pwc-archive/papers-with-abstracts huggingface dataset:
Mapper Based Classifier
Authors: Jacek Cyranka, Alexander Georges, David Meyer
Date: 2019-10-17 00:00:00
Tasks: Clustering, Topological Data Analysis

Topological data analysis aims to extract topological quantities from data, which tend to focus on the broader global structure of the data rather than local information. The Mapper method, specifically, generalizes clustering methods to identify significant global mathematical structures, which are out of reach of many other approaches. We propose a classifier based on applying the Mapper algorithm to data projected onto a latent space. We obtain the latent space by using PCA or autoencoders. Notably, a classifier based on the Mapper method is immune to any gradient based attack, and improves robustness over traditional CNNs (convolutional neural networks). We report theoretical justification and some numerical experiments that confirm our claims.
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Authors: Jiacheng Zhu, Shenghao Qin, Wenshuo Wang, Ding Zhao
Date: 2019-10-17 00:00:00
Tasks: Autonomous Vehicles, Meta-Learning, Trajectory Prediction

Predicting surrounding vehicle behaviors are critical to autonomous vehicles when negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious training process with large amounts of data and may fail to capture the propagating uncertainty in interaction behaviors. The multi-vehicle behaviors are assumed to be generated from a stochastic process. This paper proposes an attentive recurrent neural process (ARNP) approach to overcome the above limitations, which uses a neural process (NP) to learn a distribution of multi-vehicle interaction behavior. Our proposed model inherits the flexibility of neural networks while maintaining Bayesian probabilistic characteristics. Constructed by incorporating NPs with recurrent neural networks (RNNs), the ARNP model predicts the distribution of a target vehicle trajectory conditioned on the observed long-term sequential data of all surrounding vehicles. This approach is verified by learning and predicting lane-changing trajectories in complex traffic scenarios. Experimental results demonstrate that our proposed method outperforms previous counterparts in terms of accuracy and uncertainty expressiveness. Moreover, the meta-learning instinct of NPs enables our proposed ARNP model to capture global information of all observations, thereby being able to adapt to new targets efficiently.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Authors: Yura Perov, Logan Graham, Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri
Date: 2019-10-17 00:00:00
Proceeding: pproximateinference AABI Symposium 2019 12
Tasks: counterfactual, Counterfactual Inference, Probabilistic Programming

We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis
Authors: Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero, Cora Dvorkin
Date: 2019-10-17 00:00:00
Tasks: blind source separation

We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to Generalized Morphological Component Analysis (GMCA), we introduce Hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on $N_{\rm side}=256$ simulated sky maps that include dust, synchrotron, free-free and anomalous microwave emission, and show that HGMCA reduces foreground contamination by $25\%$ over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the $0.02-0.03\%$ level at $\ell>200$ (and $<0.26\%$ for all $\ell$), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art Internal Linear Combination (ILC)-type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.
Heterogeneous Graph Matching Networks
Authors: Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Yu
Date: 2019-10-17 00:00:00
Tasks: Graph Matching, Malware Detection

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohibitively high training cost. To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program's execution behaviors. We conduct a systematic evaluation of our model and show that it is accurate in detecting malicious program behavior and can help detect malware attacks with less false positives. MatchGNet outperforms the state-of-the-art algorithms in malware detection by generating 50% less false positives while keeping zero false negatives.
Detecting intracranial aneurysm rupture from 3D surfaces using a novel GraphNet approach
Authors: Z. Ma, L. Song, X. Feng, G. Yang, W. Zhu, J. Liu, Y. Zhang, X. Yang, Y. Yin
Date: 2019-10-17 00:00:00
Tasks: General Classification, Segmentation

Intracranial aneurysm (IA) is a life-threatening blood spot in human's brain if it ruptures and causes cerebral hemorrhage. It is challenging to detect whether an IA has ruptured from medical images. In this paper, we propose a novel graph based neural network named GraphNet to detect IA rupture from 3D surface data. GraphNet is based on graph convolution network (GCN) and is designed for graph-level classification and node-level segmentation. The network uses GCN blocks to extract surface local features and pools to global features. 1250 patient data including 385 ruptured and 865 unruptured IAs were collected from clinic for experiments. The performance on randomly selected 234 test patient data was reported. The experiment with the proposed GraphNet achieved accuracy of 0.82, area-under-curve (AUC) of receiver operating characteristic (ROC) curve 0.82 in the classification task, significantly outperforming the baseline approach without using graph based networks. The segmentation output of the model achieved mean graph-node-based dice coefficient (DSC) score 0.88.
Statistical Parameter Selection for Clustering Persistence Diagrams
Authors: Max Kontak, Jules Vidal, Julien Tierny
Date: 2019-10-17 00:00:00
Tasks: Clustering, Decision Making

