pwc-archive/papers-with-abstracts huggingface dataset:
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
Authors: Tianchu Guo, Yongchao Liu, HUI ZHANG, Xiabing Liu, Youngjun Kwak, Byung In Yoo, Jae-Joon Han, Changkyu Choi
Date: 2019-10-16 00:00:00
Tasks: Gaze Estimation, Knowledge Distillation

Gaze estimation for ordinary smart phone, e.g. estimating where the user is looking at on the phone screen, can be applied in various applications. However, the widely used appearance-based CNN methods still have two issues for practical adoption. First, due to the limited dataset, gaze estimation is very likely to suffer from over-fitting, leading to poor accuracy at run time. Second, the current methods are usually not robust, i.e. their prediction results having notable jitters even when the user is performing gaze fixation, which degrades user experience greatly. For the first issue, we propose a new tolerant and talented (TAT) training scheme, which is an iterative random knowledge distillation framework enhanced with cosine similarity pruning and aligned orthogonal initialization. The knowledge distillation is a tolerant teaching process providing diverse and informative supervision. The enhanced pruning and initialization is a talented learning process prompting the network to escape from the local minima and re-born from a better start. For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it. The experimental results show that our TAT method achieves state-of-the-art performance on GazeCapture dataset, and that our DwO method improves the robustness while keeping comparable accuracy.
Segmentation Criteria in the Problem of Porosity Determination based on CT Scans
Authors: V. Kokhan, M. Grigoriev, A. Buzmakov, V. Uvarov, A. Ingacheva, E. Shvets, M. Chukalina
Date: 2019-10-16 00:00:00
Tasks: Computed Tomography (CT)

Porous materials are widely used in different applications, in particular they are used to create various filters. Their quality depends on parameters that characterize the internal structure such as porosity, permeability and so on. Computed tomography (CT) allows one to see the internal structure of a porous object without destroying it. The result of tomography is a gray image. To evaluate the desired parameters, the image should be segmented. Traditional intensity threshold approaches did not reliably produce correct results due to limitations with CT images quality. Errors in the evaluation of characteristics of porous materials based on segmented images can lead to the incorrect estimation of their quality and consequently to the impossibility of exploitation, financial losses and even to accidents. It is difficult to perform correctly segmentation due to the strong difference in voxel intensities of the reconstructed object and the presence of noise. Image filtering as a preprocessing procedure is used to improve the quality of segmentation. Nevertheless, there is a problem of choosing an optimal filter. In this work, a method for selecting an optimal filter based on attributive indicator of porous objects (should be free from 'levitating stones' inside of pores) is proposed. In this paper, we use real data where beam hardening artifacts are removed, which allows us to focus on the noise reduction process
Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules
Authors: Florian Ziel, Kevin Berk
Date: 2019-10-16 00:00:00
Tasks: scoring rule

In recent years, probabilistic forecasting is an emerging topic, which is why there is a growing need of suitable methods for the evaluation of multivariate predictions. We analyze the sensitivity of the most common scoring rules, especially regarding quality of the forecasted dependency structures. Additionally, we propose scoring rules based on the copula, which uniquely describes the dependency structure for every probability distribution with continuous marginal distributions. Efficient estimation of the considered scoring rules and evaluation methods such as the Diebold-Mariano test are discussed. In detailed simulation studies, we compare the performance of the renowned scoring rules and the ones we propose. Besides extended synthetic studies based on recently published results we also consider a real data example. We find that the energy score, which is probably the most widely used multivariate scoring rule, performs comparably well in detecting forecast errors, also regarding dependencies. This contradicts other studies. The results also show that a proposed copula score provides very strong distinction between models with correct and incorrect dependency structure. We close with a comprehensive discussion on the proposed methodology.
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition
Authors: Adrien Dufraux, Emmanuel Vincent, Awni Hannun, Armelle Brun, Matthijs Douze
Date: 2019-10-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise.
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Authors: Yuta Saito, Masahiro Nomura
Date: 2019-10-16 00:00:00
Tasks: Domain Adaptation, Selection bias, Unsupervised Domain Adaptation

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
Authors: Ozsel Kilinc, Giovanni Montana
Date: 2019-10-16 00:00:00
Tasks: Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Manipulation

Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrated that using sparse rewards, i.e. rewarding the agent only when the task has been successfully completed, can lead to better policies. However, state-action space exploration is more difficult in this case. Recent RL approaches to learning with sparse rewards have leveraged high-quality human demonstrations for the task, but these can be costly, time consuming or even impossible to obtain. In this paper, we propose a novel and effective approach that does not require human demonstrations. We observe that every robotic manipulation task could be seen as involving a locomotion task from the perspective of the object being manipulated, i.e. the object could learn how to reach a target state on its own. In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator. This policy is then used to generate auxiliary rewards, called simulated locomotion demonstration rewards (SLDRs), which enable us to learn the robot manipulation policy. The proposed approach has been evaluated on 13 tasks of increasing complexity, and can achieve higher success rate and faster learning rates compared to alternative algorithms. SLDRs are especially beneficial for tasks like multi-object stacking and non-rigid object manipulation.
Newton vs the machine: solving the chaotic three-body problem using deep neural networks
Authors: Philip G. Breen, Christopher N. Foley, Tjarda Boekholt, Simon Portegies Zwart
Date: 2019-10-16 00:00:00

Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system's chaotic nature. We show that an ensemble of solutions obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at fixed computational cost and up to 100 million times faster than a state-of-the-art solver. Our results provide evidence that, for computationally challenging regions of phase-space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters.
FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance
Authors: Matteo Dell'Amico
Date: 2019-10-16 00:00:00
Tasks: Clustering

FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It is incremental and scalable: it avoids the $\mathcal O(n^2)$ performance of other approaches in non-metric spaces and requires only lightweight computation to update the clustering when few items are added. It is hierarchical: it produces a "flat" clustering which can be expanded to a tree structure, so that users can group and/or divide clusters in sub- or super-clusters when data exploration requires so. It is density-based and approximates HDBSCAN*, an evolution of DBSCAN.
On the Relation between Weak Completion Semantics and Answer Set Semantics
Authors: Emmanuelle-Anna Dietz Saldanha, Jorge Fandinno
Date: 2019-10-16 00:00:00
Tasks: Relation, Translation

The Weak Completion Semantics (WCS) is a computational cognitive theory that has shown to be successful in modeling episodes of human reasoning. As the WCS is a recently developed logic programming approach, this paper investigates the correspondence of the WCS with respect to the well-established Answer Set Semantics (ASP). The underlying three-valued logic of both semantics is different and their models are evaluated with respect to different program transformations. We first illustrate these differences by the formal representation of some examples of a well-known psychological experiment, the suppression task. After that, we will provide a translation from logic programs understood under the WCS into logic programs understood under the ASP. In particular, we will show that logic programs under the WCS can be represented as logic programs under the ASP by means of a definition completion, where all defined atoms in a program must be false when their definitions are false.
Exploring the fitness landscape of a realistic turbofan rotor blade optimization
Authors: Jakub Kmec, Sebastian Schmitt
Date: 2019-10-16 00:00:00

Aerodynamic shape optimization has established itself as a valuable tool in the engineering design process to achieve highly efficient results. A central aspect for such approaches is the mapping from the design parameters which encode the geometry of the shape to be improved to the quality criteria which describe its performance. The choices to be made in the setup of the optimization process strongly influence this mapping and thus are expected to have a profound influence on the achievable result. In this work we explore the influence of such choices on the effects on the shape optimization of a turbofan rotor blade as it can be realized within an aircraft engine design process. The blade quality is assessed by realistic three dimensional computational fluid dynamics (CFD) simulations. We investigate the outcomes of several optimization runs which differ in various configuration options, such as optimization algorithm, initialization, number of degrees of freedom for the parametrization. For all such variations, we generally find that the achievable improvement of the blade quality is comparable for most settings and thus rather insensitive to the details of the setup. On the other hand, even supposedly minor changes in the settings, such as using a different random seed for the initialization of the optimizer algorithm, lead to very different shapes. Optimized shapes which show comparable performance usually differ quite strongly in their geometries over the complete blade. Our analyses indicate that the fitness landscape for such a realistic turbofan rotor blade optimization is highly multi-modal with many local optima, where very different shapes show similar performance.
Optimising Individual-Treatment-Effect Using Bandits
Authors: Jeroen Berrevoets, Sam Verboven, Wouter Verbeke
Date: 2019-10-16 00:00:00
Tasks: Causal Inference, Marketing

Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the individual-treatment-effect (ITE) in settings such as precision medicine and targeted advertising has peaked in application. Optimising this ITE under the strong-ignorability-assumption -- meaning all confounders expressing influence on the outcome of a treatment are registered in the data -- is often referred to as uplift modeling (UM). While these techniques have proven useful in many settings, they suffer vividly in a dynamic environment due to concept drift. Take for example the negative influence on a marketing campaign when a competitor product is released. To counter this, we propose the uplifted contextual multi-armed bandit (U-CMAB), a novel approach to optimise the ITE by drawing upon bandit literature. Experiments on real and simulated data indicate that our proposed approach compares favourably against the state-of-the-art. All our code can be found online at https://github.com/vub-dl/u-cmab.
Audio-Conditioned U-Net for Position Estimation in Full Sheet Images
Authors: Florian Henkel, Rainer Kelz, Gerhard Widmer
Date: 2019-10-16 00:00:00
Tasks: Multimodal Deep Learning, Position

