pwc-archive/papers-with-abstracts huggingface dataset:
The Basic Geometric Structures of Electromagnetic Digital Information: Statistical characterization of the digital measurement of spatio-Doppler and polarimetric fluctuations of the radar electromagnetic wave
Authors: Frédéric Barbaresco, Yann Cabanes
Date: 2020-06-26 00:00:00

The aim is to describe new geometric approaches to define the statistics of spatio-temporal and polarimetric measurements of the states of an electromagnetic wave, using the works of Maurice Fr{\'e}chet, Jean-Louis Koszul and Jean-Marie Souriau, with in particular the notion of 'average' state of this digital measurement as a Fr{\'e}chet barycentre in a metric space and a model derived from statistical mechanics to define and calculate a maximum density of entropy (extension of the notion of Gaussian) to describe the fluctuations of the electromagnetic wave. The article will illustrate these new tools with examples of radar application for Doppler, spatio-temporal and polarimetric measurement of the electromagnetic wave by introducing a distance on the covariance matrices of the electromagnetic digital signal, based on Fisher's metric from Information Geometry.
Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising
Authors: Michael Tashman, Jiayi Xie, John Hoffman, Lee Winikor, Rouzbeh Gerami
Date: 2020-06-01 00:00:00

Real-Time Bidding (RTB) display advertising is a method for purchasing display advertising inventory in auctions that occur within milliseconds. The performance of RTB campaigns is generally measured with a series of Key Performance Indicators (KPIs) - measurements used to ensure that the campaign is cost-effective and that it is purchasing valuable inventory. While an RTB campaign should ideally meet all KPIs, simultaneous improvement tends to be very challenging, as an improvement to any one KPI risks a detrimental effect toward the others. Here we present an approach to simultaneously controlling multiple KPIs with a PID-based feedback-control system. This method generates a control score for each KPI, based on both the output of a PID controller module and a metric that quantifies the importance of each KPI for internal business needs. On regular intervals, this algorithm - Sequential Control - will choose the KPI with the greatest overall need for improvement. In this way, our algorithm is able to continually seek the greatest marginal improvements to its current state. Multiple methods of control can be associated with each KPI, and can be triggered either simultaneously or chosen stochastically, in order to avoid local optima. In both offline ad bidding simulations and testing on live traffic, our methods proved to be effective in simultaneously controlling multiple KPIs, and bringing them toward their respective goals.
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Authors: Shaoqing Yuan, Parminder Bhatia, Busra Celikkaya, Haiyang Liu, Kyunghwan Choi
Date: 2020-06-17 00:00:00
Tasks: Clustering, Descriptive, Entity Linking

Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversational agents such as Alexa in healthcare settings, medical name inference has become an important task. Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list. This is challenging due to the differences in the use of medical terminology from health care professionals and user conversations coming from the lay public. We begin with mapping descriptive medication phrases (DMP) to standard medication names (SMN). Given the prescriptions of each patient, we want to provide them with the flexibility of referring to the medication in their preferred ways. We approach this as a ranking problem which maps SMN to DMP by ordering the list of medications in the patient's prescription list obtained from pharmacies. Furthermore, we leveraged the output of intermediate layers and performed medication clustering. We present the Medication Inference Model (MIM) achieving state-of-the-art results. By incorporating medical entities based attention, we have obtained further improvement for ranking models.
WGANSing
Authors: Pritish Chandna, Merlijn Blaauw
Date: 2019-03-01 00:00:00
Proceeding: Interspeech 2019 3
Tasks: Acoustic Modelling

