authors	Saman Sarraf, Ghassem Tofighi
Abstract	Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is a powerful machine learning algorithm in classification while extracting low to high-level features. In this paper, we used convolutional neural network to classify Alzheimer’s brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer’s disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified structural MRI data of Alzheimer’s subjects from normal controls where the accuracy of test data on trained data reached 98.84%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.
Tasks
Published	2016-07-22
URL	http://arxiv.org/abs/1607.06583v2
PDF	http://arxiv.org/pdf/1607.06583v2.pdf
PWC	https://paperswithcode.com/paper/classification-of-alzheimers-disease
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Learning in Games: Robustness of Fast Convergence


Title	Learning in Games: Robustness of Fast Convergence
Authors	Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
Abstract	We show that learning algorithms satisfying a $\textit{low approximate regret}$ property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a $(1+\epsilon)$-multiplicative approximation to the best action in hindsight, is ubiquitous among learning algorithms; it is satisfied even by the vanilla Hedge forecaster. Our results improve upon recent work of Syrgkanis et al. [SALS15] in a number of ways. We require only that players observe payoffs under other players’ realized actions, as opposed to expected payoffs. We further show that convergence occurs with high probability, and show convergence under bandit feedback. Finally, we improve upon the speed of convergence by a factor of $n$, the number of players. Both the scope of settings and the class of algorithms for which our analysis provides fast convergence are considerably broader than in previous work. Our framework applies to dynamic population games via a low approximate regret property for shifting experts. Here we strengthen the results of Lykouris et al. [LST16] in two ways: We allow players to select learning algorithms from a larger class, which includes a minor variant of the basic Hedge algorithm, and we increase the maximum churn in players for which approximate optimality is achieved. In the bandit setting we present a new algorithm which provides a “small loss”-type bound with improved dependence on the number of actions in utility settings, and is both simple and efficient. This result may be of independent interest.
Tasks
Published	2016-06-20
URL	http://arxiv.org/abs/1606.06244v4
PDF	http://arxiv.org/pdf/1606.06244v4.pdf
PWC	https://paperswithcode.com/paper/learning-in-games-robustness-of-fast
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CoPaSul Manual - Contour-based parametric and superpositional intonation stylization


Title	CoPaSul Manual - Contour-based parametric and superpositional intonation stylization
Authors	Uwe D. Reichel
Abstract	The purposes of the CoPaSul toolkit are (1) automatic prosodic annotation and (2) prosodic feature extraction from syllable to utterance level. CoPaSul stands for contour-based, parametric, superpositional intonation stylization. In this framework intonation is represented as a superposition of global and local contours that are described parametrically in terms of polynomial coefficients. On the global level (usually associated but not necessarily restricted to intonation phrases) the stylization serves to represent register in terms of time-varying F0 level and range. On the local level (e.g. accent groups), local contour shapes are described. From this parameterization several features related to prosodic boundaries and prominence can be derived. Furthermore, by coefficient clustering prosodic contour classes can be obtained in a bottom-up way. Next to the stylization-based feature extraction also standard F0 and energy measures (e.g. mean and variance) as well as rhythmic aspects can be calculated. At the current state automatic annotation comprises: segmentation into interpausal chunks, syllable nucleus extraction, and unsupervised localization of prosodic phrase boundaries and prominent syllables. F0 and partly also energy feature sets can be derived for: standard measurements (as median and IQR), register in terms of F0 level and range, prosodic boundaries, local contour shapes, bottom-up derived contour classes, Gestalt of accent groups in terms of their deviation from higher level prosodic units, as well as for rhythmic aspects quantifying the relation between F0 and energy contours and prosodic event rates.
Tasks
Published	2016-12-14
URL	http://arxiv.org/abs/1612.04765v9
PDF	http://arxiv.org/pdf/1612.04765v9.pdf
PWC	https://paperswithcode.com/paper/copasul-manual-contour-based-parametric-and
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Title	Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
Authors	Pinxin Long, Wenxi Liu, Jia Pan
Abstract	High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent’s navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent’s steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.
Tasks
Published	2016-09-22
URL	http://arxiv.org/abs/1609.06838v2
PDF	http://arxiv.org/pdf/1609.06838v2.pdf
PWC	https://paperswithcode.com/paper/deep-learned-collision-avoidance-policy-for
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A Continuous Optimization Approach for Efficient and Accurate Scene Flow


