January 28, 2020

3366 words 16 mins read

Paper Group ANR 828

Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics. A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes. Building change detection based on multi-scale filtering and grid partition. Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss. …

Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics


Title	Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics
Authors	Gautier Cosne, Guillaume Maze, Pierre Tandeo
Abstract	Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horizontally sparse, knowledge of the ocean interior thermal structure. The objective of this paper is to develop a methodology to combine these complementary ocean observing systems measurements to obtain a three-dimensional time series of ocean temperatures with high horizontal and vertical resolution. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics in the North Atlantic region may be decomposed into a mixture of dynamical modes, characterized by different local regressions between Sea Surface Temperature (SST), Sea Level Anomalies (SLA) and Vertical Temperature fields. Ultimately we propose a Latent-class regression method to improve prediction of vertical ocean temperature.
Tasks	Time Series
Published	2019-10-18
URL	https://arxiv.org/abs/1910.08573v1
PDF	https://arxiv.org/pdf/1910.08573v1.pdf
PWC	https://paperswithcode.com/paper/coupling-oceanic-observation-systems-to-study
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Framework

A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes


Title	A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes
Authors	Daniel Poh, Stephen Roberts, Martin Tegnér
Abstract	The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generation capabilities are utilised since electricity cannot be stored and this has the impact of driving prices up very quickly. Furthermore, the non-storability also complicates physical hedging. Owing to these, the problem of joint price-quantity risk in electricity markets is a commonly studied theme. We propose using Gaussian Processes (GPs) to tackle this problem since GPs provide a versatile and elegant non-parametric approach for regression and time-series modelling. However, GPs scale poorly with the amount of training data due to a cubic complexity. These considerations suggest that knowledge transfer between price and load is vital for effective hedging, and that a computationally efficient method is required. To this end, we use the coregionalized (or multi-task) sparse GPs which addresses the aforementioned issues. To gauge the performance of our model, we use an average-load strategy as comparator. The latter is a robust approach commonly used by industry. If the spot and load are uncorrelated and Gaussian, then hedging with the expected load will result in the minimum variance position. Our main contributions are twofold. Firstly, in developing a coregionalized sparse GP-based approach for hedging. Secondly, in demonstrating that our model-based strategy outperforms the comparator, and can thus be employed for effective hedging in electricity markets.
Tasks	Gaussian Processes, Time Series, Transfer Learning
Published	2019-03-22
URL	http://arxiv.org/abs/1903.09536v2
PDF	http://arxiv.org/pdf/1903.09536v2.pdf
PWC	https://paperswithcode.com/paper/a-machine-learning-approach-to-risk
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Building change detection based on multi-scale filtering and grid partition


Title	Building change detection based on multi-scale filtering and grid partition
Authors	Qi Bi, Kun Qin, Han Zhang, Wenjun Han, Zhili Li, Kai Xu
Abstract	Building change detection is of great significance in high resolution remote sensing applications. Multi-index learning, one of the state-of-the-art building change detection methods, still has drawbacks like incapability to find change types directly and heavy computation consumption of MBI. In this paper, a two-stage building change detection method is proposed to address these problems. In the first stage, a multi-scale filtering building index (MFBI) is calculated to detect building areas in each temporal with fast speed and moderate accuracy. In the second stage, images and the corresponding building maps are partitioned into grids. In each grid, the ratio of building areas in time T2 and time T1 is calculated. Each grid is classified into one of the three change patterns, i.e., significantly increase, significantly decrease and approximately unchanged. Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
Tasks
Published	2019-08-22
URL	https://arxiv.org/abs/1908.08164v1
PDF	https://arxiv.org/pdf/1908.08164v1.pdf
PWC	https://paperswithcode.com/paper/building-change-detection-based-on-multi
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Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss


Title	Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss
Authors	Roi Ceren, Shannon Quinn, Glen Raines
Abstract	We propose a generalized decision-theoretic system for a heterogeneous team of autonomous agents who are tasked with online identification of phenotypically expressed stress in crop fields.. This system employs four distinct types of agents, specific to four available sensor modalities: satellites (Layer 3), uninhabited aerial vehicles (L2), uninhabited ground vehicles (L1), and static ground-level sensors (L0). Layers 3, 2, and 1 are tasked with performing image processing at the available resolution of the sensor modality and, along with data generated by layer 0 sensors, identify erroneous differences that arise over time. Our goal is to limit the use of the more computationally and temporally expensive subsequent layers. Therefore, from layer 3 to 1, each layer only investigates areas that previous layers have identified as potentially afflicted by stress. We introduce a reinforcement learning technique based on Perkins’ Monte Carlo Exploring Starts for a generalized Markovian model for each layer’s decision problem, and label the system the Agricultural Distributed Decision Framework (ADDF). As our domain is real-world and online, we illustrate implementations of the two major components of our system: a clustering-based image processing methodology and a two-layer POMDP implementation.
Tasks
Published	2019-01-07
URL	http://arxiv.org/abs/1901.02035v1
PDF	http://arxiv.org/pdf/1901.02035v1.pdf
PWC	https://paperswithcode.com/paper/towards-a-decentralized-autonomous-multiagent
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Exact Gaussian Processes on a Million Data Points


Title	Exact Gaussian Processes on a Million Data Points
Authors	Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Abstract	Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. In this paper, we develop a scalable approach for exact GPs that leverages multi-GPU parallelization and methods like linear conjugate gradients, accessing the kernel matrix only through matrix multiplication. By partitioning and distributing kernel matrix multiplies, we demonstrate that an exact GP can be trained on over a million points, a task previously thought to be impossible with current computing hardware, in less than 2 hours. Moreover, our approach is generally applicable, without constraints to grid data or specific kernel classes. Enabled by this scalability, we perform the first-ever comparison of exact GPs against scalable GP approximations on datasets with $10^4 !-! 10^6$ data points, showing dramatic performance improvements.
Tasks	Gaussian Processes
Published	2019-03-19
URL	https://arxiv.org/abs/1903.08114v2
PDF	https://arxiv.org/pdf/1903.08114v2.pdf
PWC	https://paperswithcode.com/paper/exact-gaussian-processes-on-a-million-data
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Local Features and Visual Words Emerge in Activations


Title	Local Features and Visual Words Emerge in Activations
Authors	Oriane Siméoni, Yannis Avrithis, Ondrej Chum
Abstract	We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process. We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images.
Tasks	Image Retrieval
Published	2019-05-15
URL	https://arxiv.org/abs/1905.06358v1
PDF	https://arxiv.org/pdf/1905.06358v1.pdf
PWC	https://paperswithcode.com/paper/local-features-and-visual-words-emerge-in
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Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy


Title	Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Authors	Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti, Ivana Išgum, Marius Staring
Abstract	Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversarial learning. We consider the case in which fixed and moving images as well as their segmentations are available for training, while segmentations are not available during testing; a common scenario in radiotherapy. The proposed framework consists of a 3D end-to-end generator network that estimates the deformation vector field (DVF) between fixed and moving images in an unsupervised fashion and applies this DVF to the moving image and its segmentation. A discriminator network is trained to evaluate how well the moving image and segmentation align with the fixed image and segmentation. The proposed network was trained and evaluated on follow-up prostate CT scans for image-guided radiotherapy, where the planning CT contours are propagated to the daily CT images using the estimated DVF. A quantitative comparison with conventional registration using \texttt{elastix} showed that the proposed method improved performance and substantially reduced computation time, thus enabling real-time contour propagation necessary for online-adaptive radiotherapy.
Tasks	Image Registration
Published	2019-06-28
URL	https://arxiv.org/abs/1906.12223v1
PDF	https://arxiv.org/pdf/1906.12223v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-optimization-for-joint
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STA: Adversarial Attacks on Siamese Trackers


Title	STA: Adversarial Attacks on Siamese Trackers
Authors	Xugang Wu, Xiaoping Wang, Xu Zhou, Songlei Jian
Abstract	Recently, the majority of visual trackers adopt Convolutional Neural Network (CNN) as their backbone to achieve high tracking accuracy. However, less attention has been paid to the potential adversarial threats brought by CNN, including Siamese network. In this paper, we first analyze the existing vulnerabilities in Siamese trackers and propose the requirements for a successful adversarial attack. On this basis, we formulate the adversarial generation problem and propose an end-to-end pipeline to generate a perturbed texture map for the 3D object that causes the trackers to fail. Finally, we conduct thorough experiments to verify the effectiveness of our algorithm. Experiment results show that adversarial examples generated by our algorithm can successfully lower the tracking accuracy of victim trackers and even make them drift off. To the best of our knowledge, this is the first work to generate 3D adversarial examples on visual trackers.
Tasks	Adversarial Attack
Published	2019-09-08
URL	https://arxiv.org/abs/1909.03413v1
PDF	https://arxiv.org/pdf/1909.03413v1.pdf
PWC	https://paperswithcode.com/paper/sta-adversarial-attacks-on-siamese-trackers
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Deep neural network solution of the electronic Schrödinger equation


