pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Authors: Charlie Kirkwood, Theo Economou, Nicolas Pugeault
Date: 2020-08-17 00:00:00
Tasks: Spatial Interpolation

For geospatial modelling and mapping tasks, variants of kriging - the spatial interpolation technique developed by South African mining engineer Danie Krige - have long been regarded as the established geostatistical methods. However, kriging and its variants (such as regression kriging, in which auxiliary variables or derivatives of these are included as covariates) are relatively restrictive models and lack capabilities that have been afforded to us in the last decade by deep neural networks. Principal among these is feature learning - the ability to learn filters to recognise task-specific patterns in gridded data such as images. Here we demonstrate the power of feature learning in a geostatistical context, by showing how deep neural networks can automatically learn the complex relationships between point-sampled target variables and gridded auxiliary variables (such as those provided by remote sensing), and in doing so produce detailed maps of chosen target variables. At the same time, in order to cater for the needs of decision makers who require well-calibrated probabilities, we obtain uncertainty estimates via a Bayesian approximation known as Monte Carlo dropout. In our example, we produce a national-scale probabilistic geochemical map from point-sampled assay data, with auxiliary information provided by a terrain elevation grid. Unlike traditional geostatistical approaches, auxiliary variable grids are fed into our deep neural network raw. There is no need to provide terrain derivatives (e.g. slope angles, roughness, etc) because the deep neural network is capable of learning these and arbitrarily more complex derivatives as necessary to maximise predictive performance. We hope our results will raise awareness of the suitability of Bayesian deep learning - and its feature learning capabilities - for large-scale geostatistical applications where uncertainty matters.
Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Authors: Hanlin Gu, Yin Xian, Ilona Christy Unarta, Yuan YAO
Date: 2020-08-17 00:00:00
Tasks: 3D Reconstruction, Clustering, Denoising, Image Denoising, SSIM

The cryo-electron microscopy (Cryo-EM) becomes popular for macromolecular structure determination. However, the 2D images which Cryo-EM detects are of high noise and often mixed with multiple heterogeneous conformations or contamination, imposing a challenge for denoising. Traditional image denoising methods can not remove Cryo-EM image noise well when the signal-noise-ratio (SNR) of images is meager. Thus it is desired to develop new effective denoising techniques to facilitate further research such as 3D reconstruction, 2D conformation classification, and so on. In this paper, we approach the robust image denoising problem in Cryo-EM by a joint Autoencoder and Generative Adversarial Networks (GAN) method. Equipped with robust $\ell_1$ Autoencoder and some designs of robust $\beta$-GANs, one can stabilize the training of GANs and achieve the state-of-the-art performance of robust denoising with low SNR data and against possible information contamination. The method is evaluated by both a heterogeneous conformational dataset on the Thermus aquaticus RNA Polymerase (RNAP) and a homogenous dataset on the Plasmodium falciparum 80S ribosome dataset (EMPIRE-10028), in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), as well as heterogeneous conformation clustering. These results suggest that our proposed methodology provides an effective tool for Cryo-EM 2D image denoising. Our code is available in "https://github.com/ghl1995/denoise-gan-in-cryo-em".
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
Authors: Philipp Geiger, Christoph-Nikolas Straehle
Date: 2020-08-17 00:00:00
Tasks: Decision Making, Interpretable Machine Learning

For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making. It uses a net that reveals preferences from the agents' past joint trajectory, and a differentiable implicit layer that maps these preferences to local Nash equilibria, forming the modes of the predicted future trajectory. Additionally, it learns an equilibrium refinement concept. For tractability, we introduce a new class of continuous potential games and an equilibrium-separating partition of the action space. We provide theoretical results for explicit gradients and soundness. In experiments, we evaluate our approach on two real-world data sets, where we predict highway driver merging trajectories, and on a simple decision-making transfer task.
WAFFLE: Watermarking in Federated Learning
Authors: Buse Gul Atli, Yuxi Xia, Samuel Marchal, N. Asokan
Date: 2020-08-17 00:00:00
Tasks: Federated Learning

