pwc-archive/papers-with-abstracts huggingface dataset:
Confirmatory Factor Analysis -- A Case study
Authors: Rui Portocarrero Sarmento, Vera Costa
Date: 2019-05-08 00:00:00

Confirmatory Factor Analysis (CFA) is a particular form of factor analysis, most commonly used in social research. In confirmatory factor analysis, the researcher first develops a hypothesis about what factors they believe are underlying the used measures and may impose constraints on the model based on these a priori hypotheses. For example, if two factors are accounting for the covariance in the measures, and these factors are unrelated to one another, we can create a model where the correlation between factor X and factor Y is set to zero. Measures could then be obtained to assess how well the fitted model captured the covariance between all the items or measures in the model. Thus, if the results of statistical tests of the model fit indicate a poor fit, the model will be rejected. If the fit is weak, it may be due to a variety of reasons. We propose to introduce state of the art techniques to do CFA in R language. Then, we propose to do some examples of CFA with R and some datasets, revealing several scenarios where CFA is relevant.
Interval Valued Trapezoidal Neutrosophic Set for Prioritization of Non-functional Requirements
Authors: Kiran Khatter
Date: 2019-05-07 00:00:00
Tasks: Attribute, Decision Making

This paper discusses the trapezoidal fuzzy number(TrFN); Interval-valued intuitionistic fuzzy number(IVIFN); neutrosophic set and its operational laws; and, trapezoidal neutrosophic set(TrNS) and its operational laws. Based on the combination of IVIFN and TrNS, an Interval Valued Trapezoidal Neutrosophic Set (IVTrNS) is proposed followed by its operational laws. The paper also presents the score and accuracy functions for the proposed Interval Valued Trapezoidal Neutrosophic Number (IVTrNN). Then, an interval valued trapezoidal neutrosophic weighted arithmetic averaging (IVTrNWAA) operator is introduced to combine the trapezoidal information which is neutrosophic and in the unit interval of real numbers. Finally, a method is developed to handle the problems in the multi attribute decision making(MADM) environment using IVTrNWAA operator followed by a numerical example of NFRs prioritization to illustrate the relevance of the developed method.
Design Space Exploration via Answer Set Programming Modulo Theories
Authors: Philipp Wanko
Date: 2019-05-07 00:00:00

The design of embedded systems, that are ubiquitously used in mobile devices and cars, is becoming continuously more complex such that efficient system-level design methods are becoming crucial. My research aims at developing systems that help the designer express the complex design problem in a declarative way and explore the design space to obtain divers sets of solutions with desirable properties. To that end, we employ knowledge representation and reasoning capabilities of ASP in combination with background theories. As a result, for the first time, we proposed a sophisticated methodology that allows for the direct integration of multi-objective optimization of non-linear objectives into ASP. This includes unique results of diverse sub-problems covered in several publications which I will present in this work.
Sparse residual tree and forest
Authors: Xin Xu, Xiaopeng Luo
Date: 2019-02-18 00:00:00
Tasks: Tree Decomposition

Sparse residual tree (SRT) is an adaptive exploration method for multivariate
scattered data approximation. It leads to sparse and stable approximations in
areas where the data is sufficient or redundant, and points out the possible
local regions where data refinement is needed. Sparse residual forest (SRF) is
a combination of SRT predictors to further improve the approximation accuracy
and stability according to the error characteristics of SRTs. The hierarchical
parallel SRT algorithm is based on both tree decomposition and adaptive radial
basis function (RBF) explorations, whereby for each child a sparse and proper
RBF refinement is added to the approximation by minimizing the norm of the
residual inherited from its parent. The convergence results are established for
both SRTs and SRFs. The worst case time complexity of SRTs is
$\mathcal{O}(N\log_2N)$ for the initial work and $\mathcal{O}(\log_2N)$ for
each prediction, meanwhile, the worst case storage requirement is
$\mathcal{O}(N\log_2N)$, where the $N$ data points can be arbitrary
distributed. Numerical experiments are performed for several illustrative
examples.
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography
Authors: Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, Ren Ng
Date: 2019-05-15 00:00:00
Tasks: BIG-bench Machine Learning, Saliency Detection