In urgent decision making applications, ensemble simulations are an important way to determine different outcome scenarios based on currently available data. In this paper, we will analyze the output of ensemble simulations by considering so-called persistence diagrams, which are reduced representations of the original data, motivated by the extraction of topological features. Based on a recently published progressive algorithm for the clustering of persistence diagrams, we determine the optimal number of clusters, and therefore the number of significantly different outcome scenarios, by the minimization of established statistical score functions. Furthermore, we present a proof-of-concept prototype implementation of the statistical selection of the number of clusters and provide the results of an experimental study, where this implementation has been applied to real-world ensemble data sets.
Deformable Kernel Networks for Joint Image Filtering
Authors: Beomjun Kim, Jean Ponce, Bumsub Ham
Date: 2019-10-17 00:00:00
Tasks: Depth Map Super-Resolution, Image Restoration, Semantic Segmentation

Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing noise. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result. In this paper, we instead learn explicitly sparse and spatially-variant kernels. We propose a CNN architecture and its efficient implementation, called the deformable kernel network (DKN), that outputs sets of neighbors and the corresponding weights adaptively for each pixel. The filtering result is then computed as a weighted average. We also propose a fast version of DKN that runs about seventeen times faster for an image of size 640 x 480. We demonstrate the effectiveness and flexibility of our models on the tasks of depth map upsampling, saliency map upsampling, cross-modality image restoration, texture removal, and semantic segmentation. In particular, we show that the weighted averaging process with sparsely sampled 3 x 3 kernels outperforms the state of the art by a significant margin in all cases.
An Update on Machine Learning in Neuro-oncology Diagnostics
Authors: Thomas Booth
Date: 2019-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Prognosis

Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Following image feature extraction, machine learning allows accurate classification in a variety of scenarios. Machine learning also enables image feature extraction de novo although the low prevalence of brain tumours makes such approaches challenging. Much research is applied to determining molecular profiles, histological tumour grade and prognosis at the time that patients first present with a brain tumour. Following treatment, differentiating a treatment response from a post-treatment related effect is clinically important and also an area of study. Most of the evidence is low level having been obtained retrospectively and in single centres.
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach
Authors: Ricardo Marcondes Marcacini, Rafael Geraldeli Rossi, Ivone Penque Matsuno, Solange Oliveira Rezende
Date: 2018-10-21 00:00:00
Proceeding: Decision support system 2018 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Inductive Learning, Opinion Mining, Sentiment Analysis, Transductive Learning, Transfer Learning

Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze
consumer reviews at a high level of detail, where the opinion about each fea-
ture of the product or service is considered. ABSA usually explores supervised
inductive learning algorithms, which requires intense human effort for the la-
beling process. In this paper, we investigate Cross-Domain Transfer Learning
approaches, in which aspects already labeled in some domains can be used to
support the aspect extraction of another domain where there are no labeled
aspects. Existing cross-domain transfer learning approaches learn classifiers
from labeled aspects in the source domain and then apply these classifiers in
the target domain, i.e., two separate stages that may cause inconsistency due
to different feature spaces. To overcome this drawback, we present an inno-
vative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation
through Heterogeneous Networks). First, we propose a heterogeneous network-
based representation that combines different features (labeled aspects, unlabeled
aspects, and linguistic features) from source and target domain as nodes in a
single network. Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used
as a bridge for this propagation. Our algorithm is based on a transductive
learning process, where we explore both labeled and unlabeled aspects during
the label propagation. Experimental results show that the CD-ALPHN out-
performs the state-of-the-art methods in scenarios where there is a high-level
of inconsistency between the source and target domains — the most common
scenario in real-world applications.
Keywords: Cross Domain, Opinion Mining, Aspect Extraction
Building Dynamic Knowledge Graphs from Text-based Games
Authors: Mikuláš Zelinka, Xingdi Yuan, Marc-Alexandre Côté, Romain Laroche, Adam Trischler
Date: 2019-10-21 00:00:00
Tasks: Knowledge Graphs, text-based games

We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data points). We conduct experiments and discuss the results.
Adaptive Gradient Descent without Descent
Authors: Yura Malitsky, Konstantin Mishchenko
Date: 2019-10-21 00:00:00
Proceeding: ICML 2020 1

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don't increase the stepsize too fast and 2) don't overstep the local curvature. No need for functional values, no line search, no information about the function except for the gradients. By following these rules, you get a method adaptive to the local geometry, with convergence guarantees depending only on the smoothness in a neighborhood of a solution. Given that the problem is convex, our method converges even if the global smoothness constant is infinity. As an illustration, it can minimize arbitrary continuously twice-differentiable convex function. We examine its performance on a range of convex and nonconvex problems, including logistic regression and matrix factorization.
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Authors: Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten
Date: 2019-10-21 00:00:00

