pwc-archive/papers-with-abstracts huggingface dataset:
Fail-Safe Adversarial Generative Imitation Learning
Authors: Philipp Geiger, Christoph-Nikolas Straehle
Date: 2022-03-03 00:00:00
Tasks: Imitation Learning

For flexible yet safe imitation learning (IL), we propose theory and a modular method, with a safety layer that enables a closed-form probability density/gradient of the safe generative continuous policy, end-to-end generative adversarial training, and worst-case safety guarantees. The safety layer maps all actions into a set of safe actions, and uses the change-of-variables formula plus additivity of measures for the density. The set of safe actions is inferred by first checking safety of a finite sample of actions via adversarial reachability analysis of fallback maneuvers, and then concluding on the safety of these actions' neighborhoods using, e.g., Lipschitz continuity. We provide theoretical analysis showing the robustness advantage of using the safety layer already during training (imitation error linear in the horizon) compared to only using it at test time (up to quadratic error). In an experiment on real-world driver interaction data, we empirically demonstrate tractability, safety and imitation performance of our approach.
Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging
Authors: Samuel Pinilla, Kumar Vijay Mishra, Igor Shevkunov, Mojtaba Soltanalian, Vladimir Katkovnik, Karen Egiazarian
Date: 2022-03-03 00:00:00
Tasks: Retrieval

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene resulting in coded diffraction patterns at the sensor. Recently, the hybrid approach of model-driven network or deep unfolding has emerged as an effective alternative to conventional model-based and learning-based phase retrieval techniques because it allows for bounding the complexity of algorithms while also retaining their efficacy. Additionally, such hybrid approaches have shown promise in improving the design of DOEs that follow theoretical uniqueness conditions. There are opportunities to exploit novel experimental setups and resolve even more complex DOE phase retrieval applications. This paper presents an overview of algorithms and applications of deep unfolding for bootstrapped - regardless of near, middle, and far zones - phase retrieval.
Learning Neural Set Functions Under the Optimal Subset Oracle
Authors: Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
Date: 2022-03-03 00:00:00
Tasks: Anomaly Detection, Drug Discovery, Product Recommendation, Variational Inference

Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin.
Anomaly Detection in Big Data
Authors: Chandresh Kumar Maurya
Date: 2022-03-03 00:00:00
Tasks: Anomaly Detection

Anomaly is defined as a state of the system that do not conform to the normal behavior. For example, the emission of neutrons in a nuclear reactor channel above the specified threshold is an anomaly. Big data refers to the data set that is \emph{high volume, streaming, heterogeneous, distributed} and often \emph{sparse}. Big data is not uncommon these days. For example, as per Internet live stats, the number of tweets posted per day has gone above 500 millions. Due to data explosion in data laden domains, traditional anomaly detection techniques developed for small data sets scale poorly on large-scale data sets. Therefore, we take an alternative approach to tackle anomaly detection in big data. Essentially, there are two ways to scale anomaly detection in big data. The first is based on the \emph{online} learning and the second is based on the \emph{distributed} learning. Our aim in the thesis is to tackle big data problems while detecting anomaly efficiently. To that end, we first take \emph{streaming} issue of the big data and propose Passive-Aggressive GMEAN (PAGMEAN) algorithms. Although, online learning algorithm can scale well over large number of data points and dimensions, they can not process data when it is distributed at multiple locations; which is quite common these days. Therefore, we propose anomaly detection algorithm which is inherently distributed using ADMM. Finally, we present a case study on anomaly detection in nuclear power plant data.
Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data
Authors: Lukas Platinsky, Tayyab Naseer, Hui Chen, Ben Haines, Haoyue Zhu, Hugo Grimmett, Luca Del Pero
Date: 2022-03-03 00:00:00
Tasks: Motion Planning