Title	A Continuous Optimization Approach for Efficient and Accurate Scene Flow
Authors	Zhaoyang Lv, Chris Beall, Pablo F. Alcantarilla, Fuxin Li, Zsolt Kira, Frank Dellaert
Abstract	We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery. As in recent work, we represent the dynamic 3D scene as a collection of rigidly moving planar segments. The scene flow problem then becomes the joint estimation of pixel-to-segment assignment, 3D position, normal vector and rigid motion parameters for each segment, leading to a complex and expensive discrete-continuous optimization problem. In contrast, we propose a purely continuous formulation which can be solved more efficiently. Using a fine superpixel segmentation that is fixed a-priori, we propose a factor graph formulation that decomposes the problem into photometric, geometric, and smoothing constraints. We initialize the solution with a novel, high-quality initialization method, then independently refine the geometry and motion of the scene, and finally perform a global non-linear refinement using Levenberg-Marquardt. We evaluate our method in the challenging KITTI Scene Flow benchmark, ranking in third position, while being 3 to 30 times faster than the top competitors.
Tasks
Published	2016-07-27
URL	http://arxiv.org/abs/1607.07983v1
PDF	http://arxiv.org/pdf/1607.07983v1.pdf
PWC	https://paperswithcode.com/paper/a-continuous-optimization-approach-for
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Conversational Contextual Cues: The Case of Personalization and History for Response Ranking


Title	Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
Authors	Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
Abstract	We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant’s history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.
Tasks
Published	2016-06-01
URL	http://arxiv.org/abs/1606.00372v1
PDF	http://arxiv.org/pdf/1606.00372v1.pdf
PWC	https://paperswithcode.com/paper/conversational-contextual-cues-the-case-of
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Framework

On Deterministic Conditions for Subspace Clustering under Missing Data


Title	On Deterministic Conditions for Subspace Clustering under Missing Data
Authors	Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal
Abstract	In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model. We consider two algorithms, which are variants of SSC with entry-wise zero-filling that differ in terms of the optimization problems used to find affinity matrix for spectral clustering. For both the algorithms, we provide deterministic conditions for any pattern of missing data such that perfect clustering can be achieved. We provide extensive sets of simulation results for clustering as well as completion of data at missing entries, under the UoS model. Our experimental results indicate that in contrast to the full data case, accurate clustering does not imply accurate subspace identification and completion, indicating the natural order of relative hardness of these problems.
Tasks
Published	2016-07-11
URL	http://arxiv.org/abs/1607.03191v1
PDF	http://arxiv.org/pdf/1607.03191v1.pdf
PWC	https://paperswithcode.com/paper/on-deterministic-conditions-for-subspace-1
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Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning


Title	Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning
Authors	Ran Raz
Abstract	We prove that any algorithm for learning parities requires either a memory of quadratic size or an exponential number of samples. This proves a recent conjecture of Steinhardt, Valiant and Wager and shows that for some learning problems a large storage space is crucial. More formally, in the problem of parity learning, an unknown string $x \in {0,1}^n$ was chosen uniformly at random. A learner tries to learn $x$ from a stream of samples $(a_1, b_1), (a_2, b_2) \ldots$, where each~$a_t$ is uniformly distributed over ${0,1}^n$ and $b_t$ is the inner product of $a_t$ and $x$, modulo~2. We show that any algorithm for parity learning, that uses less than $\frac{n^2}{25}$ bits of memory, requires an exponential number of samples. Previously, there was no non-trivial lower bound on the number of samples needed, for any learning problem, even if the allowed memory size is $O(n)$ (where $n$ is the space needed to store one sample). We also give an application of our result in the field of bounded-storage cryptography. We show an encryption scheme that requires a private key of length $n$, as well as time complexity of $n$ per encryption/decription of each bit, and is provenly and unconditionally secure as long as the attacker uses less than $\frac{n^2}{25}$ memory bits and the scheme is used at most an exponential number of times. Previous works on bounded-storage cryptography assumed that the memory size used by the attacker is at most linear in the time needed for encryption/decription.
Tasks
Published	2016-02-16
URL	http://arxiv.org/abs/1602.05161v1
PDF	http://arxiv.org/pdf/1602.05161v1.pdf
PWC	https://paperswithcode.com/paper/fast-learning-requires-good-memory-a-time
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Framework

