pwc-archive/papers-with-abstracts huggingface dataset:
Graphs with Multiple Sources per Vertex
Authors: Martin van Harmelen, Jonas Groschwitz
Date: 2020-06-19 00:00:00

Several attempts have been made at constructing Abstract Meaning Representations (AMRs) compositionally, and recently the idea of using s-graphs with the HR-algebra (Koller, 2015) has been simplified to reduce the number of options when parsing (Groschwitz et al., 2017). This apply-modify algebra (AM-algebra) is a linguistically plausible graph algebra with two classes of operations, both of rank two: the apply operation is used to combine a predicate with its argument; the modify operation is used to modify a predicate. While the AM-algebra correctly handles relative clauses and complex cases of coordination, it cannot parse reflexive sentences like: "The raven washes herself." To facilitate processing of such reflexive sentences, this paper proposes to change the definition of s-graphs underlying the AM-algebra to allow vertices with multiple sources, and additionally proposes an adaption to the type system of the algebra to correctly handle such vertices.
Common equivalence and size after forgetting
Authors: Paolo Liberatore
Date: 2020-06-19 00:00:00

Forgetting variables from a propositional formula may increase its size. Introducing new variables is a way to shorten it. Both operations can be expressed in terms of common equivalence, a weakened version of equivalence. In turn, common equivalence can be expressed in terms of forgetting. An algorithm for forgetting and checking common equivalence in polynomial space is given for the Horn case; it is polynomial-time for the subclass of single-head formulae. Minimizing after forgetting is polynomial-time if the formula is also acyclic and variables cannot be introduced, NP-hard when they can.
Compositional Learning of Image-Text Query for Image Retrieval
Authors: Muhammad Umer Anwaar, Egor Labintcev, Martin Kleinsteuber
Date: 2020-06-19 00:00:00
Tasks: Image Retrieval, Image Retrieval with Multi-Modal Query, Metric Learning, Retrieval

In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications. For instance, a user of an E-Commerce platform is interested in buying a dress, which should look similar to her friend's dress, but the dress should be of white color with a ribbon sash. In this case, we would like the algorithm to retrieve some dresses with desired modifications in the query dress. We propose an autoencoder based model, ComposeAE, to learn the composition of image and text query for retrieving images. We adopt a deep metric learning approach and learn a metric that pushes composition of source image and text query closer to the target images. We also propose a rotational symmetry constraint on the optimization problem. Our approach is able to outperform the state-of-the-art method TIRG \cite{TIRG} on three benchmark datasets, namely: MIT-States, Fashion200k and Fashion IQ. In order to ensure fair comparison, we introduce strong baselines by enhancing TIRG method. To ensure reproducibility of the results, we publish our code here: \url{https://github.com/ecom-research/ComposeAE}.
Pupil Center Detection Approaches: A comparative analysis
Authors: Talía Vázquez Romaguera, Liset Vázquez Romaguera, David Castro Piñol, Carlos Román Vázquez Seisdedos
Date: 2020-06-19 00:00:00

In the last decade, the development of technologies and tools for eye tracking has been a constantly growing area. Detecting the center of the pupil, using image processing techniques, has been an essential step in this process. A large number of techniques have been proposed for pupil center detection using both traditional image processing and machine learning-based methods. Despite the large number of methods proposed, no comparative work on their performance was found, using the same images and performance metrics. In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost. These methods are based on the circular Hough transform, ellipse fitting, Daugman's integro-differential operator and radial symmetry transform. The comparative analysis was performed with 800 infrared images from the CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of disturbances. The best performance was obtained by the method based on the radial symmetry transform with an accuracy and average robustness higher than 94%. The shortest processing time, obtained with the ellipse fitting method, was 0.06 s.
Latent variable modeling with random features
Authors: Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt
Date: 2020-06-19 00:00:00
Tasks: Dimensionality Reduction