With the Autonomous Vehicle (AV) industry shifting towards machine-learned approaches for motion planning, the performance of self-driving systems is starting to rely heavily on large quantities of expert driving demonstrations. However, collecting this demonstration data typically involves expensive HD sensor suites (LiDAR + RADAR + cameras), which quickly becomes financially infeasible at the scales required. This motivates the use of commodity sensors like cameras for data collection, which are an order of magnitude cheaper than HD sensor suites, but offer lower fidelity. Leveraging these sensors for training an AV motion planner opens a financially viable path to observe the `long tail' of driving events. As our main contribution we show it is possible to train a high-performance motion planner using commodity vision data which outperforms planners trained on HD-sensor data for a fraction of the cost. To the best of our knowledge, we are the first to demonstrate this using real-world data. We compare the performance of the autonomy system on these two different sensor configurations, and show that we can compensate for the lower sensor fidelity by means of increased quantity: a planner trained on 100h of commodity vision data outperforms the one with 25h of expensive HD data. We also share the engineering challenges we had to tackle to make this work.
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation
Authors: KiYoon Yoo, Jangho Kim, Jiho Jang, Nojun Kwak
Date: 2022-03-03 00:00:00
Tasks: Adversarial Defense, Density Estimation, Sentiment Analysis, text-classification, Text Classification

Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversarial defense has been explored, but relatively few efforts have been made to detect adversarial examples. However, detecting adversarial examples may be crucial for automated tasks (e.g. review sentiment analysis) that wish to amass information about a certain population and additionally be a step towards a robust defense system. To this end, we release a dataset for four popular attack methods on four datasets and four models to encourage further research in this field. Along with it, we propose a competitive baseline based on density estimation that has the highest AUC on 29 out of 30 dataset-attack-model combinations. Source code is available in https://github.com/anoymous92874838/text-adv-detection.
Joint Probability Estimation Using Tensor Decomposition and Dictionaries
Authors: Shaan ul Haque, Ajit Rajwade, Karthik S. Gurumoorthy
Date: 2022-03-03 00:00:00
Tasks: Tensor Decomposition

In this work, we study non-parametric estimation of joint probabilities of a given set of discrete and continuous random variables from their (empirically estimated) 2D marginals, under the assumption that the joint probability could be decomposed and approximated by a mixture of product densities/mass functions. The problem of estimating the joint probability density function (PDF) using semi-parametric techniques such as Gaussian Mixture Models (GMMs) is widely studied. However such techniques yield poor results when the underlying densities are mixtures of various other families of distributions such as Laplacian or generalized Gaussian, uniform, Cauchy, etc. Further, GMMs are not the best choice to estimate joint distributions which are hybrid in nature, i.e., some random variables are discrete while others are continuous. We present a novel approach for estimating the PDF using ideas from dictionary representations in signal processing coupled with low rank tensor decompositions. To the best our knowledge, this is the first work on estimating joint PDFs employing dictionaries alongside tensor decompositions. We create a dictionary of various families of distributions by inspecting the data, and use it to approximate each decomposed factor of the product in the mixture. Our approach can naturally handle hybrid $N$-dimensional distributions. We test our approach on a variety of synthetic and real datasets to demonstrate its effectiveness in terms of better classification rates and lower error rates, when compared to state of the art estimators.
On partitioning of an SHM problem and parallels with transfer learning
Authors: G. Tsialiamanis, D. J. Wagg, P. A. Gardner, N. Dervilis, K. Worden
Date: 2022-03-03 00:00:00
Tasks: Structural Health Monitoring, Transfer Learning

In the current work, a problem-splitting approach and a scheme motivated by transfer learning is applied to a structural health monitoring problem. The specific problem in this case is that of localising damage on an aircraft wing. The original experiment is described, together with the initial approach, in which a neural network was trained to localise damage. The results were not ideal, partly because of a scarcity of training data, and partly because of the difficulty in resolving two of the damage cases. In the current paper, the problem is split into two sub-problems and an increase in classification accuracy is obtained. The sub-problems are obtained by separating out the most difficult-to-classify damage cases. A second approach to the problem is considered by adopting ideas from transfer learning (usually applied in much deeper) networks to see if a network trained on the simpler damage cases can help with feature extraction in the more difficult cases. The transfer of a fixed trained batch of layers between the networks is found to improve classification by making the classes more separable in the feature space and to speed up convergence.
On an application of graph neural networks in population based SHM
Authors: G. Tsialiamanis, C. Mylonas, E. Chatzi, D. J. Wagg, N. Dervilis, K. Worden
Date: 2022-03-03 00:00:00
Tasks: Graph Neural Network, Structural Health Monitoring