The goal of score following is to track a musical performance, usually in the form of audio, in a corresponding score representation. Established methods mainly rely on computer-readable scores in the form of MIDI or MusicXML and achieve robust and reliable tracking results. Recently, multimodal deep learning methods have been used to follow along musical performances in raw sheet images. Among the current limits of these systems is that they require a non trivial amount of preprocessing steps that unravel the raw sheet image into a single long system of staves. The current work is an attempt at removing this particular limitation. We propose an architecture capable of estimating matching score positions directly within entire unprocessed sheet images. We argue that this is a necessary first step towards a fully integrated score following system that does not rely on any preprocessing steps such as optical music recognition.
A Notion of Harmonic Clustering in Simplicial Complexes
Authors: Stefania Ebli, Gard Spreemann
Date: 2019-10-16 00:00:00
Tasks: Clustering, Topological Data Analysis

We outline a novel clustering scheme for simplicial complexes that produces clusters of simplices in a way that is sensitive to the homology of the complex. The method is inspired by, and can be seen as a higher-dimensional version of, graph spectral clustering. The algorithm involves only sparse eigenproblems, and is therefore computationally efficient. We believe that it has broad application as a way to extract features from simplicial complexes that often arise in topological data analysis.
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
Authors: Nikolay Jetchev, Urs Bergmann, Gökhan Yildirim
Date: 2019-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Image Generation

Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material. Formally, this process takes an image set as input and outputs a collage of the set elements. Such selection from sets of source templates does not fit easily in classical convolutional neural models requiring inputs of fixed size. Inspired by advances in attention and set-input machine learning, we present a novel architecture that can generate in one forward pass image collages of source templates using set-structured representations. This paper has the following contributions: (i) a novel framework for image generation called Memory Attentive Generation of Image Collages (MAGIC) which gives artists new ways to create digital collages; (ii) from the machine-learning perspective, we show a novel Generative Adversarial Networks (GAN) architecture that uses Set-Transformer layers and set-pooling to blend sets of random image samples - a hybrid non-parametric approach.
Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3
Authors: Adel Ammar, Anis Koubaa, Mohanned Ahmed, Abdulrahman Saad, Bilel Benjdira
Date: 2019-10-16 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV's altitude, camera resolution, and object size. A total of 39 training experiments were conducted to account for the effect of different hyperparameter values. The objective of this work is to conduct the most robust and exhaustive comparison between these two cutting-edge algorithms on the specific domain of aerial images. By using a variety of metrics, we show that YOLOv3 yields better performance in most configurations, except that it exhibits a lower recall and less confident detections when object sizes and scales in the testing dataset differ largely from those in the training dataset.
Rule based Approach for Word Normalization by resolving Transcription Ambiguity in Transliterated Search Queries
Authors: Varsha Pathak, Manish Joshi
Date: 2019-10-16 00:00:00
Tasks: Information Retrieval, Retrieval, Transliteration

Query term matching with document term matching is the basic function of any best effort Information Retrieval models like Vector Space Model. In our problem of SMS based Information Systems we expect common people to participate in information search. Our system allows mobile users to formulate their queries in their own words, own transliteration style and spelling formation. To achieve this flexibility we have resolved the term level ambiguity due to inherent transcription noise in user query terms. We have developed a rule based approach to select most relevantly close standard term for each noisy term in the user query. We have used four different versions of the rule based algorithm with variation in the rule set. We have formulated this rule set including the basic Levenshtein minimum edit distance algorithm for term matching. This paper presents the experiments and corresponding results of Marathi and Hindi language literature information system. We have experimented on Marathi and Hindi literature which include songs, gazals, powadas, bharud and other types in a standard transliteration form like ITRANS.
Structural Analysis of Sparse Neural Networks
Authors: Julian Stier, Michael Granitzer
Date: 2019-10-16 00:00:00
Tasks: Neural Architecture Search

Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages. We give motivation to study Artificial Neural Networks (ANNs) from a network science perspective, provide a technique to embed arbitrary Directed Acyclic Graphs into ANNs and report study results on predicting the performance of image classifiers based on the structural properties of the networks' underlying graph. Results could further progress neuroevolution and add explanations for the success of distinct architectures from a structural perspective.
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Authors: Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-Yves Oudeyer
Date: 2019-10-16 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments. Because it does not initially know the capacities of its student, a key challenge for the teacher is to discover which environments are easy, difficult or unlearnable, and in what order to propose them to maximize the efficiency of learning over the learnable ones. To achieve this, this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student. We present a new algorithm modeling absolute learning progress with Gaussian mixture models (ALP-GMM). We also adapt existing algorithms and provide a complete study in the context of DRL. Using parameterized variants of the BipedalWalker environment, we study their efficiency to personalize a learning curriculum for different learners (embodiments), their robustness to the ratio of learnable/unlearnable environments, and their scalability to non-linear and high-dimensional parameter spaces. Videos and code are available at https://github.com/flowersteam/teachDeepRL.