Paper Group ANR 866
To Cluster, or Not to Cluster: An Analysis of Clusterability Methods. Entropy production rate as a criterion for inconsistency in decision theory. SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction. Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images. Th …
To Cluster, or Not to Cluster: An Analysis of Clusterability Methods
| Title | To Cluster, or Not to Cluster: An Analysis of Clusterability Methods |
| Authors | A. Adolfsson, M. Ackerman, N. C. Brownstein |
| Abstract | Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. However, methods for evaluating clusterability vary radically, making it challenging to select a suitable measure. In this paper, we perform an extensive comparison of measures of clusterability and provide guidelines that clustering users can reference to select suitable measures for their applications. |
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| Published | 2018-08-24 |
| URL | http://arxiv.org/abs/1808.08317v1 |
| http://arxiv.org/pdf/1808.08317v1.pdf | |
| PWC | https://paperswithcode.com/paper/to-cluster-or-not-to-cluster-an-analysis-of |
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Entropy production rate as a criterion for inconsistency in decision theory
| Title | Entropy production rate as a criterion for inconsistency in decision theory |
| Authors | Purushottam D. Dixit |
| Abstract | Individual and group decisions are complex, often involving choosing an apt alternative from a multitude of options. Evaluating pairwise comparisons breaks down such complex decision problems into tractable ones. Pairwise comparison matrices (PCMs) are regularly used to solve multiple-criteria decision-making (MCDM) problems, for example, using Saaty’s analytic hierarchy process (AHP) framework. However, there are two significant drawbacks of using PCMs. First, humans evaluate PCMs in an inconsistent manner. Second, not all entries of a large PCM can be reliably filled by human decision makers. We address these two issues by first establishing a novel connection between PCMs and time-irreversible Markov processes. Specifically, we show that every PCM induces a family of dissipative maximum path entropy random walks (MERW) over the set of alternatives. We show that only `consistent’ PCMs correspond to detailed balanced MERWs. We identify the non-equilibrium entropy production in the induced MERWs as a metric of inconsistency of the underlying PCMs. Notably, the entropy production satisfies all of the recently laid out criteria for reasonable consistency indices. We also propose an approach to use incompletely filled PCMs in AHP. Potential future avenues are discussed as well. keywords: analytic hierarchy process, markov chains, maximum entropy | |
| Tasks | Decision Making |
| Published | 2018-01-05 |
| URL | http://arxiv.org/abs/1801.01733v2 |
| http://arxiv.org/pdf/1801.01733v2.pdf | |
| PWC | https://paperswithcode.com/paper/entropy-production-rate-as-a-criterion-for |
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SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction
| Title | SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction |
| Authors | Arief Koesdwiady, Fakhri Karray |
| Abstract | This paper presents a practical approach for detecting non-stationarity in time series prediction. This method is called SAFE and works by monitoring the evolution of the spectral contents of time series through a distance function. This method is designed to work in combination with state-of-the-art machine learning methods in real time by informing the online predictors to perform necessary adaptation when a non-stationarity presents. We also propose an algorithm to proportionally include some past data in the adaption process to overcome the Catastrophic Forgetting problem. To validate our hypothesis and test the effectiveness of our approach, we present comprehensive experiments in different elements of the approach involving artificial and real-world datasets. The experiments show that the proposed method is able to significantly save computational resources in term of processor or GPU cycles while maintaining high prediction performances. |
| Tasks | Time Series, Time Series Prediction |
| Published | 2018-03-04 |
| URL | http://arxiv.org/abs/1803.01364v2 |
| http://arxiv.org/pdf/1803.01364v2.pdf | |
| PWC | https://paperswithcode.com/paper/safe-spectral-evolution-analysis-feature |
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Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
| Title | Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images |
| Authors | Ruqayya Awan, Navid Alemi Koohbanani, Muhammad Shaban, Anna Lisowska, Nasir Rajpoot |
