pwc-archive/papers-with-abstracts huggingface dataset:
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning
Authors: Zixin Wen
Date: 2020-02-17 00:00:00
Tasks: Contrastive Learning

Unsupervised contrastive learning has gained increasing attention in the latest research and has proven to be a powerful method for learning representations from unlabeled data. However, little theoretical analysis was known for this framework. In this paper, we study the optimization of deep unsupervised contrastive learning. We prove that, by applying end-to-end training that simultaneously updates two deep over-parameterized neural networks, one can find an approximate stationary solution for the non-convex contrastive loss. This result is inherently different from the existing over-parameterized analysis in the supervised setting because, in contrast to learning a specific target function, unsupervised contrastive learning tries to encode the unlabeled data distribution into the neural networks, which generally has no optimal solution. Our analysis provides theoretical insights into the practical success of these unsupervised pretraining methods.
Investigating the Compositional Structure Of Deep Neural Networks
Authors: Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi, Fabio Stella, Marco Antoniotti
Date: 2020-02-17 00:00:00

The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the strong generalization power that is typically observed in many real-world applications. In order to improve the comprehension and interpretability of deep neural networks, we here introduce a novel theoretical framework based on the compositional structure of piecewise linear activation functions. By defining a direct acyclic graph representing the composition of activation patterns through the network layers, it is possible to characterize the instances of the input data with respect to both the predicted label and the specific (linear) transformation used to perform predictions. Preliminary tests on the MNIST dataset show that our method can group input instances with regard to their similarity in the internal representation of the neural network, providing an intuitive measure of input complexity.
Learning Architectures for Binary Networks
Authors: Dahyun Kim, Kunal Pratap Singh, Jonghyun Choi
Date: 2020-02-17 00:00:00
Proceeding: ECCV 2020 8
Tasks: Quantization

Backbone architectures of most binary networks are well-known floating point architectures such as the ResNet family. Questioning that the architectures designed for floating point networks would not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective. Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder. The novel search objective diversifies early search to learn better performing binary architectures. We show that our proposed method searches architectures with stable training curves despite the quantization error inherent in binary networks. Quantitative analyses demonstrate that our searched architectures outperform the architectures used in state-of-the-art binary networks and outperform or perform on par with state-of-the-art binary networks that employ various techniques other than architectural changes.
Citation Recommendation: Approaches and Datasets
Authors: Michael Färber, Adam Jatowt
Date: 2020-02-17 00:00:00
Tasks: Articles, Citation Recommendation, Survey

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction into automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods, and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.
Computing rank-revealing factorizations of matrices stored out-of-core
Authors: Nathan Heavner, Per-Gunnar Martinsson, Gregorio Quintana-Ortí
Date: 2020-02-17 00:00:00

This paper describes efficient algorithms for computing rank-revealing factorizations of matrices that are too large to fit in RAM, and must instead be stored on slow external memory devices such as solid-state or spinning disk hard drives (out-of-core or out-of-memory). Traditional algorithms for computing rank revealing factorizations, such as the column pivoted QR factorization, or techniques for computing a full singular value decomposition of a matrix, are very communication intensive. They are naturally expressed as a sequence of matrix-vector operations, which become prohibitively expensive when data is not available in main memory. Randomization allows these methods to be reformulated so that large contiguous blocks of the matrix can be processed in bulk. The paper describes two distinct methods. The first is a blocked version of column pivoted Householder QR, organized as a "left-looking" method to minimize the number of write operations (which are more expensive than read operations on a spinning disk drive). The second method results in a so called UTV factorization which expresses a matrix $A$ as $A = U T V^*$ where $U$ and $V$ are unitary, and $T$ is triangular. This method is organized as an algorithm-by-blocks, in which floating point operations overlap read and write operations. The second method incorporates power iterations, and is exceptionally good at revealing the numerical rank; it can often be used as a substitute for a full singular value decomposition. Numerical experiments demonstrate that the new algorithms are almost as fast when processing data stored on a hard drive as traditional algorithms are for data stored in main memory. To be precise, the computational time for fully factorizing an $n\times n$ matrix scales as $cn^{3}$, with a scaling constant $c$ that is only marginally larger when the matrix is stored out of core.
Adaptive Experience Selection for Policy Gradient
Authors: Saad Mohamad, Giovanni Montana
Date: 2020-02-17 00:00:00
Tasks: continuous-control, Continuous Control, OpenAI Gym, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient reinforcement learning (RL) algorithms have achieved impressive performance in challenging learning tasks such as continuous control, but suffer from high sample complexity. Experience replay is a commonly used approach to improve sample efficiency, but gradient estimators using past trajectories typically have high variance. Existing sampling strategies for experience replay like uniform sampling or prioritised experience replay do not explicitly try to control the variance of the gradient estimates. In this paper, we propose an online learning algorithm, adaptive experience selection (AES), to adaptively learn an experience sampling distribution that explicitly minimises this variance. Using a regret minimisation approach, AES iteratively updates the experience sampling distribution to match the performance of a competitor distribution assumed to have optimal variance. Sample non-stationarity is addressed by proposing a dynamic (i.e. time changing) competitor distribution for which a closed-form solution is proposed. We demonstrate that AES is a low-regret algorithm with reasonable sample complexity. Empirically, AES has been implemented for deep deterministic policy gradient and soft actor critic algorithms, and tested on 8 continuous control tasks from the OpenAI Gym library. Ours results show that AES leads to significantly improved performance compared to currently available experience sampling strategies for policy gradient.
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT
Authors: Mohamed S. Elmahdy, Tanuj Ahuja, U. A. van der Heide, Marius Staring
Date: 2020-02-17 00:00:00
Tasks: Transfer Learning

Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging. In this work, we leverage personalized anatomical knowledge accumulated over the treatment sessions, to improve the segmentation accuracy of a pre-trained Convolution Neural Network (CNN), for a specific patient. We investigate a transfer learning approach, fine-tuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions. The baseline CNN model is trained on a prostate CT dataset from one hospital of 379 patients. This model is then fine-tuned and tested on an independent dataset of another hospital of 18 patients, each having 7 to 10 daily CT scans. For the prostate, seminal vesicles, bladder and rectum, the model fine-tuned on each specific patient achieved a Mean Surface Distance (MSD) of $1.64 \pm 0.43$ mm, $2.38 \pm 2.76$ mm, $2.30 \pm 0.96$ mm, and $1.24 \pm 0.89$ mm, respectively, which was significantly better than the baseline model. The proposed personalized model adaptation is therefore very promising for clinical implementation in the context of adaptive radiotherapy of prostate cancer.
Serial Speakers: a Dataset of TV Series
Authors: Xavier Bost, Vincent Labatut, Georges Linares
Date: 2020-02-17 00:00:00
Proceeding: LREC 2020 5
Tasks: Retrieval

For over a decade, TV series have been drawing increasing interest, both from the audience and from various academic fields. But while most viewers are hooked on the continuous plots of TV serials, the few annotated datasets available to researchers focus on standalone episodes of classical TV series. We aim at filling this gap by providing the multimedia/speech processing communities with Serial Speakers, an annotated dataset of 161 episodes from three popular American TV serials: Breaking Bad, Game of Thrones and House of Cards. Serial Speakers is suitable both for investigating multimedia retrieval in realistic use case scenarios, and for addressing lower level speech related tasks in especially challenging conditions. We publicly release annotations for every speech turn (boundaries, speaker) and scene boundary, along with annotations for shot boundaries, recurring shots, and interacting speakers in a subset of episodes. Because of copyright restrictions, the textual content of the speech turns is encrypted in the public version of the dataset, but we provide the users with a simple online tool to recover the plain text from their own subtitle files.
Semantics of negative sequential patterns
Authors: Thomas Guyet, Philippe Besnard
Date: 2020-02-17 00:00:00

In the field of pattern mining, a negative sequential pattern is specified by means of a sequence consisting of events to occur and of other events, called negative events, to be absent. For instance, containment of the pattern $\langle a\ \neg b\ c\rangle$ arises with an occurrence of a and a subsequent occurrence of c but no occurrence of b in between. This article is to shed light on the ambiguity of such a seemingly intuitive notation and we identify eight possible semantics for the containment relation between a pattern and a sequence. These semantics are illustrated and formally studied, in particular we propose dominance and equivalence relations between them. Also we prove that support is anti-monotonic for some of these semantics. Some of the results are discussed with the aim of developing algorithms to extract efficiently frequent negative patterns.
Implementing Dynamic Answer Set Programming
Authors: Pedro Cabalar, Martín Diéguez, Torsten Schaub, François Laferrière
Date: 2020-02-17 00:00:00
Tasks: Translation

We introduce an implementation of an extension of Answer Set Programming (ASP) with language constructs from dynamic (and temporal) logic that provides an expressive computational framework for modeling dynamic applications. Starting from logical foundations, provided by dynamic and temporal equilibrium logics over finite linear traces, we develop a translation of dynamic formulas into temporal logic programs. This provides us with a normal form result establishing the strong equivalence of formulas in different logics. Our translation relies on the introduction of auxiliary atoms to guarantee polynomial space complexity and to provide an embedding that is doomed to be impossible over the same language. Finally, the reduction of dynamic formulas to temporal logic programs allows us to extend ASP with both approaches in a uniform way and to implement both extensions via temporal ASP solvers such as telingo
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
Authors: Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp
Date: 2020-02-17 00:00:00
Tasks: Entity Alignment, Informativeness, Knowledge Graphs, Link Prediction

