pwc-archive/papers-with-abstracts huggingface dataset:
An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations
Authors: Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki
Date: 2019-08-26 00:00:00
Tasks: Benchmarking, Clustering

K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages; it is only able to find local minima and the positions of the initial clustering centres (centroids) can greatly affect the clustering solution. Over the years many K-Means variations and initialisation techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations along with a range of deterministic and stochastic initialisation techniques. We show that, on average, more sophisticated initialisation techniques alleviate the need for complex clustering methods. Furthermore, deterministic methods perform better than stochastic methods. However, there is a trade-off: less sophisticated stochastic methods, executed multiple times, can result in better clustering. Factoring in execution time, deterministic methods can be competitive and result in a good clustering solution. These conclusions are obtained through extensive benchmarking using a range of synthetic model generators and real-world data sets.
PixelVAE++: Improved PixelVAE with Discrete Prior
Authors: Hossein Sadeghi, Evgeny Andriyash, Walter Vinci, Lorenzo Buffoni, Mohammad H. Amin
Date: 2019-08-26 00:00:00
Tasks: Decoder, Image Generation

Constructing powerful generative models for natural images is a challenging task. PixelCNN models capture details and local information in images very well but have limited receptive field. Variational autoencoders with a factorial decoder can capture global information easily, but they often fail to reconstruct details faithfully. PixelVAE combines the best features of the two models and constructs a generative model that is able to learn local and global structures. Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder. We introduce a novel architecture that reuses a part of the decoder as an encoder. We achieve the state of the art performance on binary data sets such as MNIST and Omniglot and achieve the state of the art performance on CIFAR-10 among latent variable models while keeping the latent variables informative.
Theory and Evaluation Metrics for Learning Disentangled Representations
Authors: Kien Do, Truyen Tran
Date: 2019-08-26 00:00:00
Proceeding: ICLR 2020 1
Tasks: Disentanglement, Informativeness

We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept "disentangled representations" used in supervised and unsupervised methods along three dimensions-informativeness, separability and interpretability - which can be expressed and quantified explicitly using information-theoretic constructs. This helps explain the behaviors of several well-known disentanglement learning models. We then propose robust metrics for measuring informativeness, separability and interpretability. Through a comprehensive suite of experiments, we show that our metrics correctly characterize the representations learned by different methods and are consistent with qualitative (visual) results. Thus, the metrics allow disentanglement learning methods to be compared on a fair ground. We also empirically uncovered new interesting properties of VAE-based methods and interpreted them with our formulation. These findings are promising and hopefully will encourage the design of more theoretically driven models for learning disentangled representations.
Private Stochastic Convex Optimization with Optimal Rates
Authors: Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta
Date: 2019-08-27 00:00:00
Proceeding: NeurIPS 2019 12

We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In this problem the goal is to approximately minimize the population loss given i.i.d. samples from a distribution over convex and Lipschitz loss functions. A long line of existing work on private convex optimization focuses on the empirical loss and derives asymptotically tight bounds on the excess empirical loss. However a significant gap exists in the known bounds for the population loss. We show that, up to logarithmic factors, the optimal excess population loss for DP algorithms is equal to the larger of the optimal non-private excess population loss, and the optimal excess empirical loss of DP algorithms. This implies that, contrary to intuition based on private ERM, private SCO has asymptotically the same rate of $1/\sqrt{n}$ as non-private SCO in the parameter regime most common in practice. The best previous result in this setting gives rate of $1/n^{1/4}$. Our approach builds on existing differentially private algorithms and relies on the analysis of algorithmic stability to ensure generalization.
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
Authors: Huanrui Yang, Wei Wen, Hai Li
Date: 2019-08-27 00:00:00
Proceeding: ICLR 2020 1
Tasks: compressed sensing, Efficient Neural Network

In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values, but it cannot provide useful gradients, and therefore requires complex optimization techniques. The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent. Yet it is not scale-invariant, causing the same shrinking rate to all parameters, which is inefficient in increasing sparsity. Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant. Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level. We also show that DeepHoyer can be applied to both element-wise and structural pruning.
Automated Fashion Size Normalization
Authors: Eddie S. J. Du, Chang Liu, David H. Wayne
Date: 2019-08-27 00:00:00