We present a deep neural network based singing
voice synthesizer, inspired by the Deep Convolutions Generative
Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters
for acoustic modelling, to separate the influence of pitch and
timbre. This facilitates the modelling of the large variability
of pitch in the singing voice. Our network takes a block of
consecutive frame-wise linguistic and fundamental frequency
features, along with global singer identity as input and outputs
vocoder features, corresponding to the block of features. This
block-wise approach, along with the training methodology allows
us to model temporal dependencies within the features of the
input block. For inference, sequential blocks are concatenated
using an overlap-add procedure. We show that the performance
of our model is competitive with regards to the state-of-the-art
and the original sample using objective metrics and a subjective
listening test. We also present examples of the synthesis on a
supplementary website and the source code via GitHub.
Deep xVA solver -- A neural network based counterparty credit risk management framework
Authors: Alessandro Gnoatto, Athena Picarelli, Christoph Reisinger
Date: 2020-05-06 00:00:00
Tasks: Management

In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.
A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study
Authors: Yusuf Sermet, Ibrahim Demir
Date: 2020-07-01 00:00:00
Tasks: Natural Language Understanding

COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data utilization. The presented framework utilizes advanced web technologies to ensure reusability and reliability, and an inference engine for natural language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework's usage and benefits.
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
Authors: Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong
Date: 2020-07-02 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Semantic Segmentation

Recent advances of network architecture for point cloud processing are mainly driven by new designs of local aggregation operators. However, the impact of these operators to network performance is not carefully investigated due to different overall network architecture and implementation details in each solution. Meanwhile, most of operators are only applied in shallow architectures. In this paper, we revisit the representative local aggregation operators and study their performance using the same deep residual architecture. Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks. This finding stimulate us to rethink the necessity of sophisticated design of local aggregation operator for point cloud processing. To this end, we propose a simple local aggregation operator without learnable weights, named Position Pooling (PosPool), which performs similarly or slightly better than existing sophisticated operators. In particular, a simple deep residual network with PosPool layers achieves outstanding performance on all benchmarks, which outperforms the previous state-of-the methods on the challenging PartNet datasets by a large margin (7.4 mIoU). The code is publicly available at https://github.com/zeliu98/CloserLook3D
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Authors: Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
Date: 2020-07-02 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: All

Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i.e., all unlabeled examples are equally weighted. But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights -- as done in prior work -- is no longer possible. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. To make the approach efficient, we propose a fast and effective approximation of the influence function. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks.
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Authors: Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar
Date: 2020-07-02 00:00:00

Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation. We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using the stochastic gradient descent. We consider both 2-layer and multi-layer NNs with ReLU activation functions and prove global convergence in an overparametrized regime, where the number of neurons is diverging. The results are established using techniques from online learning and local linearization of NNs, and improve in several aspects the current state-of-the-art. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
Image Shape Manipulation from a Single Augmented Training Sample
Authors: Yael Vinker, Eliahu Horwitz, Nir Zabari, Yedid Hoshen
Date: 2020-07-02 00:00:00
Tasks: Image Generation, Image Manipulation, Image-to-Image Translation, Sketch-to-Image Translation

In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single image training, and incorporate the use of thin-plate-spline (TPS) as an effective augmentation. Our network learns to map between a primitive representation of the image to the image itself. The choice of a primitive representation has an impact on the ease and expressiveness of the manipulations and can be automatic (e.g. edges), manual (e.g. segmentation) or hybrid such as edges on top of segmentations. At manipulation time, our generator allows for making complex image changes by modifying the primitive input representation and mapping it through the network. Our method is shown to achieve remarkable performance on image manipulation tasks.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Authors: Marjane Khodatars, Afshin Shoeibi, Delaram Sadeghi, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Ali Khadem, Roohallah Alizadehsani, Assef Zare, Yinan Kong, Abbas Khosravi, Saeid Nahavandi, Sadiq Hussain, U. Rajendra Acharya, Michael Berk
Date: 2020-07-02 00:00:00
Tasks: Anatomy, Functional Connectivity, General Classification, Management

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Authors: Gautier Izacard, Edouard Grave
Date: 2020-07-02 00:00:00
Proceeding: EACL 2021 2
Tasks: Natural Questions, Open-Domain Question Answering, Passage Retrieval, Question Answering, Retrieval, TriviaQA

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.
Efficient estimation of the ANOVA mean dimension, with an application to neural net classification
Authors: Christopher Hoyt, Art B. Owen
Date: 2020-07-02 00:00:00
Tasks: General Classification

