pwc-archive/papers-with-abstracts huggingface dataset:
Extending and Analyzing Self-Supervised Learning Across Domains
Authors: Bram Wallace, Bharath Hariharan
Date: 2020-04-24 00:00:00
Proceeding: ECCV 2020 8
Tasks: Representation Learning, Self-Supervised Learning

Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods on an unprecedented variety of domains. We discover, among other findings, that Rotation is by far the most semantically meaningful task, with much of the performance of Jigsaw and Instance Discrimination being attributable to the nature of their induced distribution rather than semantic understanding. Additionally, there are several areas, such as fine-grain classification, where all tasks underperform. We quantitatively and qualitatively diagnose the reasons for these failures and successes via novel experiments studying pretext generalization, random labelings, and implicit dimensionality. Code and models are available at https://github.com/BramSW/Extending_SSRL_Across_Domains/.
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Authors: Subhradeep Kayal, Florian Dubost, Harm A. W. M. Tiddens, Marleen de Bruijne
Date: 2020-04-24 00:00:00
Tasks: Data Augmentation, Texture Classification

Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline.
CFR-RL: Traffic Engineering with Reinforcement Learning in SDN
Authors: Jun-Jie Zhang, Minghao Ye, Zehua Guo, Chen-Yu Yen, H. Jonathan Chao
Date: 2020-04-24 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.
DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
Authors: A. Ćiprijanović, G. F. Snyder, B. Nord, J. E. G. Peek
Date: 2020-04-24 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.
Question Answering over Curated and Open Web Sources
Authors: Rishiraj Saha Roy, Avishek Anand
Date: 2020-04-24 00:00:00
Tasks: Information Retrieval, Knowledge Graphs, Question Answering, Retrieval

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants.
Brain-based control of car infotainment
Authors: Andrea Bellotti, Sergey Antopolskiy, Anna Marchenkova, Alessia Colucciello, Pietro Avanzini, Giovanni Vecchiato, Jonas Ambeck-Madsen, Luca Ascari
Date: 2020-04-24 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), ERP

Nowadays, the possibility to run advanced AI on embedded systems allows natural interaction between humans and machines, especially in the automotive field. We present a custom portable EEG-based Brain-Computer Interface (BCI) that exploits Event-Related Potentials (ERPs) induced with an oddball experimental paradigm to control the infotainment menu of a car. A preliminary evaluation of the system was performed on 10 participants in a standard laboratory setting and while driving on a closed private track. The task consisted of repeated presentations of 6 different menu icons in oddball fashion. Subject-specific models were trained with different machine learning approaches on cerebral data from either only laboratory or driving experiments (in-lab and in-car models) or a combination of the two (hybrid model) to classify EEG responses to target and non-target stimuli. All models were tested on the subjects' last in-car sessions that were not used for the training. Analysis of ERPs amplitude showed statistically significant (p < 0.05) differences between the EEG responses associated with target and non-target icons, both in the laboratory and while driving. Classification Accuracy (CA) was above chance level for all subjects in all training configurations, with a deep CNN trained on the hybrid set achieving the highest scores (mean CA = 53 $\pm$ 12 %, with 16 % chance level for the 6-class discrimination). The ranking of the features importance provided by a classical BCI approach suggests an ERP-based discrimination between target and non-target responses. No statistical differences were observed between the CAs for the in-lab and in-car training sets, nor between the EEG responses in these conditions, indicating that the data collected in the standard laboratory setting could be readily used for a real driving application without a noticeable decrease in performance.
The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
Authors: Hana Al-Theiabat, Aisha Al-Sadi
Date: 2020-04-24 00:00:00
Proceeding: NSURL 2019 9
Tasks: Question Similarity

This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this work. The proposed models achieved high F1-scores, which range from (88% to 96%). Our official best result is produced from the ensemble model of using a pre-trained multilingual BERT model with different random seeds with 95.924% F1-Score, which ranks the first among nine participants teams.
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization
Authors: Shatian Wang, Shuoguang Yang, Zhen Xu, Van-Anh Truong
Date: 2020-04-24 00:00:00
Tasks: Thompson Sampling

We propose a cumulative oversampling (CO) method for online learning. Our key idea is to sample parameter estimations from the updated belief space once in each round (similar to Thompson Sampling), and utilize the cumulative samples up to the current round to construct optimistic parameter estimations that asymptotically concentrate around the true parameters as tighter upper confidence bounds compared to the ones constructed with standard UCB methods. We apply CO to a novel budgeted variant of the Influence Maximization (IM) semi-bandits with linear generalization of edge weights, whose offline problem is NP-hard. Combining CO with the oracle we design for the offline problem, our online learning algorithm simultaneously tackles budget allocation, parameter learning, and reward maximization. We show that for IM semi-bandits, our CO-based algorithm achieves a scaled regret comparable to that of the UCB-based algorithms in theory, and performs on par with Thompson Sampling in numerical experiments.
The Plant Pathology 2020 challenge dataset to classify foliar disease of apples
Authors: Ranjita Thapa, Noah Snavely, Serge Belongie, Awais Khan
Date: 2020-04-24 00:00:00
Tasks: Classification, Fine-Grained Visual Categorization, General Classification, Management

Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for 'Plant Pathology Challenge'; part of the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2020 (Computer Vision and Pattern Recognition). We also trained an off-the-shelf convolutional neural network (CNN) on this data for disease classification and achieved 97% accuracy on a held-out test set. This dataset will contribute towards development and deployment of machine learning-based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot dataset for a larger, more comprehensive expert-annotated dataset for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.
Practical Comparable Data Collection for Low-Resource Languages via Images
Authors: Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang, Graham Neubig
Date: 2020-04-24 00:00:00
Tasks: Machine Translation, Translation