Gaussian process-based latent variable models are flexible and theoretically grounded tools for nonlinear dimension reduction, but generalizing to non-Gaussian data likelihoods within this nonlinear framework is statistically challenging. Here, we use random features to develop a family of nonlinear dimension reduction models that are easily extensible to non-Gaussian data likelihoods; we call these random feature latent variable models (RFLVMs). By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable. This allows the RFLVM framework to support computationally tractable nonlinear latent variable models for a variety of data likelihoods in the exponential family without specialized derivations. Our generalized RFLVMs produce results comparable with other state-of-the-art dimension reduction methods on diverse types of data, including neural spike train recordings, images, and text data.
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Authors: Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
Date: 2020-06-19 00:00:00
Proceeding: NeurIPS 2020 12

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the sequence of iterates generated by SGD remains bounded and converges with probability $1$ under a very broad range of step-size schedules. Subsequently, going beyond existing positive probability guarantees, we show that SGD avoids strict saddle points/manifolds with probability $1$ for the entire spectrum of step-size policies considered. Finally, we prove that the algorithm's rate of convergence to Hurwicz minimizers is $\mathcal{O}(1/n^{p})$ if the method is employed with a $\Theta(1/n^p)$ step-size schedule. This provides an important guideline for tuning the algorithm's step-size as it suggests that a cool-down phase with a vanishing step-size could lead to faster convergence; we demonstrate this heuristic using ResNet architectures on CIFAR.
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles
Authors: Kiet Van Nguyen, Tin Van Huynh, Duc-Vu Nguyen, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen
Date: 2020-06-19 00:00:00
Tasks: Articles, Machine Reading Comprehension, Reading Comprehension, Sentence, Transfer Learning, Vietnamese Machine Reading Comprehension

Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models on a low-resource language like Vietnamese. Besides, machine reading comprehension (MRC) for the health domain offers great potential for practical applications; however, there is still very little MRC research in this domain. This paper presents ViNewsQA as a new corpus for the Vietnamese language to evaluate healthcare reading comprehension models. The corpus comprises 22,057 human-generated question-answer pairs. Crowd-workers create the questions and their answers based on a collection of over 4,416 online Vietnamese healthcare news articles, where the answers comprise spans extracted from the corresponding articles. In particular, we develop a process of creating a corpus for the Vietnamese machine reading comprehension. Comprehensive evaluations demonstrate that our corpus requires abilities beyond simple reasoning, such as word matching and demanding difficult reasoning based on single-or-multiple-sentence information. We conduct experiments using different types of machine reading comprehension methods to achieve the first baseline performances, compared with further models' performances. We also measure human performance on the corpus and compared it with several powerful neural network-based and transfer learning-based models. Our experiments show that the best machine model is ALBERT, which achieves an exact match score of 65.26% and an F1-score of 84.89% on our corpus. The significant differences between humans and the best-performance model (14.53% of EM and 10.90% of F1-score) on the test set of our corpus indicate that improvements in ViNewsQA could be explored in the future study. Our corpus is publicly available on our website for the research purpose to encourage the research community to make these improvements.
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Authors: Quentin Renau, Carola Doerr, Johann Dreo, Benjamin Doerr
Date: 2020-06-19 00:00:00
Tasks: General Classification

Exploratory landscape analysis (ELA) supports supervised learning approaches for automated algorithm selection and configuration by providing sets of features that quantify the most relevant characteristics of the optimization problem at hand. In black-box optimization, where an explicit problem representation is not available, the feature values need to be approximated from a small number of sample points. In practice, uniformly sampled random point sets and Latin hypercube constructions are commonly used sampling strategies. In this work, we analyze how the sampling method and the sample size influence the quality of the feature value approximations and how this quality impacts the accuracy of a standard classification task. While, not unexpectedly, increasing the number of sample points gives more robust estimates for the feature values, to our surprise we find that the feature value approximations for different sampling strategies do not converge to the same value. This implies that approximated feature values cannot be interpreted independently of the underlying sampling strategy. As our classification experiments show, this also implies that the feature approximations used for training a classifier must stem from the same sampling strategy as those used for the actual classification tasks. As a side result we show that classifiers trained with feature values approximated by Sobol' sequences achieve higher accuracy than any of the standard sampling techniques. This may indicate improvement potential for ELA-trained machine learning models.
Deep Transformation-Invariant Clustering
Authors: Tom Monnier, Thibault Groueix, Mathieu Aubry
Date: 2020-06-19 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Clustering, Image Clustering, Unsupervised Image Classification

Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space. This learning process naturally fits in the gradient-based training of K-means and Gaussian mixture model, without requiring any additional loss or hyper-parameters. It leads us to two new deep transformation-invariant clustering frameworks, which jointly learn prototypes and transformations. More specifically, we use deep learning modules that enable us to resolve invariance to spatial, color and morphological transformations. Our approach is conceptually simple and comes with several advantages, including the possibility to easily adapt the desired invariance to the task and a strong interpretability of both cluster centers and assignments to clusters. We demonstrate that our novel approach yields competitive and highly promising results on standard image clustering benchmarks. Finally, we showcase its robustness and the advantages of its improved interpretability by visualizing clustering results over real photograph collections.
Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
Authors: Josh Kalin, David Noever, Gerry Dozier
Date: 2020-06-19 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Sentiment Analysis

This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment. This method for securing each model post deployment is called the BAD (Build, Attack, and Defend) Architecture. Two implementations of the BAD architecture are evaluated to quantify the adversarial life cycle for a black box Sentiment Analysis system. As a challenging diagnostic, the Jigsaw Toxic Bias dataset is selected as the baseline in our performance tool. Each implementation of the architecture will build a baseline performance report, attack a common weakness, and defend the incoming attack. As an important note: each attack surface demonstrated in this work is detectable and preventable. The goal is to demonstrate a viable methodology for securing a machine learning model in a production setting.
A general framework for defining and optimizing robustness
Authors: Alessandro Tibo, Manfred Jaeger, Kim G. Larsen
Date: 2020-06-19 00:00:00
Tasks: Data Augmentation

Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. Therefore, in this paper, we propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our robustness concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy, and that it should be defined in purely mathematical terms without reliance on algorithmic procedures for its measurement. We develop a very general robustness framework that is applicable to any type of classification model, and that encompasses relevant robustness concepts for investigations ranging from safety against adversarial attacks to transferability of models to new domains. For two prototypical, distinct robustness objectives we then propose new learning approaches based on neural network co-training strategies for obtaining image classifiers optimized for these respective objectives.
From Discrete to Continuous Convolution Layers
Authors: Assaf Shocher, Ben Feinstein, Niv Haim, Michal Irani
Date: 2020-06-19 00:00:00

A basic operation in Convolutional Neural Networks (CNNs) is spatial resizing of feature maps. This is done either by strided convolution (donwscaling) or transposed convolution (upscaling). Such operations are limited to a fixed filter moving at predetermined integer steps (strides). Spatial sizes of consecutive layers are related by integer scale factors, predetermined at architectural design, and remain fixed throughout training and inference time. We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer. CC Layers naturally extend Conv-layers by representing the filter as a learned continuous function over sub-pixel coordinates. This allows learnable and principled resizing of feature maps, to any size, dynamically and consistently across scales. Once trained, the CC layer can be used to output any scale/size chosen at inference time. The scale can be non-integer and differ between the axes. CC gives rise to new freedoms for architectural design, such as dynamic layer shapes at inference time, or gradual architectures where the size changes by a small factor at each layer. This gives rise to many desired CNN properties, new architectural design capabilities, and useful applications. We further show that current Conv-layers suffer from inherent misalignments, which are ameliorated by CC layers.
Graph Pooling with Node Proximity for Hierarchical Representation Learning
Authors: Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard
Date: 2020-06-19 00:00:00
Tasks: Graph Classification, Representation Learning

