pwc-archive/papers-with-abstracts huggingface dataset:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Authors: Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden, Anna Goldenberg
Date: 2019-05-13 00:00:00
Tasks: BIG-bench Machine Learning, Translation

Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outcomes and assist clinicians in rationalizing the model prediction, has been generally understood to be critical to establishing trust. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may catalyze building trust in ML models, we surveyed clinicians from two distinct acute care specialties (Intenstive Care Unit and Emergency Department). We use their feedback to characterize when explainability helps to improve clinicians' trust in ML models. We further identify the classes of explanations that clinicians identified as most relevant and crucial for effective translation to clinical practice. Finally, we discern concrete metrics for rigorous evaluation of clinical explainability methods. By integrating perceptions of explainability between clinicians and ML researchers we hope to facilitate the endorsement and broader adoption and sustained use of ML systems in healthcare.
Exact high-dimensional asymptotics for Support Vector Machine
Authors: Haoyang Liu
Date: 2019-05-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The Support Vector Machine (SVM) is one of the most widely used classification methods. In this paper, we consider the soft-margin SVM used on data points with independent features, where the sample size $n$ and the feature dimension $p$ grows to $\infty$ in a fixed ratio $p/n\rightarrow \delta$. We propose a set of equations that exactly characterizes the asymptotic behavior of support vector machine. In particular, we give exact formulas for (1) the variability of the optimal coefficients, (2) the proportion of data points lying on the margin boundary (i.e. number of support vectors), (3) the final objective function value, and (4) the expected misclassification error on new data points, which in particular implies the exact formula for the optimal tuning parameter given a data generating mechanism. We first establish these formulas in the case where the label $y\in\{+1,-1\}$ is independent of the feature $x$. Then the results are generalized to the case where the label $y\in\{+1,-1\}$ is allowed to have a general dependence on the feature $x$ through a linear combination $a_0^Tx$. These formulas for the non-smooth hinge loss are analogous to the recent results in \citep{sur2018modern} for smooth logistic loss. Our approach is based on heuristic leave-one-out calculations.
Block Coordinate Regularization by Denoising
Authors: Yu Sun, Jiaming Liu, Ulugbek S. Kamilov
Date: 2019-05-13 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Denoising

We consider the problem of estimating a vector from its noisy measurements using a prior specified only through a denoising function. Recent work on plug-and-play priors (PnP) and regularization-by-denoising (RED) has shown the state-of-the-art performance of estimators under such priors in a range of imaging tasks. In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables. We theoretically analyze the convergence of the algorithm and discuss its relationship to the traditional proximal optimization. Our analysis complements and extends recent theoretical results for RED-based estimation methods. We numerically validate our method using several denoiser priors, including those based on convolutional neural network (CNN) denoisers.
Do Kernel and Neural Embeddings Help in Training and Generalization?
Authors: Arman Rahbar, Emilio Jorge, Devdatt Dubhashi, Morteza Haghir Chehreghani
Date: 2019-05-13 00:00:00
Tasks: General Classification

Recent results on optimization and generalization properties of neural networks showed that in a simple two-layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines the convergence of the optimization during training. Such analyses also provide upper bounds on the generalization error. We experimentally investigate the implications of these results to deeper networks via embeddings. We regard the layers preceding the final hidden layer as producing different representations of the input data which are then fed to the two-layer model. We show that these representations improve both optimization and generalization. In particular, we investigate three kernel representations when fed to the final hidden layer: the Gaussian kernel and its approximation by random Fourier features, kernels designed to imitate representations produced by neural networks and finally an optimal kernel designed to align the data with target labels. The approximated representations induced by these kernels are fed to the neural network and the optimization and generalization properties of the final model are evaluated and compared.
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images
Authors: Thibaud Ehret, Axel Davy, Pablo Arias, Gabriele Facciolo
Date: 2019-05-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Demosaicking, Denoising

Demosaicking and denoising are the first steps of any camera image processing pipeline and are key for obtaining high quality RGB images. A promising current research trend aims at solving these two problems jointly using convolutional neural networks. Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images. Instead, they resort to simulated data. In this paper we present a method to learn demosaicking directly from mosaicked images, without requiring ground truth RGB data. We apply this to learn joint demosaicking and denoising only from RAW images, thus enabling the use of real data. In addition we show that for this application fine-tuning a network to a specific burst improves the quality of restoration for both demosaicking and denoising.
Weakly-supervised Caricature Face Parsing through Domain Adaptation
Authors: Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai, Deng Cai, Ming-Hsuan Yang
Date: 2019-05-13 00:00:00
Tasks: Attribute, Caricature, Domain Adaptation, Face Parsing, Style Transfer

