pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Conceal: A Deep Learning Based Method for Preserving Privacy and Avoiding Prejudice
Authors: Moshe Hanukoglu, Nissan Goldberg, Aviv Rovshitz, Amos Azaria
Date: 2019-09-19 00:00:00
Tasks: Privacy Preserving

In this paper, we introduce a learning model able to conceals personal information (e.g. gender, age, ethnicity, etc.) from an image, while maintaining any additional information present in the image (e.g. smile, hair-style, brightness). Our trained model is not provided the information that it is concealing, and does not try learning it either. Namely, we created a variational autoencoder (VAE) model that is trained on a dataset including labels of the information one would like to conceal (e.g. gender, ethnicity, age). These labels are directly added to the VAE's sampled latent vector. Due to the limited number of neurons in the latent vector and its appended noise, the VAE avoids learning any relation between the given images and the given labels, as those are given directly. Therefore, the encoded image lacks any of the information one wishes to conceal. The encoding may be decoded back into an image according to any provided properties (e.g. a 40 year old woman). The proposed architecture can be used as a mean for privacy preserving and can serve as an input to systems, which will become unbiased and not suffer from prejudice. We believe that privacy and discrimination are two of the most important aspects in which the community should try and develop methods to prevent misuse of technological advances.
Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions
Authors: Hesham Al-Bataineh, Wael Farhan, Ahmad Mustafa, Haitham Seelawi, Hussein T. Al-Natsheh
Date: 2019-09-19 00:00:00
Tasks: Community Question Answering, Question Answering, Question Similarity, Semantic Similarity, Semantic Textual Similarity

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.
Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates
Authors: Thomas Winters
Date: 2019-09-19 00:00:00
Tasks: Text Generation

Automatically imitating input text is a common task in natural language generation, often used to create humorous results. Classic algorithms for learning to imitate text, e.g. simple Markov chains, usually have a trade-off between originality and syntactic correctness. We present two ways of automatically parodying philosophical statements from examples overcoming this issue, and show how these can work in interactive systems as well. The first algorithm uses interpolated Markov models with extensions to improve the quality of the generated texts. For the second algorithm, we propose dynamically extracting templates and filling these with new content. To illustrate these algorithms, we implemented TorfsBot, a Twitterbot imitating the witty, semi-philosophical tweets of professor Rik Torfs, the previous KU Leuven rector. We found that users preferred generative models that focused on local coherent sentences, rather than those mimicking the global structure of a philosophical statement. The proposed algorithms are thus valuable new tools for automatic parody as well as template learning systems.
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction
Authors: Alexander Schulz, Fabian Hinder, Barbara Hammer
Date: 2019-09-19 00:00:00
Tasks: Dimensionality Reduction

Machine learning algorithms using deep architectures have been able to implement increasingly powerful and successful models. However, they also become increasingly more complex, more difficult to comprehend and easier to fool. So far, most methods in the literature investigate the decision of the model for a single given input datum. In this paper, we propose to visualize a part of the decision function of a deep neural network together with a part of the data set in two dimensions with discriminative dimensionality reduction. This enables us to inspect how different properties of the data are treated by the model, such as outliers, adversaries or poisoned data. Further, the presented approach is complementary to the mentioned interpretation methods from the literature and hence might be even more useful in combination with those. Code is available at https://github.com/LucaHermes/DeepView .
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
Authors: Denis Kleyko, Mansour Kheffache, E. Paxon Frady, Urban Wiklund, Evgeny Osipov
Date: 2019-09-19 00:00:00
Tasks: BIG-bench Machine Learning

The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we focus on resource-efficient randomly connected neural networks known as Random Vector Functional Link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world datasets from the UCI Machine Learning Repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small n-bits integers, which results in a computationally efficient architecture. Finally, through hardware Field-Programmable Gate Array (FPGA) implementations, we show that such an approach consumes approximately eleven times less energy than that of the conventional RVFL.
Synthèse non quadratique H$\infty$ de contrôleurs décentralisés pour un ensemble de descripteurs flous T-S interconnectés
Authors: Dalel Jabri, Kevin Guelton, Noureddine Manamanni, Mohammed Naceur Abdelkrim
Date: 2019-09-19 00:00:00

This paper deals with the non-quadratic decentralized stabilization of a set of n Takagi-Sugeno descriptors. To ensure the stability of the whole closed-loop dynamics and to minimize interconnection effects between subsystems, the main result allows designing a network of non Parallel Distributed Compensation control laws via a H $\infty$ criterion. Sufficient conditions, obtained from a non quadratic fuzzy Lyapunov approach, are provided in terms of Linear Matrix Inequalities. Finally, a numerical example illustrates the efficiency of the proposed decentralized control approach.
Synthesis of Realistic ECG using Generative Adversarial Networks
Authors: Anne Marie Delaney, Eoin Brophy, Tomas E. Ward
Date: 2019-09-19 00:00:00
Tasks: De-identification, Inference Attack, Membership Inference Attack, Time Series, Time Series Analysis

