pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE
Authors: Yusuke Yasuda, Xin Wang, Junichi Yamagishi
Date: 2020-10-19 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS). We propose a new TTS framework using explicit duration modeling that incorporates duration as a discrete latent variable to TTS and enables joint optimization of whole modules from scratch. We formulate our method based on conditional VQ-VAE to handle discrete duration in a variational autoencoder and provide a theoretical explanation to justify our method. In our framework, a connectionist temporal classification (CTC) -based force aligner acts as the approximate posterior, and text-to-duration works as the prior in the variational autoencoder. We evaluated our proposed method with a listening test and compared it with other TTS methods based on soft-attention or explicit duration modeling. The results showed that our systems rated between soft-attention-based methods (Transformer-TTS, Tacotron2) and explicit duration modeling-based methods (Fastspeech).
Drug Repurposing for COVID-19 via Knowledge Graph Completion
Authors: Rui Zhang, Dimitar Hristovski, Dalton Schutte, Andrej Kastrin, Marcelo Fiszman, Halil Kilicoglu
Date: 2020-10-19 00:00:00
Tasks: Knowledge Graph Completion

Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering
Authors: Jeroen Offerijns, Suzan Verberne, Tessa Verhoef
Date: 2020-10-19 00:00:00
Tasks: Distractor Generation, Language Modeling, Language Modelling, Multiple-choice, Question Answering, Question Generation, Question-Generation, Text Generation

For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question generation itself is an active research topic, generating distractors (the incorrect multiple choice options) receives much less attention. A missed opportunity, since there is still a lot of room for improvement in this area. In this work, we train a GPT-2 language model to generate three distractors for a given question and text context, using the RACE dataset. Next, we train a BERT language model to answer MCQs, and use this model as a filter, to select only questions that can be answered and therefore presumably make sense. To evaluate our work, we start by using text generation metrics, which show that our model outperforms earlier work on distractor generation (DG) and achieves state-of-the-art performance. Also, by calculating the question answering ability, we show that larger base models lead to better performance. Moreover, we conducted a human evaluation study, which confirmed the quality of the generated questions, but showed no statistically significant effect of the QA filter.
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Authors: Difan Zou, Pan Xu, Quanquan Gu
Date: 2020-10-19 00:00:00

We provide a new convergence analysis of stochastic gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave. At the core of our approach is a novel conductance analysis of SGLD using an auxiliary time-reversible Markov Chain. Under certain conditions on the target distribution, we prove that $\tilde O(d^4\epsilon^{-2})$ stochastic gradient evaluations suffice to guarantee $\epsilon$-sampling error in terms of the total variation distance, where $d$ is the problem dimension. This improves existing results on the convergence rate of SGLD (Raginsky et al., 2017; Xu et al., 2018). We further show that provided an additional Hessian Lipschitz condition on the log-density function, SGLD is guaranteed to achieve $\epsilon$-sampling error within $\tilde O(d^{15/4}\epsilon^{-3/2})$ stochastic gradient evaluations. Our proof technique provides a new way to study the convergence of Langevin-based algorithms and sheds some light on the design of fast stochastic gradient-based sampling algorithms.
Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation
Authors: Sarvesh Patil, Chava Y P D Phani Rajanish, Naveen Margankunte
Date: 2020-10-19 00:00:00
Tasks: object-detection, Object Detection, Super-Resolution, Translation

Particle Size Analysis (PSA) is an important process carried out in a number of industries, which can significantly influence the properties of the final product. A ubiquitous instrument for this purpose is the Optical Microscope (OM). However, OMs are often prone to drawbacks like low resolution, small focal depth, and edge features being masked due to diffraction. We propose a powerful application of a combination of two Conditional Generative Adversarial Networks (cGANs) that Super Resolve OM images to look like Scanning Electron Microscope (SEM) images. We further demonstrate the use of a custom object detection module that can perform efficient PSA of the super-resolved particles on both, densely and sparsely packed images. The PSA results obtained from the super-resolved images have been benchmarked against human annotators, and results obtained from the corresponding SEM images. The proposed models show a generalizable way of multi-modal image translation and super-resolution for accurate particle size estimation.
Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier
Authors: Behrouz Rostami, D. M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu
Date: 2020-10-19 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists the specialists in the field to classify the wounds with less financial and time costs. Different machine learning and deep learning-based wound classification methods have been proposed in the literature. In this study, we have developed an ensemble Deep Convolutional Neural Network-based classifier to classify wound images including surgical, diabetic, and venous ulcers, into multi-classes. The output classification scores of two classifiers (patch-wise and image-wise) are fed into a Multi-Layer Perceptron to provide a superior classification performance. A 5-fold cross-validation approach is used to evaluate the proposed method. We obtained maximum and average classification accuracy values of 96.4% and 94.28% for binary and 91.9\% and 87.7\% for 3-class classification problems. The results show that our proposed method can be used effectively as a decision support system in classification of wound images or other related clinical applications.
Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation
Authors: Zicong Zhang, Kimerly Powell, Changchang Yin, Shilei Cao, Dani Gonzalez, Yousef Hannawi, Ping Zhang
Date: 2020-10-19 00:00:00
Tasks: Segmentation

