pwc-archive/papers-with-abstracts huggingface dataset:
Tangent Space Separability in Feedforward Neural Networks
Authors: Bálint Daróczy, Rita Aleksziev, András Benczúr
Date: 2019-12-18 00:00:00

Hierarchical neural networks are exponentially more efficient than their corresponding "shallow" counterpart with the same expressive power, but involve huge number of parameters and require tedious amounts of training. By approximating the tangent subspace, we suggest a sparse representation that enables switching to shallow networks, GradNet after a very early training stage. Our experiments show that the proposed approximation of the metric improves and sometimes even surpasses the achievable performance of the original network significantly even after a few epochs of training the original feedforward network.
$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Authors: Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert
Date: 2019-12-18 00:00:00
Tasks: Image Enhancement, Image Reconstruction, Learning Theory, Sensitivity

Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning. Theory and Methods: MR image reconstruction is formulated as learned unrolled optimization scheme with a Down-Up network as regularization and varying data consistency layers. The different architectures are split into sensitivity networks, which rely on explicit coil sensitivity maps, and parallel coil networks, which learn the combination of coils implicitly. Different content and adversarial losses, a semi-supervised fine-tuning scheme and model ensembling are investigated. Results: Evaluated on the fastMRI multicoil validation set, architectures involving raw k-space data outperform image enhancement methods significantly. Semi-supervised fine-tuning adapts to new k-space data and provides, together with reconstructions based on adversarial training, the visually most appealing results although quantitative quality metrics are reduced. The $\Sigma$-net ensembles the benefits from different models and achieves similar scores compared to the single state-of-the-art approaches. Conclusion: This work provides an open-source framework to perform a systematic wide-range comparison of state-of-the-art reconstruction approaches for parallel MR image reconstruction on the fastMRI knee dataset and explores the importance of data consistency. A suitable trade-off between perceptual image quality and quantitative scores are achieved with the ensembled $\Sigma$-net.
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks
Authors: Simon Msika, Alejandro Quintero, Foutse khomh
Date: 2019-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection

An Intrusion Detection System (IDS) is a key cybersecurity tool for network administrators as it identifies malicious traffic and cyberattacks. With the recent successes of machine learning techniques such as deep learning, more and more IDS are now using machine learning algorithms to detect attacks faster. However, these systems lack robustness when facing previously unseen types of attacks. With the increasing number of new attacks, especially against Internet of Things devices, having a robust IDS able to spot unusual and new attacks becomes necessary. This work explores the possibility of leveraging generative adversarial models to improve the robustness of machine learning based IDS. More specifically, we propose a new method named SIGMA, that leverages adversarial examples to strengthen IDS against new types of attacks. Using Generative Adversarial Networks (GAN) and metaheuristics, SIGMA %Our method consists in generates adversarial examples, iteratively, and uses it to retrain a machine learning-based IDS, until a convergence of the detection rate (i.e. until the detection system is not improving anymore). A round of improvement consists of a generative phase, in which we use GANs and metaheuristics to generate instances ; an evaluation phase in which we calculate the detection rate of those newly generated attacks ; and a training phase, in which we train the IDS with those attacks. We have evaluated the SIGMA method for four standard machine learning classification algorithms acting as IDS, with a combination of GAN and a hybrid local-search and genetic algorithm, to generate new datasets of attacks. Our results show that SIGMA can successfully generate adversarial attacks against different machine learning based IDS. Also, using SIGMA, we can improve the performance of an IDS to up to 100\% after as little as two rounds of improvement.
Research Frontiers in Transfer Learning -- a systematic and bibliometric review
Authors: Frederico Guth, Teofilo Emidio de-Campos
Date: 2019-12-18 00:00:00
Tasks: Transfer Learning

Humans can learn from very few samples, demonstrating an outstanding generalization ability that learning algorithms are still far from reaching. Currently, the most successful models demand enormous amounts of well-labeled data, which are expensive and difficult to obtain, becoming one of the biggest obstacles to the use of machine learning in practice. This scenario shows the massive potential for Transfer Learning, which aims to harness previously acquired knowledge to the learning of new tasks more effectively and efficiently. In this systematic review, we apply a quantitative method to select the main contributions to the field and make use of bibliographic coupling metrics to identify research frontiers. We further analyze the linguistic variation between the classics of the field and the frontier and map promising research directions.
Image Restoration using Plug-and-Play CNN MAP Denoisers
Authors: Siavash Bigdeli, David Honzátko, Sabine Süsstrunk, L. Andrea Dunbar
Date: 2019-12-18 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.
Feature-wise change detection and robust indoor positioning using RANSAC-like approach
Authors: Caifa Zhou
Date: 2019-12-18 00:00:00
Tasks: Change Detection