Attempts have been made recently in the field of population-based structural health monitoring (PBSHM), to transfer knowledge between SHM models of different structures. The attempts have been focussed on homogeneous and heterogeneous populations. A more general approach to transferring knowledge between structures, is by considering all plausible structures as points on a multidimensional base manifold and building a fibre bundle. The idea is quite powerful, since, a mapping between points in the base manifold and their fibres, the potential states of any arbitrary structure, can be learnt. A smaller scale problem, but still useful, is that of learning a specific point of every fibre, i.e. that corresponding to the undamaged state of structures within a population. Under the framework of PBSHM, a data-driven approach to the aforementioned problem is developed. Structures are converted into graphs and inference is attempted within a population, using a graph neural network (GNN) algorithm. The algorithm solves a major problem existing in such applications. Structures comprise different sizes and are defined as abstract objects, thus attempting to perform inference within a heterogeneous population is not trivial. The proposed approach is tested in a simulated population of trusses. The goal of the application is to predict the first natural frequency of trusses of different sizes, across different environmental temperatures and having different bar member types. After training the GNN using part of the total population, it was tested on trusses that were not included in the training dataset. Results show that the accuracy of the regression is satisfactory even in structures with higher number of nodes and members than those used to train it.
Improving X-ray Diagnostics through Eye-Tracking and XR
Authors: Catarina Moreira, Isabel Blanco Nobre, Sandra Costa Sousa, João Madeiras Pereira, Joaquim Jorge
Date: 2022-03-03 00:00:00

There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively. As radiologists strive to maximize productivity, it is essential to consider the impact of reading rooms in interpreting complex examinations and ensure that higher volume and reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a disruptive technology for clinical practice in assessing X-ray images. We argue that conjugating eye-tracking with VR devices and Machine Learning may overcome obstacles posed by inadequate ergonomic postures and poor room conditions that often cause erroneous diagnostics when professionals examine digital images.
On generating parametrised structural data using conditional generative adversarial networks
Authors: G. Tsialiamanis, D. J. Wagg, N. Dervilis, K. Worden
Date: 2022-03-03 00:00:00
Tasks: Generative Adversarial Network, Structural Health Monitoring

A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and their condition. Such methods almost exclusively rely on the quality of the data. Within the SHM discipline, data do not always suffice to build models with satisfactory accuracy for given tasks. Even worse, data may be completely missing from one's dataset, regarding the behaviour of a structure under different environmental conditions. In the current work, with a view to confronting such issues, the generation of artificial data using a variation of the generative adversarial network (GAN) algorithm, is used. The aforementioned variation is that of the conditional GAN or cGAN. The algorithm is not only used to generate artificial data, but also to learn transformations of manifolds according to some known parameters. Assuming that the structure's response is represented by points in a manifold, part of the space will be formed due to variations in external conditions affecting the structure. This idea proves efficient in SHM, as it is exploited to generate structural data for specific values of environmental coefficients. The scheme is applied here on a simulated structure which operates under different temperature and humidity conditions. The cGAN is trained on data for some discrete values of the temperature within some range, and is able to generate data for every temperature in this range with satisfactory accuracy. The novelty, compared to classic regression in similar problems, is that the cGAN allows unknown environmental parameters to affect the structure and can generate whole manifolds of data for every value of the known parameters, while the unknown ones vary within the generated manifolds.
Parallel feature selection based on the trace ratio criterion
Authors: Thu Nguyen, Thanh Nhan Phan, Van Nhuong Nguyen, Thanh Binh Nguyen, Pål Halvorsen, Michael Riegler
Date: 2022-03-03 00:00:00
Tasks: feature selection, Management