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical consequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinicalscans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance. Specifically, we designed a dual-path segmentation model with two novel connecting mechanisms, namely multi-input attention module (MAM) and attention fusion module (AFM) to fuse the information from two paths for accurate results. Experiments on two publicly available datasets show the effectiveness of the proposed BAGAU-Net. With only FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art method with T1-weighted images, paving the way for effective development of WMH segmentation. Availability:https://github.com/Ericzhang1/BAGAU-Net
Learning to Reconstruct and Segment 3D Objects
Authors: Bo Yang
Date: 2020-10-19 00:00:00

To endow machines with the ability to perceive the real-world in a three dimensional representation as we do as humans is a fundamental and long-standing topic in Artificial Intelligence. Given different types of visual inputs such as images or point clouds acquired by 2D/3D sensors, one important goal is to understand the geometric structure and semantics of the 3D environment. Traditional approaches usually leverage hand-crafted features to estimate the shape and semantics of objects or scenes. However, they are difficult to generalize to novel objects and scenarios, and struggle to overcome critical issues caused by visual occlusions. By contrast, we aim to understand scenes and the objects within them by learning general and robust representations using deep neural networks, trained on large-scale real-world 3D data. To achieve these aims, this thesis makes three core contributions from object-level 3D shape estimation from single or multiple views to scene-level semantic understanding.
EPTAS for $k$-means Clustering of Affine Subspaces
Authors: Eduard Eiben, Fedor V. Fomin, Petr A. Golovach, William Lochet, Fahad Panolan, Kirill Simonov
Date: 2020-10-19 00:00:00
Tasks: Clustering

We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries. When data objects are represented by points in $\mathbb{R}^d$, a data point is said to be incomplete when some of its entries are missing or unspecified. An incomplete data point with at most $\Delta$ unspecified entries corresponds to an axis-parallel affine subspace of dimension at most $\Delta$, called a $\Delta$-point. Thus we seek a partition of $n$ input $\Delta$-points into $k$ clusters minimizing the $k$-means objective. For $\Delta=0$, when all coordinates of each point are specified, this is the usual $k$-means clustering. We give an algorithm that finds an $(1+ \epsilon)$-approximate solution in time $f(k,\epsilon, \Delta) \cdot n^2 \cdot d$ for some function $f$ of $k,\epsilon$, and $\Delta$ only.
GANs for learning from very high class conditional noisy labels
Authors: Sandhya Tripathi, N Hemachandra
Date: 2020-10-19 00:00:00
Tasks: Binary Classification, Vocal Bursts Intensity Prediction

We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelled data points from noisy labelled data and 0.1% or 1% clean labels such that the generated and true (clean) labelled data distributions are close; generated labelled data is used to learn a good classifier. The mode collapse problem while generating correct feature-label pairs and the problem of skewed feature-label dimension ratio ($\sim$ 784:1) are avoided by using Wasserstein GAN and a simple data representation change. Another WGAN with information-theoretic flavour on top of the new representation is also proposed. The major advantage of both schemes is their significant improvement over the existing ones in presence of very high CCN rates, without either estimating or cross-validating over the noise rates. We proved that KL divergence between clean and noisy distribution increases w.r.t. noise rates in symmetric label noise model; can be extended to high CCN rates. This implies that our schemes perform well due to the adversarial nature of GANs. Further, use of generative approach (learning clean joint distribution) while handling noise enables our schemes to perform better than discriminative approaches like GLC, LDMI and GCE; even when the classes are highly imbalanced. Using Friedman F test and Nemenyi posthoc test, we showed that on high dimensional binary class synthetic, MNIST and Fashion MNIST datasets, our schemes outperform the existing methods and demonstrate consistent performance across noise rates.
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
Authors: Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu
Date: 2020-10-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i.e.,} $d\gg n$) where the underlying parameter is assumed to be sparse. Specifically, we propose a method called Trimmed (Gradient) Expectation Maximization which adds a trimming gradients step and a hard thresholding step to the Expectation step (E-step) and the Maximization step (M-step), respectively. We show that under some mild assumptions and with an appropriate initialization, the algorithm is corruption-proofing and converges to the (near) optimal statistical rate geometrically when the fraction of the corrupted samples $\epsilon$ is bounded by $ \tilde{O}(\frac{1}{\sqrt{n}})$. Moreover, we apply our general framework to three canonical models: mixture of Gaussians, mixture of regressions and linear regression with missing covariates. Our theory is supported by thorough numerical results.
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums
Authors: Marina Sokolova, Victoria Bobicev
Date: 2020-10-19 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis, Sentiment Classification