| Abstract | Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for training a CNN which limits its use by the computational pathology community. In previous studies, CNNs have demonstrated their potential in terms of feature generalizability and transferability accompanied with better performance. Considering these traits of CNN, we propose a simple yet effective method which leverages the strengths of CNN combined with the advantages of including contextual information, particularly designed for a small dataset. Our method consists of two main steps: first it uses the activation features of CNN trained for a patch-based classification and then it trains a separate classifier using features of overlapping patches to perform image-based classification using the contextual information. The proposed framework outperformed the state-of-the-art method for breast cancer classification. |
| Tasks | Classification Of Breast Cancer Histology Images |
| Published | 2018-02-12 |
| URL | http://arxiv.org/abs/1803.00386v2 |
| http://arxiv.org/pdf/1803.00386v2.pdf | |
| PWC | https://paperswithcode.com/paper/context-aware-learning-using-transferable |
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The FLUXCOM ensemble of global land-atmosphere energy fluxes
| Title | The FLUXCOM ensemble of global land-atmosphere energy fluxes |
| Authors | Martin Jung, Sujan Koirala, Ulrich Weber, Kazuhito Ichii, Fabian Gans, Gustau-Camps-Valls, Dario Papale, Christopher Schwalm, Gianluca Tramontana, Markus Reichstein |
| Abstract | Although a key driver of Earth’s climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 global gridded products in two setups: (1) 0.0833${\deg}$ resolution using MODIS remote sensing data (RS) and (2) 0.5${\deg}$ resolution using remote sensing and meteorological data (RS+METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS+METEO setups respectively, we estimate 2001-2013 global (${\pm}$ 1 standard deviation) net radiation as 75.8${\pm}$1.4 ${W\ m^{-2}}$ and 77.6${\pm}$2 ${W\ m^{-2}}$, sensible heat as 33${\pm}$4 ${W\ m^{-2}}$ and 36${\pm}$5 ${W\ m^{-2}}$, and evapotranspiration as 75.6${\pm}$10 ${\times}$ 10$^3$ ${km^3\ yr^{-1}}$ and 76${\pm}$6 ${\times}$ 10$^3$ ${km^3\ yr^{-1}}$. FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations. |
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| Published | 2018-12-11 |
| URL | http://arxiv.org/abs/1812.04951v1 |
| http://arxiv.org/pdf/1812.04951v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-fluxcom-ensemble-of-global-land |
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AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
| Title | AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI |
| Authors | Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu |
| Abstract | AutoML has become a popular service that is provided by most leading cloud service providers today. In this paper, we focus on the AutoML problem from the \emph{service provider’s perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multiple devices} at the same time, how can we allocate these devices to users in an efficient way? We focus on GP-EI, one of the most popular algorithms for automatic model selection and hyperparameter tuning, used by systems such as Google Vizer. The technical contribution of this paper is the first multi-device, multi-tenant algorithm for GP-EI that is aware of \emph{multiple} computation devices and multiple users sharing the same set of computation devices. Theoretically, given $N$ users and $M$ devices, we obtain a regret bound of $O((\text{\bf {MIU}}(T,K) + M)\frac{N^2}{M})$, where $\text{\bf {MIU}}(T,K)$ refers to the maximal incremental uncertainty up to time $T$ for the covariance matrix $K$. Empirically, we evaluate our algorithm on two applications of automatic model selection, and show that our algorithm significantly outperforms the strategy of serving users independently. Moreover, when multiple computation devices are available, we achieve near-linear speedup when the number of users is much larger than the number of devices. |
| Tasks | AutoML, Model Selection |
| Published | 2018-03-17 |
| URL | http://arxiv.org/abs/1803.06561v3 |
| http://arxiv.org/pdf/1803.06561v3.pdf | |
| PWC | https://paperswithcode.com/paper/automl-from-service-providers-perspective |
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Artificial Intelligent Diagnosis and Monitoring in Manufacturing
| Title | Artificial Intelligent Diagnosis and Monitoring in Manufacturing |
| Authors | Ye Yuan, Guijun Ma, Cheng Cheng, Beitong Zhou, Huan Zhao, Hai-Tao Zhang, Han Ding |
| Abstract | The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the ‘Industry 4.0’, which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing. |
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| Published | 2018-12-17 |
| URL | http://arxiv.org/abs/1901.02057v1 |
| http://arxiv.org/pdf/1901.02057v1.pdf | |
| PWC | https://paperswithcode.com/paper/artificial-intelligent-diagnosis-and |
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Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
| Title | Primitive Fitting Using Deep Boundary Aware Geometric Segmentation |
| Authors | Duanshun Li, Chen Feng |
