pwc-archive/papers-with-abstracts huggingface dataset:
Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder
Authors: Mark Alan Matties
Date: 2020-11-18 00:00:00
Tasks: Clustering, Triplet

The use of deep neural network (DNN) autoencoders (AEs) has recently exploded due to their wide applicability. However, the embedding representation produced by a standard DNN AE that is trained to minimize only the reconstruction error does not always reveal more subtle patterns in the data. Sometimes, the autoencoder needs further direction in the form of one or more additional loss functions. In this paper, we use an autoencoder enhanced with triplet loss to promote the clustering of vectors that are related through permutations of constituent subvectors. With this approach, we can create an embedding of the vector that is nearly invariant to such permutations. We can then use these invariant embeddings as inputs to other problems, like classification and clustering, and improve detection accuracy in those problems.
MOFA: Modular Factorial Design for Hyperparameter Optimization
Authors: Bo Xiong, Yimin Huang, Hanrong Ye, Steffen Staab, Zhenguo Li
Date: 2020-11-18 00:00:00
Tasks: Hyperparameter Optimization, Model Selection

This paper presents a novel and lightweight hyperparameter optimization (HPO) method, MOdular FActorial Design (MOFA). MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis. Each round first explores the configuration space by constructing a low-discrepancy set of hyperparameters that cover this space well while de-correlating hyperparameters, and then exploits evaluation results through factorial analysis that determines which hyperparameters should be further explored and which should become fixed in the next round. We prove that the inference of MOFA achieves higher confidence than other sampling schemes. Each individual round is highly parallelizable and hence offers major improvements of efficiency compared to model-based methods. Empirical results show that MOFA achieves better effectiveness and efficiency compared with state-of-the-art methods.
StressNet: Detecting Stress in Thermal Videos
Authors: Satish Kumar, A S M Iftekhar, Michael Goebel, Tom Bullock, Mary H. MacLean, Michael B. Miller, Tyler Santander, Barry Giesbrecht, Scott T. Grafton, B. S. Manjunath
Date: 2020-11-18 00:00:00

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network--"StressNet"--features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval: a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans ). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual's stress state ( i.e. stress or no stress ). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842. The source code is available on Github.
The Influences of Pre-birth Factors in Early Assessment of Child Mortality using Machine Learning Techniques
Authors: Asadullah Hill Galib, Nadia Nahar, B M Mainul Hossain
Date: 2020-11-18 00:00:00
Tasks: Attribute, BIG-bench Machine Learning

Analysis of child mortality is crucial as it pertains to the policy and programs of a country. The early assessment of patterns and trends in causes of child mortality help decision-makers assess needs, prioritize interventions, and monitor progress. Post-birth factors of the child, such as real-time clinical data, health data of the child, etc. are frequently used in child mortality studies. However, in the early assessment of child mortality, pre-birth factors would be more practical and beneficial than the post-birth factors. This study aims at incorporating pre-birth factors, such as birth history, maternal history, reproduction history, socioeconomic condition, etc. for classifying child mortality. To assess the relative importance of the features, Information Gain (IG) attribute evaluator is employed. For classifying child mortality, four machine learning algorithms are evaluated. Results show that the proposed approach achieved an AUC score of 0.947 in classifying child mortality which outperformed the clinical standards. In terms of accuracy, precision, recall, and f-1 score, the results are also notable and uniform. In developing countries like Bangladesh, the early assessment of child mortality using pre-birth factors would be effective and feasible as it avoids the uncertainty of the post-birth factors.
Randomized Self Organizing Map
Authors: Nicolas P. Rougier, Georgios Is. Detorakis
Date: 2020-11-18 00:00:00
Tasks: Topological Data Analysis

We propose a variation of the self organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensions tasks as well as on the MNIST handwritten digits dataset and validated using spectral analysis and topological data analysis tools. We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Authors: Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip H. S. Torr, Mingfei Sun, Shimon Whiteson
Date: 2020-11-18 00:00:00
Tasks: All, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), SMAC, SMAC+, Starcraft

Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function. In this paper, we demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a form of independent learning in which each agent simply estimates its local value function, can perform just as well as or better than state-of-the-art joint learning approaches on popular multi-agent benchmark suite SMAC with little hyperparameter tuning. We also compare IPPO to several variants; the results suggest that IPPO's strong performance may be due to its robustness to some forms of environment non-stationarity.
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
Authors: Hassan Akbari, Hamid Palangi, Jianwei Yang, Sudha Rao, Asli Celikyilmaz, Roland Fernandez, Paul Smolensky, Jianfeng Gao, Shih-Fu Chang
Date: 2020-11-18 00:00:00
Tasks: Dictionary Learning, Disentanglement, Video Captioning

Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions. We refer to these relations as relative roles and leverage them to make each token role-aware using attention. This results in a more structured and interpretable architecture that incorporates modality-specific inductive biases for the captioning task. Intuitively, the model is able to learn spatial, temporal, and cross-modal relations in a given pair of video and text. The disentanglement achieved by our proposal gives the model more capacity to capture multi-modal structures which result in captions with higher quality for videos. Our experiments on two established video captioning datasets verifies the effectiveness of the proposed approach based on automatic metrics. We further conduct a human evaluation to measure the grounding and relevance of the generated captions and observe consistent improvement for the proposed model. The codes and trained models can be found at https://github.com/hassanhub/R3Transformer
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff
Authors: Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta
Date: 2020-11-18 00:00:00
Tasks: Data Augmentation, Data Poisoning

Data poisoning and backdoor attacks manipulate victim models by maliciously modifying training data. In light of this growing threat, a recent survey of industry professionals revealed heightened fear in the private sector regarding data poisoning. Many previous defenses against poisoning either fail in the face of increasingly strong attacks, or they significantly degrade performance. However, we find that strong data augmentations, such as mixup and CutMix, can significantly diminish the threat of poisoning and backdoor attacks without trading off performance. We further verify the effectiveness of this simple defense against adaptive poisoning methods, and we compare to baselines including the popular differentially private SGD (DP-SGD) defense. In the context of backdoors, CutMix greatly mitigates the attack while simultaneously increasing validation accuracy by 9%.
Contextual Fusion For Adversarial Robustness
Authors: Aiswarya Akumalla, Seth Haney, Maksim Bazhenov
Date: 2020-11-18 00:00:00
Tasks: Adversarial Robustness

Mammalian brains handle complex reasoning tasks in a gestalt manner by integrating information from regions of the brain that are specialised to individual sensory modalities. This allows for improved robustness and better generalisation ability. In contrast, deep neural networks are usually designed to process one particular information stream and susceptible to various types of adversarial perturbations. While many methods exist for detecting and defending against adversarial attacks, they do not generalise across a range of attacks and negatively affect performance on clean, unperturbed data. We developed a fusion model using a combination of background and foreground features extracted in parallel from Places-CNN and Imagenet-CNN. We tested the benefits of the fusion approach on preserving adversarial robustness for human perceivable (e.g., Gaussian blur) and network perceivable (e.g., gradient-based) attacks for CIFAR-10 and MS COCO data sets. For gradient based attacks, our results show that fusion allows for significant improvements in classification without decreasing performance on unperturbed data and without need to perform adversarial retraining. Our fused model revealed improvements for Gaussian blur type perturbations as well. The increase in performance from fusion approach depended on the variability of the image contexts; larger increases were seen for classes of images with larger differences in their contexts. We also demonstrate the effect of regularization to bias the classifier decision in the presence of a known adversary. We propose that this biologically inspired approach to integrate information across multiple modalities provides a new way to improve adversarial robustness that can be complementary to current state of the art approaches.
TRAT: Tracking by Attention Using Spatio-Temporal Features
Authors: Hasan Saribas, Hakan Cevikalp, Okan Köpüklü, Bedirhan Uzun
Date: 2020-11-18 00:00:00
Tasks: Object Tracking

Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time. Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information. In this paper, we propose a two-stream deep neural network tracker that uses both spatial and temporal features. Our architecture is developed over ATOM tracker and contains two backbones: (i) 2D-CNN network to capture appearance features and (ii) 3D-CNN network to capture motion features. The features returned by the two networks are then fused with attention based Feature Aggregation Module (FAM). Since the whole architecture is unified, it can be trained end-to-end. The experimental results show that the proposed tracker TRAT (TRacking by ATtention) achieves state-of-the-art performance on most of the benchmarks and it significantly outperforms the baseline ATOM tracker.
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Authors: Tanveer Syeda-Mahmood, K. C. L Wong, Joy T. Wu, M. D., M. P. H, Ashutosh Jadhav, Ph. D, Orest Boyko, M. D. Ph. D
Date: 2020-11-18 00:00:00
Tasks: Diagnostic

Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful of coarse finding classes such as opacities, masses and nodules. In this paper, we focus on extracting and learning fine-grained labels for chest X-ray images. Specifically we develop a new method of extracting fine-grained labels from radiology reports by combining vocabulary-driven concept extraction with phrasal grouping in dependency parse trees for association of modifiers with findings. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected and sufficiently large datasets acquired to train a new deep learning model designed for fine-grained classification. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels. The resulting network, to our knowledge, is the first to recognize fine-grained descriptions of findings in images covering over nine modifiers including laterality, location, severity, size and appearance.
GRAPHSPY: Fused Program Semantic-Level Embedding via Graph Neural Networks for Dead Store Detection
Authors: Yixin Guo, Pengcheng Li, Yingwei Luo, Xiaolin Wang, Zhenlin Wang
Date: 2020-11-18 00:00:00
Tasks: Graph Neural Network