Fingerprinting-based positioning, one of the promising indoor positioning solutions, has been broadly explored owing to the pervasiveness of sensor-rich mobile devices, the prosperity of opportunistically measurable location-relevant signals and the progress of data-driven algorithms. One critical challenge is to controland improve the quality of the reference fingerprint map (RFM), which is built at the offline stage and applied for online positioning. The key concept concerningthe quality control of the RFM is updating the RFM according to the newly measured data. Though varies methods have been proposed for adapting the RFM, they approach the problem by introducing extra-positioning schemes (e.g. PDR orUGV) and directly adjust the RFM without distinguishing whether critical changes have occurred. This paper aims at proposing an extra-positioning-free solution by making full use of the redundancy of measurable features. Loosely inspired by random sampling consensus (RANSAC), arbitrarily sampled subset of features from the online measurement are used for generating multi-resamples, which areused for estimating the intermediate locations. In the way of resampling, it can mitigate the impact of the changed features on positioning and enables to retrieve accurate location estimation. The users location is robustly computed by identifying the candidate locations from these intermediate ones using modified Jaccardindex (MJI) and the feature-wise change belief is calculated according to the world model of the RFM and the estimated variability of features. In order to validate our proposed approach, two levels of experimental analysis have been carried out. On the simulated dataset, the average change detection accuracy is about 90%. Meanwhile, the improvement of positioning accuracy within 2 m is about 20% by dropping out the features that are detected as changed when performing positioning comparing to that of using all measured features for location estimation. On the long-term collected dataset, the average change detection accuracy is about 85%.
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data
Authors: Alexander Gepperth, Benedikt Pfülb
Date: 2019-12-18 00:00:00

We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus be trained based on a random initialization. Furthermore, the approach allows mini-batch sizes as low as 1, which are typical for streaming-data settings. Major problems in such settings are undesirable local optima during early training phases and numerical instabilities due to high data dimensionalities. We introduce an adaptive annealing procedure to address the first problem, whereas numerical instabilities are eliminated by using an exponential-free approximation to the standard GMM log-likelihood. Experiments on a variety of visual and non-visual benchmarks show that our SGD approach can be trained completely without, for instance, k-means based centroid initialization. It also compares favorably to an online variant of Expectation-Maximization (EM) - stochastic EM (sEM), which it outperforms by a large margin for very high-dimensional data.
MG-WFBP: Merging Gradients Wisely for Efficient Communication in Distributed Deep Learning
Authors: Shaohuai Shi, Xiaowen Chu, Bo Li
Date: 2019-12-18 00:00:00
Tasks: GPU, Playing the Game of 2048

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks (DNNs) on computer clusters. With the increase of computational power, network communications generally limit the system scalability. Wait-free backpropagation (WFBP) is a popular solution to overlap communications with computations during the training process. In this paper, we observe that many DNNs have a large number of layers with only a small amount of data to be communicated at each layer in distributed training, which could make WFBP inefficient. Based on the fact that merging some short communication tasks into a single one can reduce the overall communication time, we formulate an optimization problem to minimize the training time in pipelining communications and computations. We derive an optimal solution that can be solved efficiently without affecting the training performance. We then apply the solution to propose a distributed training algorithm named merged-gradient WFBP (MG-WFBP) and implement it in two platforms Caffe and PyTorch. Extensive experiments in three GPU clusters are conducted to verify the effectiveness of MG-WFBP. We further exploit trace-based simulations of 4 to 2048 GPUs to explore the potential scaling efficiency of MG-WFBP. Experimental results show that MG-WFBP achieves much better scaling performance than existing methods.
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)
Authors: Tianyu Zhang, Lei Zhu, Qian Zhao, Kilho Shin
Date: 2019-12-18 00:00:00
Tasks: Quantization

Quantization of weights of deep neural networks (DNN) has proven to be an effective solution for the purpose of implementing DNNs on edge devices such as mobiles, ASICs and FPGAs, because they have no sufficient resources to support computation involving millions of high precision weights and multiply-accumulate operations. This paper proposes a novel method to compress vectors of high precision weights of DNNs to ternary vectors, namely a cosine similarity based target non-retraining ternary (TNT) compression method. Our method leverages cosine similarity instead of Euclidean distances as commonly used in the literature and succeeds in reducing the size of the search space to find optimal ternary vectors from 3N to N, where N is the dimension of target vectors. As a result, the computational complexity for TNT to find theoretically optimal ternary vectors is only O(N log(N)). Moreover, our experiments show that, when we ternarize models of DNN with high precision parameters, the obtained quantized models can exhibit sufficiently high accuracy so that re-training models is not necessary.
Learning Regional Attraction for Line Segment Detection
Authors: Nan Xue, Song Bai, Fu-Dong Wang, Gui-Song Xia, Tianfu Wu, Liangpei Zhang, Philip H. S. Torr
Date: 2019-12-18 00:00:00
Tasks: Line Segment Detection