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .
A novel statistical metric learning for hyperspectral image classification
Authors: Zhiqiang Gong, Ping Zhong, Weidong Hu, Zixuan Xiao, Xuping Yin
Date: 2019-05-13 00:00:00
Tasks: Classification, Diversity, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Metric Learning

In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image. First, the standard variance of the samples of each class in each batch is used to decrease the intra-class variance within each class. Then, the distances between the means of different classes are used to penalize the inter-class variance of the training samples. Finally, the standard variance between the means of different classes is added as an additional diversity term to repulse different classes from each other. Experiments have conducted over two real-world hyperspectral image datasets and the experimental results have shown the effectiveness of the proposed statistical metric learning.
Medical image super-resolution method based on dense blended attention network
Authors: Kewen Liu, Yuan Ma, Hongxia Xiong, Zejun Yan, Zhijun Zhou, Panpan Fang, Chaoyang Liu
Date: 2019-05-13 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruction, a novel medical image super-resolution method based on dense neural network and blended attention mechanism is proposed. The proposed method adds blended attention blocks to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details. Batch normalization layers are removed to avoid loss of high-frequency texture details. Final obtained high resolution medical image are obtained using deconvolutional layers at the very end of the network as up-sampling operators. Experimental results show that the proposed method has an improvement of 0.05db to 11.25dB and 0.6% to 14.04% on the peak signal-to-noise ratio(PSNR) metric and structural similarity index(SSIM) metric, respectively, compared with the mainstream image super-resolution methods. This work provides a new idea for theoretical studies of medical image super-resolution reconstruction.
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Authors: Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo
Date: 2019-05-13 00:00:00
Tasks: Multi-Label Learning

Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance. In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.
Object Detection in 20 Years: A Survey
Authors: Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, Jieping Ye
Date: 2019-05-13 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection, Pedestrian Detection, Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today's object detection technique as a revolution driven by deep learning, then back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This paper extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods.
Multi-View Multiple Clustering
Authors: Shixing Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
Date: 2019-05-13 00:00:00
Tasks: Clustering, Representation Learning

Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality and diverse clusterings. To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view self-representation learning to explore the individuality encoding matrices and the shared commonality matrix of multi-view data. It additionally reduces the redundancy (i.e., enhancing the individuality) among the matrices using the Hilbert-Schmidt Independence Criterion (HSIC), and collects shared information by forcing the shared matrix to be smooth across all views. It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality. We further extend multiple co-clustering on multi-view data and propose a solution called multi-view multiple co-clustering (MVMCC). Our empirical study shows that MVMC (MVMCC) can exploit multi-view data to generate multiple high-quality and diverse clusterings (co-clusterings), with superior performance to the state-of-the-art methods.
Scalable and Efficient Comparison-based Search without Features
Authors: Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
Date: 2019-05-13 00:00:00
Proceeding: ICML 2020 1
Tasks: Object, Triplet

We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair $(i,j)$ is more similar to $t$?"}. Objects live in a space of latent features, from which the oracle generates noisy answers. First, we consider the {\em non-blind} setting where these features are accessible. We propose a new Bayesian comparison-based search algorithm with noisy answers; it has low computational complexity yet is efficient in the number of queries. We provide theoretical guarantees, deriving the form of the optimal query and proving almost sure convergence to the target $t$. Second, we consider the \emph{blind} setting, where the object features are hidden from the search algorithm. In this setting, we combine our search method and a new distributional triplet embedding algorithm into one scalable learning framework called \textsc{Learn2Search}. We show that the query complexity of our approach on two real-world datasets is on par with the non-blind setting, which is not achievable using any of the current state-of-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting an experiment with users searching for movie actors.
A Review of Keyphrase Extraction
Authors: Eirini Papagiannopoulou, Grigorios Tsoumakas
Date: 2019-05-13 00:00:00
Tasks: Clustering, Keyphrase Extraction, Management

Keyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content. Keyphrases constitute a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering and classification. This article introduces keyphrase extraction, provides a well-structured review of the existing work, offers interesting insights on the different evaluation approaches, highlights open issues and presents a comparative experimental study of popular unsupervised techniques on five datasets.
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Authors: Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang
Date: 2019-05-13 00:00:00
Tasks: Image Classification