Access to medical data is highly restricted due to its sensitive nature, preventing communities from using this data for research or clinical training. Common methods of de-identification implemented to enable the sharing of data are sometimes inadequate to protect the individuals contained in the data. For our research, we investigate the ability of generative adversarial networks (GANs) to produce realistic medical time series data which can be used without concerns over privacy. The aim is to generate synthetic ECG signals representative of normal ECG waveforms. GANs have been used successfully to generate good quality synthetic time series and have been shown to prevent re-identification of individual records. In this work, a range of GAN architectures are developed to generate synthetic sine waves and synthetic ECG. Two evaluation metrics are then used to quantitatively assess how suitable the synthetic data is for real world applications such as clinical training and data analysis. Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack. For the first time we both quantitatively and qualitatively demonstrate that GAN architecture can successfully generate time series signals that are not only structurally similar to the training sets but also diverse in nature across generated samples. We also report on their ability to withstand a simple membership inference attack, protecting the privacy of the training set.
Timage -- A Robust Time Series Classification Pipeline
Authors: Marc Wenninger, Sebastian P. Bayerl, Jochen Schmidt, Korbinian Riedhammer
Date: 2019-09-19 00:00:00
Tasks: Benchmarking, Classification, General Classification, image-classification, Image Classification, Time Series, Time Series Analysis, Time Series Classification, Transfer Learning

Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset.
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Authors: Zhuoxun He, Lingxi Xie, Xin Chen, Ya zhang, Yan-Feng Wang, Qi Tian
Date: 2019-09-19 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, object-detection, Object Detection

Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a few intensive data augmentation techniques, which indeed improved accuracy, yet we notice that these methods augment data have also caused a considerable gap between clean and augmented data. In this paper, we revisit this problem from an analytical perspective, for which we estimate the upper-bound of expected risk using two terms, namely, empirical risk and generalization error, respectively. We develop an understanding of data augmentation as regularization, which highlights the major features. As a result, data augmentation significantly reduces the generalization error, but meanwhile leads to a slightly higher empirical risk. On the assumption that data augmentation helps models converge to a better region, the model can benefit from a lower empirical risk achieved by a simple method, i.e., using less-augmented data to refine the model trained on fully-augmented data. Our approach achieves consistent accuracy gain on a few standard image classification benchmarks, and the gain transfers to object detection.
Differentially Private Regression and Classification with Sparse Gaussian Processes
Authors: Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence
Date: 2019-09-19 00:00:00
Tasks: Classification, Gaussian Processes, General Classification, regression

A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable privacy guarantees of differential privacy which has been combined with Gaussian processes through the previously published \emph{cloaking method}. In this paper we solve several shortcomings of this method, starting with the problem of predictions in regions with low data density. We experiment with the use of inducing points to provide a sparse approximation and show that these can provide robust differential privacy in outlier areas and at higher dimensions. We then look at classification, and modify the Laplace approximation approach to provide differentially private predictions. We then combine this with the sparse approximation and demonstrate the capability to perform classification in high dimensions. We finally explore the issue of hyperparameter selection and develop a method for their private selection. This paper and associated libraries provide a robust toolkit for combining differential privacy and GPs in a practical manner.
Weighted Linear Bandits for Non-Stationary Environments
Authors: Yoan Russac, Claire Vernade, Olivier Cappé
Date: 2019-09-19 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past. This involves studying the deviations of the sequential weighted least-squares estimator under generic assumptions. As a by-product, we obtain novel deviation results that can be used beyond non-stationary environments. We provide theoretical guarantees on the behavior of D-LinUCB in both slowly-varying and abruptly-changing environments. We obtain an upper bound on the dynamic regret that is of order d^{2/3} B\_T^{1/3}T^{2/3}, where B\_T is a measure of non-stationarity (d and T being, respectively, dimension and horizon). This rate is known to be optimal. We also illustrate the empirical performance of D-LinUCB and compare it with recently proposed alternatives in simulated environments.
Multi-sense Definition Modeling using Word Sense Decompositions
Authors: Ruimin Zhu, Thanapon Noraset, Alisa Liu, Wenxin Jiang, Doug Downey
Date: 2019-09-19 00:00:00
Tasks: Word Embeddings

Word embeddings capture syntactic and semantic information about words. Definition modeling aims to make the semantic content in each embedding explicit, by outputting a natural language definition based on the embedding. However, existing definition models are limited in their ability to generate accurate definitions for different senses of the same word. In this paper, we introduce a new method that enables definition modeling for multiple senses. We show how a Gumble-Softmax approach outperforms baselines at matching sense-specific embeddings to definitions during training. In experiments, our multi-sense definition model improves recall over a state-of-the-art single-sense definition model by a factor of three, without harming precision.
Detailed comparison of communication efficiency of split learning and federated learning
Authors: Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar
Date: 2019-09-18 00:00:00
Tasks: Federated Learning