The growth of data today poses a challenge in management and inference. While feature extraction methods are capable of reducing the size of the data for inference, they do not help in minimizing the cost of data storage. On the other hand, feature selection helps to remove the redundant features and therefore is helpful not only in inference but also in reducing management costs. This work presents a novel parallel feature selection approach for classification, namely Parallel Feature Selection using Trace criterion (PFST), which scales up to very large datasets. Our method uses trace criterion, a measure of class separability used in Fisher's Discriminant Analysis, to evaluate feature usefulness. We analyzed the criterion's desirable properties theoretically. Based on the criterion, PFST rapidly finds important features out of a set of features for big datasets by first making a forward selection with early removal of seemingly redundant features parallelly. After the most important features are included in the model, we check back their contribution for possible interaction that may improve the fit. Lastly, we make a backward selection to check back possible redundant added by the forward steps. We evaluate our methods via various experiments using Linear Discriminant Analysis as the classifier on selected features. The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison. In addition, the classifier trained on the features selected by PFST not only achieves better accuracy than the ones chosen by other approaches but can also achieve better accuracy than the classification on all available features.
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
Authors: Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
Date: 2022-03-03 00:00:00

Neural network on Riemannian symmetric space such as hyperbolic space and the manifold of symmetric positive definite (SPD) matrices is an emerging subject of research in geometric deep learning. Based on the well-established framework of the Helgason-Fourier transform on the noncompact symmetric space, we present a fully-connected network and its associated ridgelet transform on the noncompact symmetric space, covering the hyperbolic neural network (HNN) and the SPDNet as special cases. The ridgelet transform is an analysis operator of a depth-2 continuous network spanned by neurons, namely, it maps an arbitrary given function to the weights of a network. Thanks to the coordinate-free reformulation, the role of nonlinear activation functions is revealed to be a wavelet function, and the reconstruction formula directly yields the universality of the proposed networks.
Learning Group Importance using the Differentiable Hypergeometric Distribution
Authors: Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
Date: 2022-03-03 00:00:00
Tasks: Clustering, Selection bias, Weakly-supervised Learning

Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability distributions over correct combinations of subset sizes are non-differentiable due to hard constraints, which prohibit gradient-based optimization. In this work, we propose the differentiable hypergeometric distribution. The hypergeometric distribution models the probability of different group sizes based on their relative importance. We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering. In both applications, we outperform previous approaches, which rely on suboptimal heuristics to model the unknown size of groups.
Physics-Data Driven Machine Learning Based Model: A Hybrid Way for Nonlinear, Dynamic, and Open-loop Identification of IPMC Soft Artificial Muscles
Authors: Mohsen Annabestani, Mohammad Hossein Sayyad, Zahra Meskar, Mehdi Fardmanesh, Barbara Mazzolai
Date: 2022-03-03 00:00:00

Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choices among biocompatible materials for industrial and biomedical applications. Despite their advantages, some of their drawbacks include non-linear and hysteretic behavior, which complicates the modeling process. In previous works, usually autoregressive models were used to predict the behavior of an IPMC actuator. The main drawback of using an autoregressive model is that it cannot be used in mobile and real-time applications. In this study, we proposed a hybrid analytical intelligent model for an IPMC actuator. The most outstanding feature of this model is its non-autoregressive structure. The hybrid concept proposed in this study can be generalized to various problems other than IPMCs. The structure used in this work comprises an analytical model and a deep neural network, providing a non-linear, dynamic, and non-autoregressive model for the IPMC actuator. Lastly, the average NMSE achieved using the proposed hybrid model is 9.5781e-04 showing a significant drop in the error rate compared to other non-autoregressive structures.
Ensemble Methods for Robust Support Vector Machines using Integer Programming
Authors: Jannis Kurtz
Date: 2022-03-03 00:00:00
Tasks: Binary Classification

In this work we study binary classification problems where we assume that our training data is subject to uncertainty, i.e. the precise data points are not known. To tackle this issue in the field of robust machine learning the aim is to develop models which are robust against small perturbations in the training data. We study robust support vector machines (SVM) and extend the classical approach by an ensemble method which iteratively solves a non-robust SVM on different perturbations of the dataset, where the perturbations are derived by an adversarial problem. Afterwards for classification of an unknown data point we perform a majority vote of all calculated SVM solutions. We study three different variants for the adversarial problem, the exact problem, a relaxed variant and an efficient heuristic variant. While the exact and the relaxed variant can be modeled using integer programming formulations, the heuristic one can be implemented by an easy and efficient algorithm. All derived methods are tested on random and realistic datasets and the results indicate that the derived ensemble methods have a much more stable behaviour when changing the protection level compared to the classical robust SVM model.