The mean dimension of a black box function of $d$ variables is a convenient way to summarize the extent to which it is dominated by high or low order interactions. It is expressed in terms of $2^d-1$ variance components but it can be written as the sum of $d$ Sobol' indices that can be estimated by leave one out methods. We compare the variance of these leave one out methods: a Gibbs sampler called winding stairs, a radial sampler that changes each variable one at a time from a baseline, and a naive sampler that never reuses function evaluations and so costs about double the other methods. For an additive function the radial and winding stairs are most efficient. For a multiplicative function the naive method can easily be most efficient if the factors have high kurtosis. As an illustration we consider the mean dimension of a neural network classifier of digits from the MNIST data set. The classifier is a function of $784$ pixels. For that problem, winding stairs is the best algorithm. We find that inputs to the final softmax layer have mean dimensions ranging from $1.35$ to $2.0$.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
Authors: Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, Fahime Khozeimeh, Assef Zare, Hossein Hosseini-Nejad, Abbas Khosravi, Amir F. Atiya, Diba Aminshahidi, Sadiq Hussain, Modjtaba Rouhani, Saeid Nahavandi, Udyavara Rajendra Acharya
Date: 2020-07-02 00:00:00
Tasks: Cloud Computing, Deep Learning, EEG, Electroencephalogram (EEG), Seizure Detection

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Authors: Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, Andrea Vedaldi
Date: 2020-07-02 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Object, Scene Generation

We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accounts for correlations between individual objects. This allows the model to generate realistic scenes and videos from a physically-interpretable parameterization. Furthermore, we show that modeling the object correlation is necessary to learn to disentangle object positions and identity. We find that RELATE is also amenable to physically realistic scene editing and that it significantly outperforms prior art in object-centric scene generation in both synthetic (CLEVR, ShapeStacks) and real-world data (cars). In addition, in contrast to state-of-the-art methods in object-centric generative modeling, RELATE also extends naturally to dynamic scenes and generates videos of high visual fidelity. Source code, datasets and more results are available at http://geometry.cs.ucl.ac.uk/projects/2020/relate/.
Outlier Detection through Null Space Analysis of Neural Networks
Authors: Matthew Cook, Alina Zare, Paul Gader
Date: 2020-07-02 00:00:00
Tasks: Classification, General Classification, Outlier Detection

Many machine learning classification systems lack competency awareness. Specifically, many systems lack the ability to identify when outliers (e.g., samples that are distinct from and not represented in the training data distribution) are being presented to the system. The ability to detect outliers is of practical significance since it can help the system behave in an reasonable way when encountering unexpected data. In prior work, outlier detection is commonly carried out in a processing pipeline that is distinct from the classification model. Thus, for a complete system that incorporates outlier detection and classification, two models must be trained, increasing the overall complexity of the approach. In this paper we use the concept of the null space to integrate an outlier detection method directly into a neural network used for classification. Our method, called Null Space Analysis (NuSA) of neural networks, works by computing and controlling the magnitude of the null space projection as data is passed through a network. Using these projections, we can then calculate a score that can differentiate between normal and abnormal data. Results are shown that indicate networks trained with NuSA retain their classification performance while also being able to detect outliers at rates similar to commonly used outlier detection algorithms.
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
Authors: Luyu Yang, Yogesh Balaji, Ser-Nam Lim, Abhinav Shrivastava
Date: 2020-07-02 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Multi-Source Unsupervised Domain Adaptation, Unsupervised Domain Adaptation

The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS). The Curriculum Manager, an independent network module, constantly updates the curriculum during training, and iteratively learns which domains or samples are best suited for aligning to the target. The intuition behind this is to force the Curriculum Manager to constantly re-measure the transferability of latent domains over time to adversarially raise the error rate of the domain discriminator. CMSS does not require any knowledge of the domain labels, yet it outperforms other methods on four well-known benchmarks by significant margins. We also provide interpretable results that shed light on the proposed method.