Title	Deep neural network solution of the electronic Schrödinger equation
Authors	Jan Hermann, Zeno Schätzle, Frank Noé
Abstract	The electronic Schr"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo is a possible way out: it scales well to large molecules, can be parallelized, and its accuracy has, as yet, only been limited by the flexibility of the used wave function ansatz. Here we propose PauliNet, a deep-learning wave function ansatz that achieves nearly exact solutions of the electronic Schr"odinger equation. PauliNet has a multireference Hartree-Fock solution built in as a baseline, incorporates the physics of valid wave functions, and is trained using variational quantum Monte Carlo (VMC). PauliNet outperforms comparable state-of-the-art VMC ansatzes for atoms, diatomic molecules and a strongly-correlated hydrogen chain by a margin and is yet computationally efficient. We anticipate that thanks to the favourable scaling with system size, this method may become a new leading method for highly accurate electronic-strucutre calculations on medium-sized molecular systems.
Tasks
Published	2019-09-16
URL	https://arxiv.org/abs/1909.08423v2
PDF	https://arxiv.org/pdf/1909.08423v2.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-solution-of-the
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Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles


Title	Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Authors	Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun
Abstract	The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior planner, which handles high-level decisions and produces a coarse trajectory, and trajectory planner that generates a smooth, feasible trajectory for the duration of the planning horizon. These planners, however, are typically developed separately, and changes in the behavior planner might affect the trajectory planner in unexpected ways. Furthermore, the final trajectory outputted by the trajectory planner might differ significantly from the one generated by the behavior planner, as they do not share the same objective. In this paper, we propose a jointly learnable behavior and trajectory planner. Unlike most existing learnable motion planners that address either only behavior planning, or use an uninterpretable neural network to represent the entire logic from sensors to driving commands, our approach features an interpretable cost function on top of perception, prediction and vehicle dynamics, and a joint learning algorithm that learns a shared cost function employed by our behavior and trajectory components. Experiments on real-world self-driving data demonstrate that jointly learned planner performs significantly better in terms of both similarity to human driving and other safety metrics, compared to baselines that do not adopt joint behavior and trajectory learning.
Tasks
Published	2019-10-10
URL	https://arxiv.org/abs/1910.04586v1
PDF	https://arxiv.org/pdf/1910.04586v1.pdf
PWC	https://paperswithcode.com/paper/jointly-learnable-behavior-and-trajectory
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Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data


Title	Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data
Authors	Gábor Horváth, Edith Kovács, Roland Molontay, Szabolcs Nováczki
Abstract	The anomaly detection method presented by this paper has a special feature: it does not only indicate whether an observation is anomalous or not but also tells what exactly makes an anomalous observation unusual. Hence, it provides support to localize the reason of the anomaly. The proposed approach is model-based; it relies on the multivariate probability distribution associated with the observations. Since the rare events are present in the tails of the probability distributions, we use copula functions, that are able to model the fat-tailed distributions well. The presented procedure scales well; it can cope with a large number of high-dimensional samples. Furthermore, our procedure can cope with missing values, too, which occur frequently in high-dimensional data sets. In the second part of the paper, we demonstrate the usability of the method through a case study, where we analyze a large data set consisting of the performance counters of a real mobile telecommunication network. Since such networks are complex systems, the signs of sub-optimal operation can remain hidden for a potentially long time. With the proposed procedure, many such hidden issues can be isolated and indicated to the network operator.
Tasks	Anomaly Detection
Published	2019-12-04
URL	https://arxiv.org/abs/1912.02166v1
PDF	https://arxiv.org/pdf/1912.02166v1.pdf
PWC	https://paperswithcode.com/paper/copula-based-anomaly-scoring-and-localization
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Best Practices for Learning Domain-Specific Cross-Lingual Embeddings