In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple different scores are employed to assess different aspects of model performance. We analyze the informativeness of these evaluation measures and identify several shortcomings. In particular, we demonstrate that all existing scores can hardly be used to compare results across different datasets. Moreover, we demonstrate that varying size of the test size automatically has impact on the performance of the same model based on commonly used metrics for the Entity Alignment task. We show that this leads to various problems in the interpretation of results, which may support misleading conclusions. Therefore, we propose adjustments to the evaluation and demonstrate empirically how this supports a fair, comparable, and interpretable assessment of model performance. Our code is available at https://github.com/mberr/rank-based-evaluation.
An ASP semantics for Constraints involving Conditional Aggregates
Authors: Pedro Cabalar, Jorge Fandinno, Torsten Schaub, Philipp Wanko
Date: 2020-02-17 00:00:00
Tasks: Translation

We elaborate upon the formal foundations of hybrid Answer Set Programming (ASP) and extend its underlying logical framework with aggregate functions over constraint values and variables. This is achieved by introducing the construct of conditional expressions, which allow for considering two alternatives while evaluating constraints. Which alternative is considered is interpretation-dependent and chosen according to an associated condition. We put some emphasis on logic programs with linear constraints and show how common ASP aggregates can be regarded as particular cases of so-called conditional linear constraints. Finally, we introduce a polynomial-size, modular and faithful translation from our framework into regular (condition-free) Constraint ASP, outlining an implementation of conditional aggregates on top of existing hybrid ASP solvers.
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
Authors: Angelos Chatzimparmpas, Rafael M. Martins, Andreas Kerren
Date: 2020-02-17 00:00:00
Tasks: Dimensionality Reduction

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
Amplifying The Uncanny
Authors: Terence Broad, Frederic Fol Leymarie, Mick Grierson
Date: 2020-02-17 00:00:00

Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images. Deepfakes are produced by algorithms that learn to distinguish between real and fake images and are optimised to generate samples that the system deems realistic. This paper, and the resulting series of artworks Being Foiled explore the aesthetic outcome of inverting this process, instead optimising the system to generate images that it predicts as being fake. This maximises the unlikelihood of the data and in turn, amplifies the uncanny nature of these machine hallucinations.
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
Authors: Swapnil Mishra, Seth Flaxman, Tresnia Berah, Harrison Zhu, Mikko Pakkanen, Samir Bhatt
Date: 2020-02-17 00:00:00
Tasks: Bayesian Inference, Computational Efficiency, Gaussian Processes, Probabilistic Programming, Spatial Interpolation

Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. In practice, however, efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder ($\pi$VAE). The $\pi$VAE is finitely exchangeable and Kolmogorov consistent, and thus is a continuous stochastic process. We use $\pi$VAE to learn low dimensional embeddings of function classes. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process). For popular tasks, such as spatial interpolation, $\pi$VAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate that the low dimensional independently distributed latent space representation learnt provides an elegant and scalable means of performing Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.
Scalable Quantitative Verification For Deep Neural Networks
Authors: Teodora Baluta, Zheng Leong Chua, Kuldeep S. Meel, Prateek Saxena
Date: 2020-02-17 00:00:00
Tasks: Adversarial Robustness

Despite the functional success of deep neural networks (DNNs), their trustworthiness remains a crucial open challenge. To address this challenge, both testing and verification techniques have been proposed. But these existing techniques provide either scalability to large networks or formal guarantees, not both. In this paper, we propose a scalable quantitative verification framework for deep neural networks, i.e., a test-driven approach that comes with formal guarantees that a desired probabilistic property is satisfied. Our technique performs enough tests until soundness of a formal probabilistic property can be proven. It can be used to certify properties of both deterministic and randomized DNNs. We implement our approach in a tool called PROVERO and apply it in the context of certifying adversarial robustness of DNNs. In this context, we first show a new attack-agnostic measure of robustness which offers an alternative to purely attack-based methodology of evaluating robustness being reported today. Second, PROVERO provides certificates of robustness for large DNNs, where existing state-of-the-art verification tools fail to produce conclusive results. Our work paves the way forward for verifying properties of distributions captured by real-world deep neural networks, with provable guarantees, even where testers only have black-box access to the neural network.
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
Authors: Taro Langner, Robin Strand, Håkan Ahlström, Joel Kullberg
Date: 2020-02-17 00:00:00
Tasks: Anatomy, regression

In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R^2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.