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels. In this paper, we find that the test accuracy can be quantitatively characterized in terms of the noise ratio in datasets. In particular, the test accuracy is a quadratic function of the noise ratio in the case of symmetric noise, which explains the experimental findings previously published. Based on our analysis, we apply cross-validation to randomly split noisy datasets, which identifies most samples that have correct labels. Then we adopt the Co-teaching strategy which takes full advantage of the identified samples to train DNNs robustly against noisy labels. Compared with extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs under both synthetic and real-world training noise.
Precipitation nowcasting using a stochastic variational frame predictor with learned prior distribution
Authors: Alexander Bihlo
Date: 2019-05-13 00:00:00

We propose the use of a stochastic variational frame prediction deep neural network with a learned prior distribution trained on two-dimensional rain radar reflectivity maps for precipitation nowcasting with lead times of up to 2 1/2 hours. We present a comparison to a standard convolutional LSTM network and assess the evolution of the structural similarity index for both methods. Case studies are presented that illustrate that the novel methodology can yield meaningful forecasts without excessive blur for the time horizons of interest.
Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
Authors: Weipeng Huang, Nishma Laitonjam, Guangyuan Piao, Neil Hurley
Date: 2019-05-13 00:00:00
Tasks: Clustering

This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the data items stored in the sub-tree beneath it, so that each level of the hierarchy corresponds to a partition of the dataset. We develop a novel Bayesian nonparametric method combining the nested Chinese Restaurant Process (nCRP) and the Hierarchical Dirichlet Process (HDP). Compared with other existing Bayesian approaches, our solution tackles data with complex latent mixture features which has not been previously explored in the literature. We discuss the details of the model and the inference procedure. Furthermore, experiments on three datasets show that our method achieves solid empirical results in comparison with existing algorithms.
Ludii -- The Ludemic General Game System
Authors: Éric Piette, Dennis J. N. J. Soemers, Matthew Stephenson, Chiara F. Sironi, Mark H. M. Winands, Cameron Browne
Date: 2019-05-13 00:00:00

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns
Authors: Oleksii Sidorov, Jon Yngve Hardeberg
Date: 2019-05-13 00:00:00
Tasks: Graph Neural Network

Cracks on a painting is not a defect but an inimitable signature of an artwork which can be used for origin examination, aging monitoring, damage identification, and even forgery detection. This work presents the development of a new methodology and corresponding toolbox for the extraction and characterization of information from an image of a craquelure pattern. The proposed approach processes craquelure network as a graph. The graph representation captures the network structure via mutual organization of junctions and fractures. Furthermore, it is invariant to any geometrical distortions. At the same time, our tool extracts the properties of each node and edge individually, which allows to characterize the pattern statistically. We illustrate benefits from the graph representation and statistical features individually using novel Graph Neural Network and hand-crafted descriptors correspondingly. However, we also show that the best performance is achieved when both techniques are merged into one framework. We perform experiments on the dataset for paintings' origin classification and demonstrate that our approach outperforms existing techniques by a large margin.
Towards a regularity theory for ReLU networks -- chain rule and global error estimates
Authors: Julius Berner, Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen
Date: 2019-05-13 00:00:00

Although for neural networks with locally Lipschitz continuous activation functions the classical derivative exists almost everywhere, the standard chain rule is in general not applicable. We will consider a way of introducing a derivative for neural networks that admits a chain rule, which is both rigorous and easy to work with. In addition we will present a method of converting approximation results on bounded domains to global (pointwise) estimates. This can be used to extend known neural network approximation theory to include the study of regularity properties. Of particular interest is the application to neural networks with ReLU activation function, where it contributes to the understanding of the success of deep learning methods for high-dimensional partial differential equations.
Learning Hierarchical Priors in VAEs
Authors: Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
Date: 2019-05-13 00:00:00
Proceeding: NeurIPS 2019 12

We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incentivise an informative latent representation of the data, we formulate the learning problem as a constrained optimisation problem by extending the Taming VAEs framework to two-level hierarchical models. We introduce a graph-based interpolation method, which shows that the topology of the learned latent representation corresponds to the topology of the data manifold---and present several examples, where desired properties of latent representation such as smoothness and simple explanatory factors are learned by the prior.