Title	Best Practices for Learning Domain-Specific Cross-Lingual Embeddings
Authors	Lena Shakurova, Beata Nyari, Chao Li, Mihai Rotaru
Abstract	Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed dictionaries and pre-trained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks. In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain. We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence the performance. We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.
Tasks	Transfer Learning
Published	2019-07-06
URL	https://arxiv.org/abs/1907.03112v1
PDF	https://arxiv.org/pdf/1907.03112v1.pdf
PWC	https://paperswithcode.com/paper/best-practices-for-learning-domain-specific
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Framework

Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network


Title	Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Authors	Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu, Min Sun
Abstract	Tomography medical imaging is essential in the clinical workflow of modern cancer radiotherapy. Radiation oncologists identify cancerous tissues, applying delineation on treatment regions throughout all image slices. This kind of task is often formulated as a volumetric segmentation task by means of 3D convolutional networks with considerable computational cost. Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently. More specifically, we propose convolutional recurrent Gated Graph Propagator (GGP) to propagate high-level information through image slices, with learnable adjacency weighted matrix. Furthermore, as physicians often investigate a few specific slices to refine their decision, we model this slice-wise interaction procedure to further improve our segmentation result. This can be set by editing any slice effortlessly as updating predictions of other slices using GGP. To evaluate our method, we collect an Esophageal Cancer Radiotherapy Target Treatment Contouring dataset of 81 patients which includes tomography images with radiotherapy target. On this dataset, our convolutional graph network produces state-of-the-art results and outperforms the baselines. With the addition of interactive setting, performance is improved even further. Our method has the potential to be easily applied to diverse kinds of medical tasks with volumetric images. Incorporating both the ability to make a feasible prediction and to consider the human interactive input, the proposed method is suitable for clinical scenarios.
Tasks
Published	2019-04-05
URL	http://arxiv.org/abs/1904.03086v1
PDF	http://arxiv.org/pdf/1904.03086v1.pdf
PWC	https://paperswithcode.com/paper/radiotherapy-target-contouring-with
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Framework

High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence


Title	High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence
Authors	Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
Abstract	We consider the problem of estimating the parameters of a multivariate Bernoulli process with auto-regressive feedback in the high-dimensional setting where the number of samples available is much less than the number of parameters. This problem arises in learning interconnections of networks of dynamical systems with spiking or binary-valued data. We allow the process to depend on its past up to a lag $p$, for a general $p \ge 1$, allowing for more realistic modeling in many applications. We propose and analyze an $\ell_1$-regularized maximum likelihood estimator (MLE) under the assumption that the parameter tensor is approximately sparse. Rigorous analysis of such estimators is made challenging by the dependent and non-Gaussian nature of the process as well as the presence of the nonlinearities and multi-level feedback. We derive precise upper bounds on the mean-squared estimation error in terms of the number of samples, dimensions of the process, the lag $p$ and other key statistical properties of the model. The ideas presented can be used in the high-dimensional analysis of regularized $M$-estimators for other sparse nonlinear and non-Gaussian processes with long-range dependence.
Tasks	Gaussian Processes
Published	2019-03-19
URL	http://arxiv.org/abs/1903.09631v1
PDF	http://arxiv.org/pdf/1903.09631v1.pdf
PWC	https://paperswithcode.com/paper/high-dimensional-bernoulli-autoregressive
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Framework

Deep Gaussian Processes for Multi-fidelity Modeling


Title	Deep Gaussian Processes for Multi-fidelity Modeling
Authors	Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González
Abstract	Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models. This arises in both fundamental machine learning procedures such as Bayesian optimization, as well as more practical science and engineering applications. In this paper we develop a novel multi-fidelity model which treats layers of a deep Gaussian process as fidelity levels, and uses a variational inference scheme to propagate uncertainty across them. This allows for capturing nonlinear correlations between fidelities with lower risk of overfitting than existing methods exploiting compositional structure, which are conversely burdened by structural assumptions and constraints. We show that the proposed approach makes substantial improvements in quantifying and propagating uncertainty in multi-fidelity set-ups, which in turn improves their effectiveness in decision making pipelines.
Tasks	Decision Making, Gaussian Processes
Published	2019-03-18
URL	http://arxiv.org/abs/1903.07320v1
PDF	http://arxiv.org/pdf/1903.07320v1.pdf
PWC	https://paperswithcode.com/paper/deep-gaussian-processes-for-multi-fidelity
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