Graph neural networks have attracted wide attentions to enable representation learning of graph data in recent works. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of graph data. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Node proximity is obtained by harmonizing the kernel representation of topology information and node features. Implicit structure-aware kernel representation of topology information allows efficient graph pooling without explicit eigendecomposition of the graph Laplacian. Similarities of node signals are adaptively evaluated with the combination of the affine transformation and kernel trick using the Gaussian RBF function. Experimental results demonstrate that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Authors: Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Shadab Khan, Simon K. Warfield, Ali Gholipour
Date: 2020-06-19 00:00:00

Multi-compartment modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several existing methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with standard methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
How Does Momentum Help Frank Wolfe?
Authors: Bingcong Li, Mario Coutino, Georgios B. Giannakis, Geert Leus
Date: 2020-06-19 00:00:00

We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM). On the negative side, these connections illustrate why momentum is unlikely to be effective for FW type algorithms. The encouraging message behind this link, on the other hand, is that momentum is useful for FW on a class of problems. In particular, we prove that a momentum variant of FW, that we term accelerated Frank Wolfe (AFW), converges with a faster rate $\tilde{\cal O}(\frac{1}{k^2})$ on certain constraint sets despite the same ${\cal O}(\frac{1}{k})$ rate as FW on general cases. Given the possible acceleration of AFW at almost no extra cost, it is thus a competitive alternative to FW. Numerical experiments on benchmarked machine learning tasks further validate our theoretical findings.
A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines
Authors: Günther Waxenegger-Wilfing, Kai Dresia, Jan Christian Deeken, Michael Oschwald
Date: 2020-06-19 00:00:00
Tasks: Deep Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. A quantitative comparison with carefully tuned open-loop sequences and PID controllers is included. The deep reinforcement learning controller achieves the highest performance and requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control. control.
Classifier uncertainty: evidence, potential impact, and probabilistic treatment
Authors: Niklas Tötsch, Daniel Hoffmann
Date: 2020-06-19 00:00:00
Tasks: General Classification

Classifiers are often tested on relatively small data sets, which should lead to uncertain performance metrics. Nevertheless, these metrics are usually taken at face value. We present an approach to quantify the uncertainty of classification performance metrics, based on a probability model of the confusion matrix. Application of our approach to classifiers from the scientific literature and a classification competition shows that uncertainties can be surprisingly large and limit performance evaluation. In fact, some published classifiers are likely to be misleading. The application of our approach is simple and requires only the confusion matrix. It is agnostic of the underlying classifier. Our method can also be used for the estimation of sample sizes that achieve a desired precision of a performance metric.
Analyzing the Real-World Applicability of DGA Classifiers
Authors: Arthur Drichel, Ulrike Meyer, Samuel Schüppen, Dominik Teubert
Date: 2020-06-19 00:00:00

Separating benign domains from domains generated by DGAs with the help of a binary classifier is a well-studied problem for which promising performance results have been published. The corresponding multiclass task of determining the exact DGA that generated a domain enabling targeted remediation measures is less well studied. Selecting the most promising classifier for these tasks in practice raises a number of questions that have not been addressed in prior work so far. These include the questions on which traffic to train in which network and when, just as well as how to assess robustness against adversarial attacks. Moreover, it is unclear which features lead a classifier to a decision and whether the classifiers are real-time capable. In this paper, we address these issues and thus contribute to bringing DGA detection classifiers closer to practical use. In this context, we propose one novel classifier based on residual neural networks for each of the two tasks and extensively evaluate them as well as previously proposed classifiers in a unified setting. We not only evaluate their classification performance but also compare them with respect to explainability, robustness, and training and classification speed. Finally, we show that our newly proposed binary classifier generalizes well to other networks, is time-robust, and able to identify previously unknown DGAs.