Production software oftentimes suffers from the issue of performance inefficiencies caused by inappropriate use of data structures, programming abstractions, and conservative compiler optimizations. It is desirable to avoid unnecessary memory operations. However, existing works often use a whole-program fine-grained monitoring method with incredibly high overhead. To this end, we propose a learning-aided approach to identify unnecessary memory operations intelligently with low overhead. By applying several prevalent graph neural network models to extract program semantics with respect to program structure, execution order and dynamic states, we present a novel, hybrid program embedding approach so that to derive unnecessary memory operations through the embedding. We train our model with tens of thousands of samples acquired from a set of real-world benchmarks. Results show that our model achieves 90% of accuracy and incurs only around a half of time overhead of the state-of-art tool.
High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records
Authors: Alex Taylor, Ross Kleiman, Scott Hebbring, Peggy Peissig, David Page
Date: 2020-11-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Accurate estimation of healthcare costs is crucial for healthcare systems to plan and effectively negotiate with insurance companies regarding the coverage of patient-care costs. Greater accuracy in estimating healthcare costs would provide mutual benefit for both health systems and the insurers that support these systems by better aligning payment models with patient-care costs. This study presents the results of a generalizable machine learning approach to predicting medical events built from 40 years of data from >860,000 patients pertaining to >6,700 prescription medications, courtesy of Marshfield Clinic in Wisconsin. It was found that models built using this approach performed well when compared to similar studies predicting physician prescriptions of individual medications. In addition to providing a comprehensive predictive model for all drugs in a large healthcare system, the approach taken in this research benefits from potential applicability to a wide variety of other medical events.
Data Representing Ground-Truth Explanations to Evaluate XAI Methods
Authors: Shideh Shams Amiri, Rosina O. Weber, Prateek Goel, Owen Brooks, Archer Gandley, Brian Kitchell, Aaron Zehm
Date: 2020-11-18 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Interpretable Machine Learning

Explainable artificial intelligence (XAI) methods are currently evaluated with approaches mostly originated in interpretable machine learning (IML) research that focus on understanding models such as comparison against existing attribution approaches, sensitivity analyses, gold set of features, axioms, or through demonstration of images. There are problems with these methods such as that they do not indicate where current XAI approaches fail to guide investigations towards consistent progress of the field. They do not measure accuracy in support of accountable decisions, and it is practically impossible to determine whether one XAI method is better than the other or what the weaknesses of existing models are, leaving researchers without guidance on which research questions will advance the field. Other fields usually utilize ground-truth data and create benchmarks. Data representing ground-truth explanations is not typically used in XAI or IML. One reason is that explanations are subjective, in the sense that an explanation that satisfies one user may not satisfy another. To overcome these problems, we propose to represent explanations with canonical equations that can be used to evaluate the accuracy of XAI methods. The contributions of this paper include a methodology to create synthetic data representing ground-truth explanations, three data sets, an evaluation of LIME using these data sets, and a preliminary analysis of the challenges and potential benefits in using these data to evaluate existing XAI approaches. Evaluation methods based on human-centric studies are outside the scope of this paper.
Challenges in Deploying Machine Learning: a Survey of Case Studies
Authors: Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
Date: 2020-11-18 00:00:00
Tasks: BIG-bench Machine Learning, Survey

In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow we show that practitioners face issues at each stage of the deployment process. The goal of this paper is to lay out a research agenda to explore approaches addressing these challenges.
NeVer 2.0: Learning, Verification and Repair of Deep Neural Networks
Authors: Dario Guidotti, Luca Pulina, Armando Tacchella
Date: 2020-11-18 00:00:00
Tasks: Philosophy

In this work, we present an early prototype of NeVer 2.0, a new system for automated synthesis and analysis of deep neural networks.NeVer 2.0borrows its design philosophy from NeVer, the first package that integrated learning, automated verification and repair of (shallow) neural networks in a single tool. The goal of NeVer 2.0 is to provide a similar integration for deep networks by leveraging a selection of state-of-the-art learning frameworks and integrating them with verification algorithms to ease the scalability challenge and make repair of faulty networks possible.
Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM
Authors: Elham Gholami, Seyed Reza Kamel Tabbakh, Maryam Kheirabadi
Date: 2020-11-18 00:00:00

Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, the images to the convolutional neural network are provided and the training and test operations are triggered. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient. Based on experiments, the DenseNet network has the highest accuracy compared to other deep architectures. The experimental results show that the accuracy of this network for gastric cancer detection reaches 91% which shows the superiority of method in comparison to the state-of-the-art methods.
A Theoretical Computer Science Perspective on Consciousness
Authors: Manuel Blum, Lenore Blum
Date: 2020-11-18 00:00:00

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM). We are not looking for a complex model of the brain nor of cognition but for a simple model of (the admittedly complex concept of) consciousness. After formally defining CTM, we give a formal definition of consciousness in CTM. We then suggest why the CTM has the feeling of consciousness. The reasonableness of the definitions and explanations can be judged by how well they agree with commonly accepted intuitive concepts of human consciousness, the breadth of related concepts that the model explains easily and naturally, and the extent of its agreement with scientific evidence.