pwc-archive/papers-with-abstracts huggingface dataset:
Pairwise Neural Machine Translation Evaluation
Authors: Francisco Guzman, Shafiq Joty, Lluis Marquez, Preslav Nakov
Date: 2019-12-05 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Machine Translation, Sentence, Sentence Embeddings, Translation

We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?
Authors: Aristotelis-Angelos Papadopoulos, Nazim Shaikh, Mohammad Reza Rajati
Date: 2019-12-05 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Deep neural networks are known to achieve superior results in classification tasks. However, it has been recently shown that they are incapable to detect examples that are generated by a distribution which is different than the one they have been trained on since they are making overconfident prediction for Out-Of-Distribution (OOD) examples. OOD detection has attracted a lot of attention recently. In this paper, we review some of the most seminal recent algorithms in the OOD detection field, we divide those methods into training and post-training and we experimentally show how the combination of the former with the latter can achieve state-of-the-art results in the OOD detection task.
New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice
Authors: Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang
Date: 2019-12-05 00:00:00

This work addresses the classic machine learning problem of online prediction with expert advice. A new potential-based framework for the fixed horizon version of this problem has been recently developed using verification arguments from optimal control theory. This paper extends this framework to the random (geometric) stopping version. To obtain explicit bounds, we construct potentials for the geometric version from potentials used for the fixed horizon version of the problem. This construction leads to new explicit lower and upper bounds associated with specific adversary and player strategies. While there are several known lower bounds in the fixed horizon setting, our lower bounds appear to be the first such results in the geometric stopping setting with an arbitrary number of experts. Our framework also leads in some cases to improved upper bounds. For two and three experts, our bounds are optimal to leading order.
Learnt dynamics generalizes across tasks, datasets, and populations
Authors: U. Mahmood, M. M . Rahman, A. Fedorov, Z. Fu, V. D. Calhoun, S. M. Plis
Date: 2019-12-04 00:00:00

Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the problem---signal dynamics---can be used to our advantage and noticeably improve classification performance on a range of discrimination tasks when training data is scarce. We demonstrate that self-supervised pre-training guided by signal dynamics produces embedding that generalizes across tasks, datasets, data collection sites, and data distributions. We perform an extensive evaluation of this approach on a range of tasks including simulated data, keyword detection problem, and a range of functional neuroimaging data, where we show that a single embedding learnt on healthy subjects generalizes across a number of disorders, age groups, and datasets.
Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities
Authors: Zimin Chen, Steve Kommrusch, Martin Monperrus
Date: 2019-12-04 00:00:00

Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them. In this article, we contribute a new technique for automatic vulnerability fixing. We present a system that uses the rich software development history that can be found on GitHub to train an AI system that generates patches. We apply sequence-to-sequence learning on a big dataset of code changes and we evaluate the trained system on real world vulnerabilities from the CVE database. The result shows the feasibility of using sequence-to-sequence learning for fixing software vulnerabilities.
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
Authors: Guocheng Qian, Abdulellah Abualshour, Guohao Li, Ali Thabet, Bernard Ghanem
Date: 2019-11-30 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Reconstruction, Point Cloud Super Resolution, point cloud upsampling

The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves state-of-the-art upsampling methods. For feature extraction, we also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds. We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.
Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications
Authors: Suguman Bansal, Yong Li, Lucas M. Tabajara, Moshe Y. Vardi
Date: 2019-11-19 00:00:00