In cinema, large camera lenses create beautiful shallow depth of field (DOF), but make focusing difficult and expensive. Accurate cinema focus usually relies on a script and a person to control focus in realtime. Casual videographers often crave cinematic focus, but fail to achieve it. We either sacrifice shallow DOF, as in smartphone videos; or we struggle to deliver accurate focus, as in videos from larger cameras. This paper is about a new approach in the pursuit of cinematic focus for casual videography. We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver context-aware autofocus for the current frame. To create refocusable video, we extend recent machine learning methods designed for still photography, contributing a new dataset for machine training, a rendering model better suited to cinema focus, and a filtering solution for temporal coherence. To choose focus accurately for each frame, we demonstrate autofocus that looks at upcoming video frames and applies AI-assist modules such as motion, face, audio and saliency detection. We also show that autofocus benefits from machine learning and a large-scale video dataset with focus annotation, where we use our RVR-LAAF GUI to create this sizable dataset efficiently. We deliver, for example, a shallow DOF video where the autofocus transitions onto each person before she begins to speak. This is impossible for conventional camera autofocus because it would require seeing into the future.
Exact Hard Monotonic Attention for Character-Level Transduction
Authors: Shijie Wu, Ryan Cotterell
Date: 2019-05-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Hard Attention, Inductive Bias, Morphological Inflection

Many common character-level, string-to string transduction tasks, e.g., grapheme-tophoneme conversion and morphological inflection, consist almost exclusively of monotonic transductions. However, neural sequence-to sequence models that use non-monotonic soft attention often outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias for these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns a latent alignment jointly while learning to transduce. With the help of dynamic programming, we are able to compute the exact marginalization over all monotonic alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks. Code is available at https://github.com/shijie-wu/neural-transducer.
Which principal components are most sensitive to distributional changes?
Authors: Martin Tveten
Date: 2019-05-15 00:00:00
Tasks: Anomaly Detection, Sensitivity

PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity is defined by the Hellinger distance between distributions before and after a change. In particular, this is almost always the case if only one parameter of the bivariate normal distribution changes, i.e., the change is sparse. Simulations indicate that the minor projections are the most sensitive for a large range of changes and pre-change settings in higher dimensions as well. This motivates using the minor projections for detecting sparse distributional changes in high-dimensional data.
What do you learn from context? Probing for sentence structure in contextualized word representations
Authors: Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick
Date: 2019-05-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modelling, Sentence

The jiant toolkit for general-purpose text understanding models
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Authors: Ziyuan Zhao, Kerui Zhang, Xuejie Hao, Jing Tian, Matthew Chin Heng Chua, Li Chen, Xin Xu
Date: 2019-05-15 00:00:00
Tasks: Classification, Diabetic Retinopathy Grading, General Classification, image-classification, Image Classification

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
Authors: Dong Wook Shu, Sung Woo Park, Junseok Kwon
Date: 2019-05-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: Generative Adversarial Network, Point Cloud Generation

In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution network (TreeGCN) is introduced as a generator for tree-GAN. Because TreeGCN performs graph convolutions within a tree, it can use ancestor information to boost the representation power for features. To evaluate GANs for 3D point clouds accurately, we develop a novel evaluation metric called Frechet point cloud distance (FPD). Experimental results demonstrate that the proposed tree-GAN outperforms state-of-the-art GANs in terms of both conventional metrics and FPD, and can generate point clouds for different semantic parts without prior knowledge.
A Surprisingly Robust Trick for Winograd Schema Challenge
Authors: Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, Thomas Lukasiewicz
Date: 2019-05-15 00:00:00
Tasks: Common Sense Reasoning, Coreference Resolution, Language Modeling, Language Modelling, Natural Language Inference, Natural Language Understanding, WNLI