We propose the Echo-Chamber Effect assessment of an online forum. Sentiments perceived by the forum readers are at the core of the analysis; a complete message is the unit of the study. We build 14 models and apply those to represent discussions gathered from an online medical forum. We use four multi-class sentiment classification applications and two Machine Learning algorithms to evaluate prowess of the assessment models.
Teacher-Student Competition for Unsupervised Domain Adaptation
Authors: Ruixin Xiao, Zhilei Liu, Baoyuan Wu
Date: 2020-10-19 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared feature extractor, which causes the source-bias problem. This paper proposes an unsupervised domain adaptation approach with Teacher-Student Competition (TSC). In particular, a student network is introduced to learn the target-specific feature space, and we design a novel competition mechanism to select more credible pseudo-labels for the training of student network. We introduce a teacher network with the structure of existing conventional UDA method, and both teacher and student networks compete to provide target pseudo-labels to constrain every target sample's training in student network. Extensive experiments demonstrate that our proposed TSC framework significantly outperforms the state-of-the-art domain adaptation methods on Office-31 and ImageCLEF-DA benchmarks.
Bayesian Neural Networks with Soft Evidence
Authors: Edward Yu
Date: 2020-10-19 00:00:00
Tasks: Benchmarking

Bayes's rule deals with hard evidence, that is, we can calculate the probability of event $A$ occuring given that event $B$ has occurred. Soft evidence, on the other hand, involves a degree of uncertainty about whether event $B$ has actually occurred or not. Jeffrey's rule of conditioning provides a way to update beliefs in the case of soft evidence. We provide a framework to learn a probability distribution on the weights of a neural network trained using soft evidence by way of two simple algorithms for approximating Jeffrey conditionalization. We propose an experimental protocol for benchmarking these algorithms on empirical datasets and find that Jeffrey based methods are competitive or better in terms of accuracy yet show improvements in calibration metrics upwards of 20% in some cases, even when the data contains mislabeled points.
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
Authors: Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck
Date: 2020-10-19 00:00:00
Tasks: All, BIG-bench Machine Learning, Malware Detection

Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition, resisting a variety of attacks from independent attackers. Our system combines multiple, diverse defenses: we address the semantic gap, use various classification models, and apply a stateful defense. This competition gives us the unique opportunity to examine evasion attacks under a realistic scenario. It also highlights that existing machine learning methods can be hardened against attacks by thoroughly analyzing the attack surface and implementing concepts from adversarial learning. Our defense can serve as an additional baseline in the future to strengthen the research on secure learning.
Introducing and Applying Newtonian Blurring: An Augmented Dataset of 126,000 Human Connectomes at braingraph.org
Authors: Laszlo Keresztes, Evelin Szogi, Balint Varga, Vince Grolmusz
Date: 2020-10-19 00:00:00
Tasks: Data Augmentation

Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence (AI) applications. When we apply AI for non-imagelike biological data, hardly any related method exists. Here we introduce the "Newtonian blurring" in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then take 7 repetitions in every possible way, delete the lower and upper extremes, and average the remaining 7-2=5 edge-weights for the data of each subject. This way we augment the 1053 graph-set to 120 x 1053 = 126,360 graphs. In augmentation techniques, it is an important requirement that no artificial additions should be introduced into the dataset. Gaussian blurring and also this Newtonian blurring satisfy this goal. The resulting dataset of 126,360 graphs, each in 5 resolutions (i.e., 631,800 graphs in total), is freely available at the site https://braingraph.org/cms/download-pit-group-connectomes/. Augmenting with Newtonian blurring may also be applicable in other non-image related fields, where probabilistic processing and data averaging are implemented.
Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions
Authors: Vladimir Kolmogorov
Date: 2020-10-19 00:00:00
Proceeding: CVPR 2023 1

We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in (Swoboda, Kolmogorov 2019; Kolmogorov, Pock 2021). As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subproutine based on in-face Frank-Wolfe directions, introduced in (Freund et al. 2017) in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at https://pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html.
On the Generalisation Capabilities of Fingerprint Presentation Attack Detection Methods in the Short Wave Infrared Domain
Authors: Jascha Kolberg, Marta Gomez-Barrero, Christoph Busch
Date: 2020-10-19 00:00:00

Nowadays, fingerprint-based biometric recognition systems are becoming increasingly popular. However, in spite of their numerous advantages, biometric capture devices are usually exposed to the public and thus vulnerable to presentation attacks (PAs). Therefore, presentation attack detection (PAD) methods are of utmost importance in order to distinguish between bona fide and attack presentations. Due to the nearly unlimited possibilities to create new presentation attack instruments (PAIs), unknown attacks are a threat to existing PAD algorithms. This fact motivates research on generalisation capabilities in order to find PAD methods that are resilient to new attacks. In this context, we evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI species. The PAD data is captured in the short wave infrared domain and the results discuss the advantages and drawbacks of this PAD technique regarding unknown attacks.