LTLf synthesis is the automated construction of a reactive system from a high-level description, expressed in LTLf, of its finite-horizon behavior. So far, the conversion of LTLf formulas to deterministic finite-state automata (DFAs) has been identified as the primary bottleneck to the scalabity of synthesis. Recent investigations have also shown that the size of the DFA state space plays a critical role in synthesis as well. Therefore, effective resolution of the bottleneck for synthesis requires the conversion to be time and memory performant, and prevent state-space explosion. Current conversion approaches, however, which are based either on explicit-state representation or symbolic-state representation, fail to address these necessities adequately at scale: Explicit-state approaches generate minimal DFA but are slow due to expensive DFA minimization. Symbolic-state representations can be succinct, but due to the lack of DFA minimization they generate such large state spaces that even their symbolic representations cannot compensate for the blow-up. This work proposes a hybrid representation approach for the conversion. Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths. In doing so, we offer an LTLf to DFA conversion technique that addresses all three necessities, hence resolving the bottleneck. A comprehensive empirical evaluation on conversion and synthesis benchmarks supports the merits of our hybrid approach.
Multi-task Temporal Convolutional Network for Predicting Water Quality Sensor
Authors: Zhang, Yifan; Thorburn, Peter; Fitch, Peter
Date: 2019-12-05 00:00:00
Proceeding: International Conference on Neural Information Processing (ICONIP 2019) 2019 12
Tasks: Management, Time Series Prediction

Predicting the trend of water quality is essential in environmental management decision support systems. Despite various data-driven models in water quality prediction, most studies focus on predicting a single water quality variable. When multiple water quality variables need to be estimated, preparing several data-driven models may require unaffordable computing resources. Also, the changing patterns of several water quality variables can only be revealed by processing long term historical observations, which is not well supported by conventional data-driven models. In this paper, we propose a multi-task temporal convolution network (MTCN) for predicting multiple water quality variables. The temporal convolution offers one the capability to explore the temporal dependencies among a remarkably long historical period. Furthermore, instead of providing predictions for only one water quality variable, the MTCN is designed to predict multiple water quality variables simultaneously. Data collected from the Burnett River, Queensland is used to evaluate the MTCN. Compared to training a set of single-task TCNs for each variable separately, the proposed MTCN achieves the best RMSE scores in predicting both temperature and DO in the following 48 time steps but only requires 53% of the total training time of the TCN. Therefore, the MTCN is an encouraging approach for water quality management by processing a large amount of sensor data.
Personalizing Similar Product Recommendations in Fashion E-commerce
Authors: Pankaj Agarwal, Sreekanth Vempati, Sumit Borar
Date: 2018-06-29 00:00:00
Tasks: Collaborative Filtering

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion e-commerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.
ReacNetGenerator: an Automatic Reaction Network Generator for Reactive Molecular Dynamic Simulations
Authors: Jinzhe Zeng, Liqun Cao, Chih-Hao Chin, Haisheng Ren, John Z.H. Zhang, Tong Zhu
Date: 2019-11-26 00:00:00
Proceeding: Physical Chemistry Chemical Physics 2019 11

Reactive molecular dynamics (MD) simulation makes it possible to study the reaction mechanisms of complex reaction systems at the atomic level. However, the analysis of the MD trajectories which contain thousands of species and reaction pathways has become a major obstacle to the application of reactive MD simulation in large-scale systems. Here, we report the development and application of the Reaction Network Generator (ReacNetGenerator) method. It can automatically extract the reaction network from the reaction trajectory without any predefined reaction coordinates and elementary reaction steps. Molecular species can be automatically identified from the cartesian coordinates of atoms and the hidden Markov model is used to filter the trajectory noises which makes the analysis process easier and more accurate. The ReacNetGenerator has been successfully used to analyze the reactive MD trajectories of the combustion of methane and 4-component surrogate fuel for rocket propellant 3 (RP-3), and it has great advantages in efficiency and accuracy compared to traditional manual analysis.
Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain
Authors: Yunfeng Zhao, Huiyu Zhou, Chris Elliott, Karen Rafferty
Date: 2018-12-06 00:00:00
Proceeding: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2018 12
Tasks: Camera Calibration, Color Constancy, Object Recognition