Federated learning is a distributed learning technique where machine learning models are trained on client devices in which the local training data resides. The training is coordinated via a central server which is, typically, controlled by the intended owner of the resulting model. By avoiding the need to transport the training data to the central server, federated learning improves privacy and efficiency. But it raises the risk of model theft by clients because the resulting model is available on every client device. Even if the application software used for local training may attempt to prevent direct access to the model, a malicious client may bypass any such restrictions by reverse engineering the application software. Watermarking is a well-known deterrence method against model theft by providing the means for model owners to demonstrate ownership of their models. Several recent deep neural network (DNN) watermarking techniques use backdooring: training the models with additional mislabeled data. Backdooring requires full access to the training data and control of the training process. This is feasible when a single party trains the model in a centralized manner, but not in a federated learning setting where the training process and training data are distributed among several client devices. In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning. It introduces a retraining step at the server after each aggregation of local models into the global model. We show that WAFFLE efficiently embeds a resilient watermark into models incurring only negligible degradation in test accuracy (-0.17%), and does not require access to training data. We also introduce a novel technique to generate the backdoor used as a watermark. It outperforms prior techniques, imposing no communication, and low computational (+3.2%) overhead.
AP-Loss for Accurate One-Stage Object Detection
Authors: Kean Chen, Weiyao Lin, Jianguo Li, John See, Ji Wang, Junni Zou
Date: 2020-08-17 00:00:00
Tasks: Classification, General Classification, Object, object-detection, Object Detection

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We provide in-depth analyses on the good convergence property and computational complexity of the proposed algorithm, both theoretically and empirically. Experimental results demonstrate notable improvement in addressing the imbalance issue in object detection over existing AP-based optimization algorithms. An improved state-of-the-art performance is achieved in one-stage detectors based on AP-loss over detectors using classification-losses on various standard benchmarks. The proposed framework is also highly versatile in accommodating different network architectures. Code is available at https://github.com/cccorn/AP-loss .
Evaluating for Diversity in Question Generation over Text
Authors: Michael Sejr Schlichtkrull, Weiwei Cheng
Date: 2020-08-17 00:00:00
Tasks: Decoder, Diversity, Question Generation, Question-Generation

Generating diverse and relevant questions over text is a task with widespread applications. We argue that commonly-used evaluation metrics such as BLEU and METEOR are not suitable for this task due to the inherent diversity of reference questions, and propose a scheme for extending conventional metrics to reflect diversity. We furthermore propose a variational encoder-decoder model for this task. We show through automatic and human evaluation that our variational model improves diversity without loss of quality, and demonstrate how our evaluation scheme reflects this improvement.
Forward and inverse reinforcement learning sharing network weights and hyperparameters
Authors: Eiji Uchibe, Kenji Doya
Date: 2020-08-17 00:00:00
Tasks: Imitation Learning, MuJoCo, reinforcement-learning, Reinforcement Learning (RL)

This paper proposes model-free imitation learning named Entropy-Regularized Imitation Learning (ERIL) that minimizes the reverse Kullback-Leibler (KL) divergence. ERIL combines forward and inverse reinforcement learning (RL) under the framework of an entropy-regularized Markov decision process. An inverse RL step computes the log-ratio between two distributions by evaluating two binary discriminators. The first discriminator distinguishes the state generated by the forward RL step from the expert's state. The second discriminator, which is structured by the theory of entropy regularization, distinguishes the state-action-next-state tuples generated by the learner from the expert ones. One notable feature is that the second discriminator shares hyperparameters with the forward RL, which can be used to control the discriminator's ability. A forward RL step minimizes the reverse KL estimated by the inverse RL step. We show that minimizing the reverse KL divergence is equivalent to finding an optimal policy. Our experimental results on MuJoCo-simulated environments and vision-based reaching tasks with a robotic arm show that ERIL is more sample-efficient than the baseline methods. We apply the method to human behaviors that perform a pole-balancing task and describe how the estimated reward functions show how every subject achieves her goal.
Estimating Causal Effects with the Neural Autoregressive Density Estimator
Authors: Sergio Garrido, Stanislav S. Borysov, Jeppe Rich, Francisco C. Pereira
Date: 2020-08-17 00:00:00
Tasks: Causal Inference

Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables given conditional dependencies. In this paper, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within the Pearl's do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables.
Adaptive Hierarchical Hyper-gradient Descent
Authors: Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran
Date: 2020-08-17 00:00:00
Tasks: Meta-Learning

In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures. Meanwhile, we show the relationship between regularizing over-parameterized learning rates and building combinations of adaptive learning rates at different levels. The experiments on several network architectures, including feed-forward networks, LeNet-5 and ResNet-18/34, show that the proposed multi-level adaptive approach can outperform baseline adaptive methods in a variety of circumstances.
A Survey of Active Learning for Text Classification using Deep Neural Networks
Authors: Christopher Schröder, Andreas Niekler
Date: 2020-08-17 00:00:00
Tasks: Active Learning, Classification, General Classification, text-classification, Text Classification, Word Embeddings