We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each method outperforms the other in terms of communication efficiency. We consider various practical scenarios of distributed learning setup and juxtapose the two methods under various real-life scenarios. We consider settings of small and large number of clients as well as small models (1M - 6M parameters), large models (10M - 200M parameters) and very large models (1 Billion-100 Billion parameters). We show that increasing number of clients or increasing model size favors split learning setup over the federated while increasing the number of data samples while keeping the number of clients or model size low makes federated learning more communication efficient.
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Authors: Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash
Date: 2019-09-18 00:00:00

Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications. In this paper, we present a long short-term memory neural network to approximate the nonlinear component of the reduced-order model (ROM) of an advection-dominated partial differential equation. This is motivated by the fact that the nonlinear term is the most expensive component of a successful ROM. For our approach, we utilize a Galerkin projection to isolate the linear and the transient components of the dynamical system and then use discrete empirical interpolation to generate training data for supervised learning. We note that the numerical time-advancement and linear-term computation of the system ensure a greater preservation of physics than does a process that is fully modeled. Our results show that the proposed framework recovers transient dynamics accurately without nonlinear term computations in full-order space and represents a cost-effective alternative to solely equation-based ROMs.
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant
Authors: Deepak Muralidharan, Justine Kao, Xiao Yang, Lin Li, Lavanya Viswanathan, Mubarak Seyed Ibrahim, Kevin Luikens, Stephen Pulman, Ashish Garg, Atish Kothari, Jason Williams
Date: 2019-09-18 00:00:00
Tasks: Data Augmentation, Multi-Task Learning

Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expensive and time consuming to collect. The ability to incorporate unsupervised, weakly supervised, or distantly supervised data holds significant promise in overcoming this bottleneck. In this paper, we describe a framework that leverages user engagement signals (user behaviors that demonstrate a positive or negative response to content) to automatically create granular entity labels for training data augmentation. Strategies such as multi-task learning and validation using an external knowledge base are employed to incorporate the engagement annotated data and to boost the model's accuracy on a sequence labeling task. Our results show that learning from data automatically labeled by user engagement signals achieves significant accuracy gains in a production deep learning system, when measured on both the sequence labeling task as well as on user facing results produced by the system end-to-end. We believe this is the first use of user engagement signals to help generate training data for a sequence labeling task on a large scale, and can be applied in practical settings to speed up new feature deployment when little human annotated data is available.
Using Quantifier Elimination to Enhance the Safety Assurance of Deep Neural Networks
Authors: Hao Ren, Sai Krishnan Chandrasekar, Anitha Murugesan
Date: 2019-09-18 00:00:00
Tasks: Collision Avoidance

Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and medical devices. Hence, there is a surge in interest to explore the use of advanced mathematical techniques such as formal methods to address this challenge. In fact, the initial results of such efforts are promising. Along these lines, we propose the use of quantifier elimination (QE) - a formal method technique, as a complimentary technique to the state-of-the-art static analysis and verification procedures. Using an airborne collision avoidance DNN as a case example, we illustrate the use of QE to formulate the precise range forward propagation through a network as well as analyze its robustness. We discuss the initial results of this ongoing work and explore the future possibilities of extending this approach and/or integrating it with other approaches to perform advanced safety assurance of DNNs.
Causal Modeling for Fairness in Dynamical Systems
Authors: Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel
Date: 2019-09-18 00:00:00
Proceeding: ICML 2020 1
Tasks: Fairness

In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in such dynamical systems. We show that this formulation affords several new directions of inquiry to the modeler, where causal assumptions can be expressed and manipulated. We emphasize the importance of computing interventional quantities in the dynamical fairness setting, and show how causal assumptions enable simulation (when environment dynamics are known) and off-policy estimation (when dynamics are unknown) of intervention on short- and long-term outcomes, at both the group and individual levels.
Meta-Neighborhoods
Authors: Siyuan Shan, Yang Li, Junier Oliva
Date: 2019-09-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Meta-Learning, Multi-Task Learning

Making an adaptive prediction based on one's input is an important ability for general artificial intelligence. In this work, we step forward in this direction and propose a semi-parametric method, Meta-Neighborhoods, where predictions are made adaptively to the neighborhood of the input. We show that Meta-Neighborhoods is a generalization of $k$-nearest-neighbors. Due to the simpler manifold structure around a local neighborhood, Meta-Neighborhoods represent the predictive distribution $p(y \mid x)$ more accurately. To reduce memory and computation overhead, we propose induced neighborhoods that summarize the training data into a much smaller dictionary. A meta-learning based training mechanism is then exploited to jointly learn the induced neighborhoods and the model. Extensive studies demonstrate the superiority of our method.
How Does Batch Normalization Help Binary Training?
Authors: Eyyüb Sari, Mouloud Belbahri, Vahid Partovi Nia
Date: 2019-09-18 00:00:00
Tasks: Quantization

Binary Neural Networks (BNNs) are difficult to train, and suffer from drop of accuracy. It appears in practice that BNNs fail to train in the absence of Batch Normalization (BatchNorm) layer. We find the main role of BatchNorm is to avoid exploding gradients in the case of BNNs. This finding suggests that the common initialization methods developed for full-precision networks are irrelevant to BNNs. We build a theoretical study on the role of BatchNorm in binary training, backed up by numerical experiments.