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.
Field Label Prediction for Autofill in Web Browsers
Authors: Joy Bose
Date: 2019-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Form, Prediction

Automatic form fill is an important productivity related feature present in major web browsers, which predicts the field labels of a web form and automatically fills values in a new form based on the values previously filled for the same field in other forms. This feature increases the convenience and efficiency of users who have to fill similar information in fields in multiple forms. In this paper we describe a machine learning solution for predicting the form field labels, implemented as a web service using Azure ML Studio.
Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection
Authors: Artem Lutov, Dingqi Yang, Philippe Cudré-Mauroux
Date: 2019-12-17 00:00:00
Tasks: Community Detection, Graph Embedding, Graph Representation Learning, Link Prediction, Node Classification, Representation Learning, Stochastic Optimization

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of embedding dimensions. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.
AI and Holistic Review: Informing Human Reading in College Admissions
Authors: AJ Alvero, Noah Arthurs, anthony lising antonio, Benjamin W. Domingue, Ben Gebre-Medhin, Sonia Giebel, Mitchell L. Stevens
Date: 2019-12-17 00:00:00
Tasks: Fairness

College admissions in the United States is carried out by a human-centered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings.
Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models
Authors: Sheroze Sheriffdeen, Jean C. Ragusa, Jim E. Morel, Marvin L. Adams, Tan Bui-Thanh
Date: 2019-12-17 00:00:00

Inverse problems are pervasive mathematical methods in inferring knowledge from observational and experimental data by leveraging simulations and models. Unlike direct inference methods, inverse problem approaches typically require many forward model solves usually governed by Partial Differential Equations (PDEs). This a crucial bottleneck in determining the feasibility of such methods. While machine learning (ML) methods, such as deep neural networks (DNNs), can be employed to learn nonlinear forward models, designing a network architecture that preserves accuracy while generalizing to new parameter regimes is a daunting task. Furthermore, due to the computation-expensive nature of forward models, state-of-the-art black-box ML methods would require an unrealistic amount of work in order to obtain an accurate surrogate model. On the other hand, standard Reduced-Order Models (ROMs) accurately capture supposedly important physics of the forward model in the reduced subspaces, but otherwise could be inaccurate elsewhere. In this paper, we propose to enlarge the validity of ROMs and hence improve the accuracy outside the reduced subspaces by incorporating a data-driven ML technique. In particular, we focus on a goal-oriented approach that substantially improves the accuracy of reduced models by learning the error between the forward model and the ROM outputs. Once an ML-enhanced ROM is constructed it can accelerate the performance of solving many-query problems in parametrized forward and inverse problems. Numerical results for inverse problems governed by elliptic PDEs and parametrized neutron transport equations will be presented to support our approach.
Logarithmic Regret in Multisecretary and Online Linear Programs with Continuous Valuations
Authors: Robert L. Bray
Date: 2019-12-16 00:00:00
Tasks: Management

I study how the shadow prices of a linear program that allocates an endowment of $n\beta \in \mathbb{R}^{m}$ resources to $n$ customers behave as $n \rightarrow \infty$. I show the shadow prices (i) adhere to a concentration of measure, (ii) converge to a multivariate normal under central-limit-theorem scaling, and (iii) have a variance that decreases like $\Theta(1/n)$. I use these results to prove that the expected regret in \cites{Li2019b} online linear program is $\Theta(\log n)$, both when the customer variable distribution is known upfront and must be learned on the fly. I thus tighten \citeauthors{Li2019b} upper bound from $O(\log n \log \log n)$ to $O(\log n)$, and extend \cites{Lueker1995} $\Omega(\log n)$ lower bound to the multi-dimensional setting. I illustrate my new techniques with a simple analysis of \cites{Arlotto2019} multisecretary problem.
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
Authors: Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik Scheme, François Laviolette, Benoit Gosselin
Date: 2019-12-16 00:00:00
Tasks: EMG Gesture Recognition, Gesture Recognition, Transfer Learning

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p<0.05) outperforms using fine-tuning as the recalibration technique.