The ability to accurately predict the fit of fashion items and recommend the correct size is key to reducing merchandise returns in e-commerce. A critical prerequisite of fit prediction is size normalization, the mapping of product sizes across brands to a common space in which sizes can be compared. At present, size normalization is usually a time-consuming manual process. We propose a method to automate size normalization through the use of salesdata. The size mappings generated from our automated approaches are comparable to human-generated mappings.
Real-world Conversational AI for Hotel Bookings
Authors: Bai Li, Nanyi Jiang, Joey Sham, Henry Shi, Hussein Fazal
Date: 2019-08-27 00:00:00
Tasks: BIG-bench Machine Learning, Chatbot, Dialogue Management, General Classification, Information Retrieval, intent-classification, Intent Classification, Management, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval

In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry.
Finite size corrections for neural network Gaussian processes
Authors: Joseph M. Antognini
Date: 2019-08-27 00:00:00
Tasks: Gaussian Processes, valid

There has been a recent surge of interest in modeling neural networks (NNs) as Gaussian processes. In the limit of a NN of infinite width the NN becomes equivalent to a Gaussian process. Here we demonstrate that for an ensemble of large, finite, fully connected networks with a single hidden layer the distribution of outputs at initialization is well described by a Gaussian perturbed by the fourth Hermite polynomial for weights drawn from a symmetric distribution. We show that the scale of the perturbation is inversely proportional to the number of units in the NN and that higher order terms decay more rapidly, thereby recovering the Edgeworth expansion. We conclude by observing that understanding how this perturbation changes under training would reveal the regimes in which the Gaussian process framework is valid to model NN behavior.
Asymptotically Optimal One- and Two-Sample Testing with Kernels
Authors: Shengyu Zhu, Biao Chen, Zhitang Chen, Pengfei Yang
Date: 2019-08-27 00:00:00
Tasks: Change Detection, Two-sample testing, Vocal Bursts Valence Prediction

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test achieves the maximum rate subject to a constant level constraint on the type-I error probability. With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i.e., for any distribution defining the alternative hypothesis. We then show that two classes of Maximum Mean Discrepancy (MMD) based tests attain the optimal type-II error exponent on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve this optimality with an asymptotic level constraint. For general two-sample testing, however, Sanov's theorem is insufficient to obtain a similar sufficient condition. We proceed to establish an extended version of Sanov's theorem and derive an exact error exponent for the quadratic-time MMD based two-sample tests. The obtained error exponent is further shown to be optimal among all two-sample tests satisfying a given level constraint. Our work hence provides an achievability result for optimal nonparametric one- and two-sample testing in the universal setting. Application to off-line change detection and related issues are also discussed.
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style
Authors: M. Ricardo Carlos
Date: 2019-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Learning-To-Rank, Time Series Analysis

Road transportation is of critical importance for a nation, having profound effects in the economy, the health and life style of its people. With the growth of cities and populations come bigger demands for mobility and safety, creating new problems and magnifying those of the past. New tools are needed to face the challenge, to keep roads in good conditions, their users safe, and minimize the impact on the environment. This dissertation is concerned with road quality assessment and aggressive driving, two important problems in road transportation, approached in the context of Intelligent Transportation Systems by using Machine Learning techniques to analyze acceleration time series acquired with smartphone-based opportunistic sensing to automatically detect, classify, and characterize events of interest. Two aspects of road quality assessment are addressed: the detection and the characterization of road anomalies. For the first, the most widely cited works in the literature are compared and proposals capable of equal or better performance are presented, removing the reliance on threshold values and reducing the computational cost and dimensionality of previous proposals. For the second, new approaches for the estimation of pothole depth and the functional condition of speed reducers are showed. The new problem of pothole depth ranking is introduced, using a learning-to-rank approach to sort acceleration signals by the depth of the potholes that they reflect. The classification of aggressive driving maneuvers is done with automatic feature extraction, finding characteristically shaped subsequences in the signals as more effective discriminants than conventional descriptors calculated over time windows. Finally, all the previously mentioned tasks are combined to produce a robust road transport evaluation platform.
The many faces of deep learning
Authors: Raul Vicente
Date: 2019-08-25 00:00:00
Tasks: Deep Learning, Diversity

Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration that fuels novel developments in the theory and applications of machine learning. In this perspective, we collect and synthesize different intuitions scattered across several communities as for how deep learning works. In particular, we will briefly discuss the different perspectives that disciplines across mathematics, physics, computation, and neuroscience take on how deep learning does its tricks. Our discussion on each perspective is necessarily shallow due to the multiple views that had to be covered. The deepness in this case should come from putting all these faces of deep learning together in the reader's mind, so that one can look at the same problem from different angles.
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
Authors: Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Christopher E. Barbieri, Ulas Bagci, Sachin Jambawalikar
Date: 2019-08-26 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, SSIM, Transfer Learning

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Authors: Sameera Ramasinghe, Salman Khan, Nick Barnes, Stephen Gould
Date: 2019-08-24 00:00:00
Tasks: 3D Shape Recognition, Retrieval, Translation

Existing networks directly learn feature representations on 3D point clouds for shape analysis. We argue that 3D point clouds are highly redundant and hold irregular (permutation-invariant) structure, which makes it difficult to achieve inter-class discrimination efficiently. In this paper, we propose a two-faceted solution to this problem that is seamlessly integrated in a single `Blended Convolution and Synthesis' layer. This fully differentiable layer performs two critical tasks in succession. In the first step, it projects the input 3D point clouds into a latent 3D space to synthesize a highly compact and more inter-class discriminative point cloud representation. Since, 3D point clouds do not follow a Euclidean topology, standard 2/3D Convolutional Neural Networks offer limited representation capability. Therefore, in the second step, it uses a novel 3D convolution operator functioning inside the unit ball ($\mathbb{B}^3$) to extract useful volumetric features. We extensively derive formulae to achieve both translation and rotation of our novel convolution kernels. Finally, using the proposed techniques we present an extremely light-weight, end-to-end architecture that achieves compelling results on 3D shape recognition and retrieval.
Urban flows prediction from spatial-temporal data using machine learning: A survey
Authors: Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang
Date: 2019-08-26 00:00:00
Tasks: BIG-bench Machine Learning, Management, Prediction, Reinforcement Learning, Transfer Learning

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
Authors: Bo Li, Marius de Groot, Meike Vernooij, Arfan Ikram, Wiro Niessen, Esther Bron
Date: 2019-08-26 00:00:00
Tasks: Diffusion  MRI, Segmentation

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Authors: Bo Li, Wiro Niessen, Stefan Klein, Marius de Groot, Arfan Ikram, Meike Vernooij, Esther Bron
Date: 2019-08-26 00:00:00
Tasks: Segmentation

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.
End-to-End Conditional GAN-based Architectures for Image Colourisation
Authors: Marc Górriz, Marta Mrak, Alan F. Smeaton, Noel E. O'Connor
Date: 2019-08-26 00:00:00

In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image. Observing that existing colourisation methods sometimes exhibit a lack of colourfulness, this paper proposes a method to improve colourisation results. In particular, the method uses Generative Adversarial Neural Networks (GANs) and focuses on improvement of training stability to enable better generalisation in large multi-class image datasets. Additionally, the integration of instance and batch normalisation layers in both generator and discriminator is introduced to the popular U-Net architecture, boosting the network capabilities to generalise the style changes of the content. The method has been tested using the ILSVRC 2012 dataset, achieving improved automatic colourisation results compared to other methods based on GANs.
BULNER: BUg Localization with word embeddings and NEtwork Regularization
Authors: Jacson Rodrigues Barbosa, Ricardo Marcondes Marcacini, Ricardo Britto, Frederico Soares, Solange Rezende, Auri M. R. Vincenzi, Marcio E. Delamaro
Date: 2019-08-26 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Retrieval, Word Embeddings

Bug localization (BL) from the bug report is the strategic activity of the software maintaining process. Because BL is a costly and tedious activity, BL techniques information retrieval-based and machine learning-based could aid software engineers. We propose a method for BUg Localization with word embeddings and Network Regularization (BULNER). The preliminary results suggest that BULNER has better performance than two state-of-the-art methods.