| Abstract | To identify and fit geometric primitives (e.g., planes, spheres, cylinders, cones) in a noisy point cloud is a challenging yet beneficial task for fields such as robotics and reverse engineering. As a multi-model multi-instance fitting problem, it has been tackled with different approaches including RANSAC, which however often fit inferior models in practice with noisy inputs of cluttered scenes. Inspired by the corresponding human recognition process, and benefiting from the recent advancements in image semantic segmentation using deep neural networks, we propose BAGSFit as a new framework addressing this problem. Firstly, through a fully convolutional neural network, the input point cloud is point-wisely segmented into multiple classes divided by jointly detected instance boundaries without any geometric fitting. Thus, segments can serve as primitive hypotheses with a probability estimation of associating primitive classes. Finally, all hypotheses are sent through a geometric verification to correct any misclassification by fitting primitives respectively. We performed training using simulated range images and tested it with both simulated and real-world point clouds. Quantitative and qualitative experiments demonstrated the superiority of BAGSFit. |
| Tasks | Semantic Segmentation |
| Published | 2018-10-03 |
| URL | http://arxiv.org/abs/1810.01604v1 |
| http://arxiv.org/pdf/1810.01604v1.pdf | |
| PWC | https://paperswithcode.com/paper/primitive-fitting-using-deep-boundary-aware |
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Reference-Conditioned Super-Resolution by Neural Texture Transfer
| Title | Reference-Conditioned Super-Resolution by Neural Texture Transfer |
| Authors | Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi |
| Abstract | With the recent advancement in deep learning, we have witnessed a great progress in single image super-resolution. However, due to the significant information loss of the image downscaling process, it has become extremely challenging to further advance the state-of-the-art, especially for large upscaling factors. This paper explores a new research direction in super resolution, called reference-conditioned super-resolution, in which a reference image containing desired high-resolution texture details is provided besides the low-resolution image. We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods. Inspired by recent work on image stylization, we address the problem via neural texture transfer. We design an end-to-end trainable deep model which generates detail enriched results by adaptively fusing the content from the low-resolution image with the texture patterns from the reference image. We create a benchmark dataset for the general research of reference-based super-resolution, which contains reference images paired with low-resolution inputs with varying degrees of similarity. Both objective and subjective evaluations demonstrate the great potential of using reference images as well as the superiority of our results over other state-of-the-art methods. |
| Tasks | Image Stylization, Image Super-Resolution, Super-Resolution |
| Published | 2018-04-10 |
| URL | http://arxiv.org/abs/1804.03360v1 |
| http://arxiv.org/pdf/1804.03360v1.pdf | |
| PWC | https://paperswithcode.com/paper/reference-conditioned-super-resolution-by |
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Predictive Uncertainty through Quantization
| Title | Predictive Uncertainty through Quantization |
| Authors | Bastiaan S. Veeling, Rianne van den Berg, Max Welling |
| Abstract | High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used for tractable inference, which err on the side of overconfidence. We propose Stochastic Quantized Activation Distributions (SQUAD), which imposes a flexible yet tractable distribution over discretized latent variables. The proposed method is scalable, self-normalizing and sample efficient. We demonstrate that the model fully utilizes the flexible distribution, learns interesting non-linearities, and provides predictive uncertainty of competitive quality. |
| Tasks | Latent Variable Models, Quantization |
| Published | 2018-10-12 |
| URL | http://arxiv.org/abs/1810.05500v1 |
| http://arxiv.org/pdf/1810.05500v1.pdf | |
| PWC | https://paperswithcode.com/paper/predictive-uncertainty-through-quantization |
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Effective Building Block Design for Deep Convolutional Neural Networks using Search
| Title | Effective Building Block Design for Deep Convolutional Neural Networks using Search |
| Authors | Jayanta K Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah |
| Abstract | Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex layer structures known as building blocks. Finding the best deep model requires a combination of finding both the right architecture and the correct set of parameters appropriate for that architecture. In addition, this complexity (in terms of layer types, number of neurons, and number of layers) also present problems with generalization since larger networks are easier to overfit to the data. In this paper, we propose a search framework for finding effective architectural building blocks for convolutional neural networks (CNN). Our approach is much faster at finding models that are close to state-of-the-art in performance. In addition, the models discovered by our approach are also smaller than models discovered by similar techniques. We achieve these twin advantages by designing our search space in such a way that it searches over a reduced set of state-of-the-art building blocks for CNNs including residual block, inception block, inception-residual block, ResNeXt block and many others. We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case). We also show that learned models are transferable between datasets. |
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| Published | 2018-01-25 |
| URL | http://arxiv.org/abs/1801.08577v1 |
| http://arxiv.org/pdf/1801.08577v1.pdf | |
| PWC | https://paperswithcode.com/paper/effective-building-block-design-for-deep |
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Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
| Title | Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation |
| Authors | Xing Niu, Weijia Xu, Marine Carpuat |
| Abstract | We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input language. We leverage differentiable sampling and bi-directional NMT to train models end-to-end, without introducing additional parameters. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states. |
| Tasks | Low-Resource Neural Machine Translation, Machine Translation |
| Published | 2018-11-02 |
| URL | http://arxiv.org/abs/1811.01116v2 |
| http://arxiv.org/pdf/1811.01116v2.pdf | |
| PWC | https://paperswithcode.com/paper/bi-directional-differentiable-input |
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End-to-End Content and Plan Selection for Data-to-Text Generation
| Title | End-to-End Content and Plan Selection for Data-to-Text Generation |
| Authors | Sebastian Gehrmann, Falcon Z. Dai, Henry Elder, Alexander M. Rush |
| Abstract | Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation. |
| Tasks | Data-to-Text Generation, Text Generation |
| Published | 2018-10-10 |
| URL | http://arxiv.org/abs/1810.04700v1 |
| http://arxiv.org/pdf/1810.04700v1.pdf | |
| PWC | https://paperswithcode.com/paper/end-to-end-content-and-plan-selection-for |
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Broad Learning for Healthcare
| Title | Broad Learning for Healthcare |
| Authors | Bokai Cao |
| Abstract | A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital footprints recorded on smart sensors). Capability for modeling information from these heterogeneous data sources is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Our works in this thesis attempt to facilitate healthcare applications in the setting of broad learning which focuses on fusing heterogeneous data sources for a variety of synergistic knowledge discovery and machine learning tasks. We are generally interested in computer-aided diagnosis, precision medicine, and mobile health by creating accurate user profiles which include important biomarkers, brain connectivity patterns, and latent representations. In particular, our works involve four different data mining problems with application to the healthcare domain: multi-view feature selection, subgraph pattern mining, brain network embedding, and multi-view sequence prediction. |
| Tasks | Feature Selection, Network Embedding |
| Published | 2018-03-23 |
| URL | http://arxiv.org/abs/1803.08978v1 |
| http://arxiv.org/pdf/1803.08978v1.pdf | |
| PWC | https://paperswithcode.com/paper/broad-learning-for-healthcare |
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Unpaired Image Captioning by Language Pivoting
| Title | Unpaired Image Captioning by Language Pivoting |
| Authors | Jiuxiang Gu, Shafiq Joty, Jianfei Cai, Gang Wang |
| Abstract | Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description. In general, the mapping function is learned from a training set of image-caption pairs. However, for some language, large scale image-caption paired corpus might not be available. We present an approach to this unpaired image captioning problem by language pivoting. Our method can effectively capture the characteristics of an image captioner from the pivot language (Chinese) and align it to the target language (English) using another pivot-target (Chinese-English) sentence parallel corpus. We evaluate our method on two image-to-English benchmark datasets: MSCOCO and Flickr30K. Quantitative comparisons against several baseline approaches demonstrate the effectiveness of our method. |
| Tasks | Image Captioning |
| Published | 2018-03-14 |
| URL | http://arxiv.org/abs/1803.05526v2 |
| http://arxiv.org/pdf/1803.05526v2.pdf | |
| PWC | https://paperswithcode.com/paper/unpaired-image-captioning-by-language |
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