Natural language processing (NLP) and neural networks (NNs) have both undergone significant changes in recent years. For active learning (AL) purposes, NNs are, however, less commonly used -- despite their current popularity. By using the superior text classification performance of NNs for AL, we can either increase a model's performance using the same amount of data or reduce the data and therefore the required annotation efforts while keeping the same performance. We review AL for text classification using deep neural networks (DNNs) and elaborate on two main causes which used to hinder the adoption: (a) the inability of NNs to provide reliable uncertainty estimates, on which the most commonly used query strategies rely, and (b) the challenge of training DNNs on small data. To investigate the former, we construct a taxonomy of query strategies, which distinguishes between data-based, model-based, and prediction-based instance selection, and investigate the prevalence of these classes in recent research. Moreover, we review recent NN-based advances in NLP like word embeddings or language models in the context of (D)NNs, survey the current state-of-the-art at the intersection of AL, text classification, and DNNs and relate recent advances in NLP to AL. Finally, we analyze recent work in AL for text classification, connect the respective query strategies to the taxonomy, and outline commonalities and shortcomings. As a result, we highlight gaps in current research and present open research questions.
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Authors: Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen
Date: 2020-08-17 00:00:00
Tasks: Arrhythmia Detection, Diversity

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average $F_1$ score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.
BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense
Authors: Josef Jon, Martin Fajčík, Martin Dočekal, Pavel Smrž
Date: 2020-08-17 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Data Augmentation, Machine Translation, Reranking, Translation

This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 - Commonsense Validation and Explanation. We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation. We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs. We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss. In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low. We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle. We performed an error and dataset analysis for all subtasks and we present our findings.
FLBench: A Benchmark Suite for Federated Learning
Authors: Yuan Liang, Yange Guo, Yanxia Gong, Chunjie Luo, Jianfeng Zhan, Yunyou Huang
Date: 2020-08-17 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Privacy Preserving

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data island, while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly used public datasets into partitions to simulate real world isolated data island scenarios. Still, this simulation fails to capture real world isolated data island intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open sourced and in fast evolution. We package it as an automated deployment tool. The benchmark suite is available from https://www.benchcouncil.org/flbench.html.
An Improved Dilated Convolutional Network for Herd Counting in Crowded Scenes
Authors: Soufien Hamrouni, Hakim Ghazzai, Hamid Menouar, Yahya Massoud
Date: 2020-08-17 00:00:00
Tasks: Management

Crowd management technologies that leverage computer vision are widespread in contemporary times. There exists many security-related applications of these methods, including, but not limited to: following the flow of an array of people and monitoring large gatherings. In this paper, we propose an accurate monitoring system composed of two concatenated convolutional deep learning architectures. The first part called Front-end, is responsible for converting bi-dimensional signals and delivering high-level features. The second part, called the Back-end, is a dilated Convolutional Neural Network (CNN) used to replace pooling layers. It is responsible for enlarging the receptive field of the whole network and converting the descriptors provided by the first network to a saliency map that will be utilized to estimate the number of people in highly congested images. We also propose to utilize a genetic algorithm in order to find an optimized dilation rate configuration in the back-end. The proposed model is shown to converge 30\% faster than state-of-the-art approaches. It is also shown that it achieves 20\% lower Mean Absolute Error (MAE) when applied to the Shanghai data~set.
Exploring the weather impact on bike sharing usage through a clustering analysis
Authors: Jessica Quach, Reza Malekian
Date: 2020-08-17 00:00:00
Tasks: Clustering

Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.
Deep Learning Based Open Set Acoustic Scene Classification
Authors: Zuzanna Kwiatkowska, Beniamin Kalinowski, Michał Kośmider, Krzysztof Rykaczewski
Date: 2020-08-17 00:00:00
Tasks: Acoustic Scene Classification, Classification, Deep Learning, General Classification, Scene Classification

In this work, we compare the performance of three selected techniques in open set acoustic scenes classification (ASC). We test thresholding of the softmax output of a deep network classifier, which is the most popular technique nowadays employed in ASC. Further we compare the results with the Openmax classifier which is derived from the computer vision field. As the third model, we use the Adapted Class-Conditioned Autoencoder (Adapted C2AE) which is our variation of another computer vision related technique called C2AE. Adapted C2AE encompasses a more fair comparison of the given experiments and simplifies the original inference procedure, making it more applicable in the real-life scenarios. We also analyse two training scenarios: without additional knowledge of unknown classes and another where a limited subset of examples from the unknown classes is available. We find that the C2AE based method outperforms the thresholding and Openmax, obtaining $85.5\%$ Area Under the Receiver Operating Characteristic curve (AUROC) and $66\%$ of open set accuracy on data used in Detection and Classification of Acoustic Scenes and Events Challenge 2019 Task 1C.