We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter. Being able to estimate the marginal likelihood without bias is highly relevant for model comparison, computation of interpretable and reliable confidence intervals, and in exact approximation methods, such as particle Markov chain Monte Carlo. In the paper we present a particle filter with rejection control that enables unbiased estimation of the marginal likelihood.
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures
Authors: Naser Damer, Fadi Boutros, Khawla Mallat, Florian Kirchbuchner, Jean-Luc Dugelay, Arjan Kuijper
Date: 2019-10-21 00:00:00
Tasks: Data Augmentation, Face Alignment, Face Recognition, Vocal Bursts Intensity Prediction

Generating visible-like face images from thermal images is essential to perform manual and automatic cross-spectrum face recognition. We successfully propose a solution based on cascaded refinement network that, unlike previous works, produces high quality generated color images without the need for face alignment, large databases, data augmentation, polarimetric sensors, computationally-intense training, or unrealistic restriction on the generated resolution. The training of our solution is based on the contextual loss, making it inherently scale (face area) and rotation invariant. We present generated image samples of unknown individuals under different poses and occlusion conditions.We also prove the high similarity in image quality between ground-truth images and generated ones by comparing seven quality metrics. We compare our results with two state-of-the-art approaches proving the superiority of our proposed approach.
Comparative Study between Adversarial Networks and Classical Techniques for Speech Enhancement
Authors: Tito Spadini, Ricardo Suyama
Date: 2019-10-21 00:00:00
Tasks: Speech Enhancement

Speech enhancement is a crucial task for several applications. Among the most explored techniques are the Wiener filter and the LogMMSE, but approaches exploring deep learning adapted to this task, such as SEGAN, have presented relevant results. This study compared the performance of the mentioned techniques in 85 noise conditions regarding quality, intelligibility, and distortion; and concluded that classical techniques continue to exhibit superior results for most scenarios, but, in severe noise scenarios, SEGAN performed better and with lower variance.
The Czech Court Decisions Corpus (CzCDC): Availability as the First Step
Authors: Tereza Novotná, Jakub Harašta
Date: 2019-10-21 00:00:00

In this paper, we describe the Czech Court Decision Corpus (CzCDC). CzCDC is a dataset of 237,723 decisions published by the Czech apex (or top-tier) courts, namely the Supreme Court, the Supreme Administrative Court and the Constitutional Court. All the decisions were published between 1st January 1993 and 30th September 2018. Court decisions are available on the webpages of the respective courts or via commercial databases of legal information. This often leads researchers interested in these decisions to reach either to respective court or to commercial provider. This leads to delays and additional costs. These are further exacerbated by a lack of inter-court standard in the terms of the data format in which courts provide their decisions. Additionally, courts' databases often lack proper documentation. Our goal is to make the dataset of court decisions freely available online in consistent (plain) format to lower the cost associated with obtaining data for future research. We believe that simplified access to court decisions through the CzCDC could benefit other researchers. In this paper, we describe the processing of decisions before their inclusion into CzCDC and basic statistics of the dataset. This dataset contains plain texts of court decisions and these texts are not annotated for any grammatical or syntactical features.
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
Authors: Dongge Han, Wendelin Boehmer, Michael Wooldridge, Alex Rogers
Date: 2019-10-21 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predicting the behaviours of others, and responding promptly to changes in such behaviours. One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical reinforcement learning framework. However, this approach results in inflexibility of agents if options have an extended duration and are dynamic. While adjusting the executed option at each step improves flexibility from a single-agent perspective, frequent changes in options can induce inconsistency between an agent's actual behaviour and its broadcast intention. In order to balance flexibility and predictability, we propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options. We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Spectral Characterization of functional MRI data on voxel-resolution cortical graphs
Authors: Hamid Behjat, Martin Larsson
Date: 2019-10-21 00:00:00
Tasks: Anatomy

The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. We study graph spectral energy metrics associated to fMRI data of 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as experimental conditions within each task.
Multi-Resolution Weak Supervision for Sequential Data
Authors: Frederic Sala, Paroma Varma, Jason Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré
Date: 2019-10-21 00:00:00
Proceeding: NeurIPS 2019 12

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data. Theoretically, we prove that Dugong, under mild conditions, can uniquely recover the unobserved accuracy and correlation parameters and use parameter sharing to improve sample complexity. Our method assigns clinician-validated labels to population-scale biomedical video repositories, helping outperform traditional supervision by 36.8 F1 points and addressing a key use case where machine learning has been severely limited by the lack of expert labeled data. On average, Dugong improves over traditional supervision by 16.0 F1 points and existing weak supervision approaches by 24.2 F1 points across several video and sensor classification tasks.
CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks
Authors: Gautier Marti
Date: 2019-10-21 00:00:00

We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts about empirical correlation matrices estimated on asset returns. This is the first time such results are documented in the literature. Practical financial applications range from trading strategies enhancement to risk and portfolio stress testing. Such generative models can also help ground empirical finance deeper into science by allowing for falsifiability of statements and more objective comparison of empirical methods.