Deep Convolutional Neural Networks on Cartoon Functions


Title	Deep Convolutional Neural Networks on Cartoon Functions
Authors	Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei
Abstract	Wiatowski and B"olcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities. While the translation invariance result applies to square-integrable functions, the deformation stability bound holds for band-limited functions only. Many signals of practical relevance (such as natural images) exhibit, however, sharp and curved discontinuities and are, hence, not band-limited. The main contribution of this paper is a deformation stability result that takes these structural properties into account. Specifically, we establish deformation stability bounds for the class of cartoon functions introduced by Donoho, 2001.
Tasks
Published	2016-04-29
URL	http://arxiv.org/abs/1605.00031v2
PDF	http://arxiv.org/pdf/1605.00031v2.pdf
PWC	https://paperswithcode.com/paper/deep-convolutional-neural-networks-on-cartoon
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Framework


Title	Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality
Authors	Khadija Tijani, Stephane Ploix, Benjamin Haas, Julie Dugdale, Quoc Dung Ngo
Abstract	This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour with probabilistic cause-effect relations based on knowledge, but also with conditional probabilities coming either from knowledge or deduced from observations. This approach has been applied to the co-simulation of the CO2 concentration in an office coupled with human behaviour.
Tasks
Published	2016-05-19
URL	http://arxiv.org/abs/1605.05966v1
PDF	http://arxiv.org/pdf/1605.05966v1.pdf
PWC	https://paperswithcode.com/paper/dynamic-bayesian-networks-to-simulate
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Benchmarking State-of-the-Art Deep Learning Software Tools


Title	Benchmarking State-of-the-Art Deep Learning Software Tools
Authors	Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu
Abstract	Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training different types of deep networks on different hardware platforms, which makes it difficult for end users to select an appropriate pair of software and hardware. In this paper, we aim to make a comparative study of the state-of-the-art GPU-accelerated deep learning software tools, including Caffe, CNTK, MXNet, TensorFlow, and Torch. We first benchmark the running performance of these tools with three popular types of neural networks on two CPU platforms and three GPU platforms. We then benchmark some distributed versions on multiple GPUs. Our contribution is two-fold. First, for end users of deep learning tools, our benchmarking results can serve as a guide to selecting appropriate hardware platforms and software tools. Second, for software developers of deep learning tools, our in-depth analysis points out possible future directions to further optimize the running performance.
Tasks
Published	2016-08-25
URL	http://arxiv.org/abs/1608.07249v7
PDF	http://arxiv.org/pdf/1608.07249v7.pdf
PWC	https://paperswithcode.com/paper/benchmarking-state-of-the-art-deep-learning
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An Innovative Imputation and Classification Approach for Accurate Disease Prediction


Title	An Innovative Imputation and Classification Approach for Accurate Disease Prediction
Authors	Yelipe UshaRani, P. Sammulal
Abstract	Imputation of missing attribute values in medical datasets for extracting hidden knowledge from medical datasets is an interesting research topic of interest which is very challenging. One cannot eliminate missing values in medical records. The reason may be because some tests may not been conducted as they are cost effective, values missed when conducting clinical trials, values may not have been recorded to name some of the reasons. Data mining researchers have been proposing various approaches to find and impute missing values to increase classification accuracies so that disease may be predicted accurately. In this paper, we propose a novel imputation approach for imputation of missing values and performing classification after fixing missing values. The approach is based on clustering concept and aims at dimensionality reduction of the records. The case study discussed shows that missing values can be fixed and imputed efficiently by achieving dimensionality reduction. The importance of proposed approach for classification is visible in the case study which assigns single class label in contrary to multi-label assignment if dimensionality reduction is not performed.
Tasks	Dimensionality Reduction, Disease Prediction, Imputation
Published	2016-03-10
URL	http://arxiv.org/abs/1603.03281v1
PDF	http://arxiv.org/pdf/1603.03281v1.pdf
PWC	https://paperswithcode.com/paper/an-innovative-imputation-and-classification
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A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion


Title	A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Authors	Johannes Welbl, Guillaume Bouchard, Sebastian Riedel
Abstract	Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links. Facts can however also be represented using pairwise embeddings, i.e. embeddings for pairs of entities and relations. In this paper we explore such bigram embeddings with a flexible Factorization Machine model and several ablations from it. We investigate the relevance of various bigram types on the fb15k237 dataset and find relative improvements compared to a compositional model.
Tasks	Knowledge Base Completion
Published	2016-04-20
URL	http://arxiv.org/abs/1604.05878v1
PDF	http://arxiv.org/pdf/1604.05878v1.pdf
PWC	https://paperswithcode.com/paper/a-factorization-machine-framework-for-testing
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