The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 strongly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.5% and 74.7% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8% and 9.6%, respectively. Furthermore, our fine-tuned models are also consistently more robust on the "complex" subsets of WSC273, introduced by Trichelair et al. (2018).
A Human-Centered Approach to Interactive Machine Learning
Authors: Kory W. Mathewson
Date: 2019-05-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

The interactive machine learning (IML) community aims to augment humans' ability to learn and make decisions over time through the development of automated decision-making systems. This interaction represents a collaboration between multiple intelligent systems---humans and machines. A lack of appropriate consideration for the humans involved can lead to problematic system behaviour, and issues of fairness, accountability, and transparency. This work presents a human-centred thinking approach to applying IML methods. This guide is intended to be used by AI practitioners who incorporate human factors in their work. These practitioners are responsible for the health, safety, and well-being of interacting humans. An obligation of responsibility for public interaction means acting with integrity, honesty, fairness, and abiding by applicable legal statutes. With these values and principles in mind, we as a research community can better achieve the collective goal of augmenting human ability. This practical guide aims to support many of the responsible decisions necessary throughout iterative design, development, and dissemination of IML systems.
Output-Constrained Bayesian Neural Networks
Authors: Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez
Date: 2019-05-15 00:00:00

Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an interpretable approach of enforcing a range of constraints, fully consistent with the Bayesian framework and amenable to black-box inference. We demonstrate how OC-BNNs improve model robustness and prevent the prediction of infeasible outputs in two real-world applications of healthcare and robotics.
End-to-End Multi-Channel Speech Separation
Authors: Rongzhi Gu, Jian Wu, Shi-Xiong Zhang, Lian-Wu Chen, Yong Xu, Meng Yu, Dan Su, Yuexian Zou, Dong Yu
Date: 2019-05-15 00:00:00
Tasks: Speech Separation

The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation. The primary contributions of this work include 1) an integrated waveform-in waveform-out separation system in a single neural network architecture. 2) We reformulate the traditional short time Fourier transform (STFT) and inter-channel phase difference (IPD) as a function of time-domain convolution with a special kernel. 3) We further relaxed those fixed kernels to be learnable, so that the entire architecture becomes purely data-driven and can be trained from end-to-end. We demonstrate on the WSJ0 far-field speech separation task that, with the benefit of learnable spatial features, our proposed end-to-end multi-channel model significantly improved the performance of previous end-to-end single-channel method and traditional multi-channel methods.
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction
Authors: Narendra Patwardhan, Zequn Wang
Date: 2019-05-15 00:00:00
Tasks: Management, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimum controller which may not always be feasible in robotics due to safety and time consumption. Model-based methods such as PILCO or BlackDrops, while data-efficient, provide solutions with limited robustness and complexity. To address this tradeoff, we introduce active uncertainty reduction-based virtual environments, which are formed through limited trials conducted in the original environment. We provide an efficient method for uncertainty management, which is used as a metric for self-improvement by identification of the points with maximum expected improvement through adaptive sampling. Capturing the uncertainty also allows for better mimicking of the reward responses of the original system. Our approach enables the use of complex policy structures and reward functions through a unique combination of model-based and model-free methods, while still retaining the data efficiency. We demonstrate the validity of our method on several classic reinforcement learning problems in OpenAI gym. We prove that our approach offers a better modeling capacity for complex system dynamics as compared to established methods.
Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning
Authors: Martin J. Wainwright
Date: 2019-05-15 00:00:00
Tasks: Q-Learning, Reinforcement Learning