We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of images as a pivot between the source and target languages by getting captions for such images in both languages independently. Human evaluations on the English-Hindi comparable corpora created with our method show that 81.1% of the pairs are acceptable translations, and only 2.47% of the pairs are not translations at all. We further establish the potential of the dataset collected through our approach by experimenting on two downstream tasks - machine translation and dictionary extraction. All code and data are available at https://github.com/madaan/PML4DC-Comparable-Data-Collection.
Symbolic Regression Driven by Training Data and Prior Knowledge
Authors: J. Kubalík, E. Derner, R. Babuška
Date: 2020-04-24 00:00:00
Tasks: regression, Symbolic Regression

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
Authors: Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu, Yen-Kuang Chen, Yuan Xie
Date: 2020-04-24 00:00:00

Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are very computationally intensive and reach the hardware limit of the existing server and IoT devices. Thus, finding better model architectures with much less amount of computation while maximally preserving the accuracy is a popular research topic. Among various mechanisms that aim to reduce the computation complexity, identifying the zero values in the model weights and in the activations to avoid computing them is a promising direction. In this paper, we summarize the current status of the research on the computation of sparse neural networks, from the perspective of the sparse algorithms, the software frameworks, and the hardware accelerations. We observe that the search for the sparse structure can be a general methodology for high-quality model explorations, in addition to a strategy for high-efficiency model execution. We discuss the model accuracy influenced by the number of weight parameters and the structure of the model. The corresponding models are called to be located in the weight dominated and structure dominated regions, respectively. We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region. In order to achieve the goal, new approaches are required to search for proper sparse structures, and new sparse training hardware needs to be developed to facilitate fast iterations of sparse models.
Towards Differentiable Resampling
Authors: Michael Zhu, Kevin Murphy, Rico Jonschkowski
Date: 2020-04-24 00:00:00
Tasks: State Estimation

Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However, resampling remains a challenge in these works, as it is inherently non-differentiable. We address this challenge by replacing traditional resampling with a learned neural network resampler. We present a novel network architecture, the particle transformer, and train it for particle resampling using a likelihood-based loss function over sets of particles. Incorporated into a differentiable particle filter, our model can be end-to-end optimized jointly with the other particle filter components via gradient descent. Our results show that our learned resampler outperforms traditional resampling techniques on synthetic data and in a simulated robot localization task.
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
Authors: Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine
Date: 2020-04-24 00:00:00
Proceeding: ICLR 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target. In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input. This is typically the case when we have a standard conditioning input, such as a state observation, and a "privileged" input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent. In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication). Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access. In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not. We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Authors: Ruohong Zhang, Yu Hao, Donghan Yu, Wei-Cheng Chang, Guokun Lai, Yiming Yang
Date: 2020-04-24 00:00:00
Tasks: Change Point Detection, Decoder, Time Series, Time Series Analysis

Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore the dependency structures entirely or rely on the (unrealistic) assumption that the correlation structures are static over time. In this paper, we propose a Correlation-aware Dynamics Model for CPD, which explicitly models the correlation structure and dynamics of variables by incorporating graph neural networks into an encoder-decoder framework. Extensive experiments on synthetic and real-world datasets demonstrate the advantageous performance of the proposed model on CPD tasks over strong baselines, as well as its ability to classify the change-points as correlation changes or independent changes. Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
Robust posterior inference when statistically emulating forward simulations
Authors: Grigor Aslanyan, Richard Easther, Nathan Musoke, Layne C. Price
Date: 2020-04-24 00:00:00

Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate these slow calculations, these approaches are only safe if the approximations are well understood and controlled. This workshop submission reviews and updates a previously published method, which has been used in cosmological simulations, to (1) train an emulator while simultaneously estimating posterior probabilities with MCMC and (2) explicitly propagate the emulation error into errors on the posterior probabilities for model parameters. We demonstrate how these techniques can be applied to quickly estimate posterior distributions for parameters of the $\Lambda$CDM cosmology model, while also gauging the robustness of the emulator approximation.
How Reliable are Test Numbers for Revealing the COVID-19 Ground Truth and Applying Interventions?
Authors: Aditya Gopalan, Himanshu Tyagi
Date: 2020-04-24 00:00:00

The number of confirmed cases of COVID-19 is often used as a proxy for the actual number of ground truth COVID-19 infected cases in both public discourse and policy making. However, the number of confirmed cases depends on the testing policy, and it is important to understand how the number of positive cases obtained using different testing policies reveals the unknown ground truth. We develop an agent-based simulation framework in Python that can simulate various testing policies as well as interventions such as lockdown based on them. The interaction between the agents can take into account various communities and mobility patterns. A distinguishing feature of our framework is the presence of another `flu'-like illness with symptoms similar to COVID-19, that allows us to model the noise in selecting the pool of patients to be tested. We instantiate our model for the city of Bengaluru in India, using census data to distribute agents geographically, and traffic flow mobility data to model long-distance interactions and mixing. We use the simulation framework to compare the performance of three testing policies: Random Symptomatic Testing (RST), Contact Tracing (CT), and a new Location Based Testing policy (LBT). We observe that if a sufficient fraction of symptomatic patients come out for testing, then RST can capture the ground truth quite closely even with very few daily tests. However, CT consistently captures more positive cases. Interestingly, our new LBT, which is operationally less intensive than CT, gives performance that is comparable with CT. In another direction, we compare the efficacy of these three testing policies in enabling lockdown, and observe that CT flattens the ground truth curve maximally, followed closely by LBT, and significantly better than RST.