Motivated by the study of $Q$-learning algorithms in reinforcement learning, we study a class of stochastic approximation procedures based on operators that satisfy monotonicity and quasi-contractivity conditions with respect to an underlying cone. We prove a general sandwich relation on the iterate error at each time, and use it to derive non-asymptotic bounds on the error in terms of a cone-induced gauge norm. These results are derived within a deterministic framework, requiring no assumptions on the noise. We illustrate these general bounds in application to synchronous $Q$-learning for discounted Markov decision processes with discrete state-action spaces, in particular by deriving non-asymptotic bounds on the $\ell_\infty$-norm for a range of stepsizes. These results are the sharpest known to date, and we show via simulation that the dependence of our bounds cannot be improved in a worst-case sense. These results show that relative to a model-based $Q$-iteration, the $\ell_\infty$-based sample complexity of $Q$-learning is suboptimal in terms of the discount factor $\gamma$.
Predictive Online Convex Optimization
Authors: Antoine Lesage-Landry, Iman Shames, Joshua A. Taylor
Date: 2019-05-15 00:00:00

We incorporate future information in the form of the estimated value of future gradients in online convex optimization. This is motivated by demand response in power systems, where forecasts about the current round, e.g., the weather or the loads' behavior, can be used to improve on predictions made with only past observations. Specifically, we introduce an additional predictive step that follows the standard online convex optimization step when certain conditions on the estimated gradient and descent direction are met. We show that under these conditions and without any assumptions on the predictability of the environment, the predictive update strictly improves on the performance of the standard update. We give two types of predictive update for various family of loss functions. We provide a regret bound for each of our predictive online convex optimization algorithms. Finally, we apply our framework to an example based on demand response which demonstrates its superior performance to a standard online convex optimization algorithm.
Automatic Malware Description via Attribute Tagging and Similarity Embedding
Authors: Felipe N. Ducau, Ethan M. Rudd, Tad M. Heppner, Alex Long, Konstantin Berlin
Date: 2019-05-15 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Malware Detection, TAG

With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection. Although powerful for conviction of malicious artifacts, these methods do not produce any further information about the type of threat that has been detected neither allows for identifying relationships between malware samples. In this work, we address the information gap between machine learning and signature-based detection methods by learning a representation space for malware samples in which files with similar malicious behaviors appear close to each other. We do so by introducing a deep learning based tagging model trained to generate human-interpretable semantic descriptions of malicious software, which, at the same time provides potentially more useful and flexible information than malware family names. We show that the malware descriptions generated with the proposed approach correctly identify more than 95% of eleven possible tag descriptions for a given sample, at a deployable false positive rate of 1% per tag. Furthermore, we use the learned representation space to introduce a similarity index between malware files, and empirically demonstrate using dynamic traces from files' execution, that is not only more effective at identifying samples from the same families, but also 32 times smaller than those based on raw feature vectors.
Function Space Pooling For Graph Convolutional Networks
Authors: Padraig Corcoran
Date: 2019-05-15 00:00:00
Tasks: General Classification, Graph Classification

Convolutional layers in graph neural networks are a fundamental type of layer which output a representation or embedding of each graph vertex. The representation typically encodes information about the vertex in question and its neighbourhood. If one wishes to perform a graph centric task, such as graph classification, this set of vertex representations must be integrated or pooled to form a graph representation. In this article we propose a novel pooling method which maps a set of vertex representations to a function space representation. This method is distinct from existing pooling methods which perform a mapping to either a vector or sequence space. Experimental graph classification results demonstrate that the proposed method generally outperforms most baseline pooling methods and in some cases achieves best performance.
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Authors: Cristiano Saltori, Subhankar Roy, Nicu Sebe, Giovanni Iacca
Date: 2019-05-15 00:00:00
Tasks: Evolutionary Algorithms, Neural Architecture Search, Object Recognition

Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically searches for optimal network architectures using evolutionary algorithms, has recently become very popular. Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i.e., network architectures) and is therefore memory expensive. In this work, we propose a Regularized Evolutionary Algorithm with low memory footprint to evolve a dynamic image classifier. In details, we introduce novel custom operators that regularize the evolutionary process of a micro-population of 10 individuals. We conduct experiments on three different digits datasets (MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive results with the current state-of-the-art.