Achieving color constancy between and within images, i.e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition. Most current methods attempt to solve this by illumination correction on perceptual color spaces. In this paper, we proposed two pixel-wise algorithms to achieve relative color constancy by working under the spectral domain. That is, the proposed algorithms map each pixel to the reflectance ratio of objects appeared in the scene and perform illumination correction in this spectral domain. Also, we proposed a camera calibration technique that calculates the characteristics of a camera without the need of a standard reference. We show that both of the proposed algorithms achieved the best performance on nonuniform illumination correction and relative illumination matching respectively compared to the benchmarked algorithms.
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors
Authors: Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
Date: 2019-12-09 00:00:00
Tasks: Denoising

We propose to study the generalization error of a learned predictor $\hat h$ in terms of that of a surrogate (potentially randomized) predictor that is coupled to $\hat h$ and designed to trade empirical risk for control of generalization error. In the case where $\hat h$ interpolates the data, it is interesting to consider theoretical surrogate classifiers that are partially derandomized or rerandomized, e.g., fit to the training data but with modified label noise. We also show that replacing $\hat h$ by its conditional distribution with respect to an arbitrary $\sigma$-field is a convenient way to derandomize. We study two examples, inspired by the work of Nagarajan and Kolter (2019) and Bartlett et al. (2019), where the learned classifier $\hat h$ interpolates the training data with high probability, has small risk, and, yet, does not belong to a nonrandom class with a tight uniform bound on two-sided generalization error. At the same time, we bound the risk of $\hat h$ in terms of surrogates constructed by conditioning and denoising, respectively, and shown to belong to nonrandom classes with uniformly small generalization error.
A time resolved clustering method revealing longterm structures and their short-term internal dynamics
Authors: Jonas I. Liechti, Sebastian Bonhoeffer
Date: 2019-12-09 00:00:00
Tasks: Clustering, Time Series Clustering

The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource. Their value comes with the ability to extract meaningful patterns that are of economic, societal or scientific relevance. A particular challenge is the identification of patterns across time, including those that might only become apparent when the temporal dimension is taken into account. Here, we present a novel method that aims to achieve this by detecting dynamic clusters, i.e. structural elements that can be present over prolonged durations. It is based on an adaptive identification of majority overlaps between groups at different time points and accommodates the transient decompositions in otherwise persistent dynamic clusters. Our method enables the detection of persistent structural elements with internal dynamics and can be applied to any classifiable data, ranging from social contact networks to arbitrary sets of time stamped feature vectors. It represents a unique tool to study systems with non-trivial temporal dynamics and has a broad applicability to scientific, societal and economic data.
Side-Aware Boundary Localization for More Precise Object Detection
Authors: Jiaqi Wang, Wenwei Zhang, Yuhang Cao, Kai Chen, Jiangmiao Pang, Tao Gong, Jianping Shi, Chen Change Loy, Dahua Lin
Date: 2019-12-09 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object, object-detection, Object Detection, regression

Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placement of each side of the bounding box. However, the mainstream approach, which focuses on predicting centers and sizes, is not the most effective way to accomplish this task, especially when there exists displacements with large variance between the anchors and the targets. In this paper, we propose an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch. To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We test the proposed method on both two-stage and single-stage detection frameworks. Replacing the standard bounding box regression branch with the proposed design leads to significant improvements on Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%, respectively. Code is available at https://github.com/open-mmlab/mmdetection.
An Empirical Study on Position of the Batch Normalization Layer in Convolutional Neural Networks
Authors: Moein Hasani, Hassan Khotanlou
Date: 2019-12-09 00:00:00
Tasks: Position

In this paper, we have studied how the training of the convolutional neural networks (CNNs) can be affected by changing the position of the batch normalization (BN) layer. Three different convolutional neural networks have been chosen for our experiments. These networks are AlexNet, VGG-16, and ResNet- 20. We show that the speed up in training provided by the BN algorithm can be improved by using other positions for the BN layer than the one suggested by its original paper. Also, we discuss how the BN layer in a certain position can aid the training of one network but not the other. Three different positions for the BN layer have been studied in this research. These positions are: the BN layer between the convolution layer and the non-linear activation function, the BN layer after the non-linear activation function and finally, the BN layer before each of the convolutional layers.
Automated Classification of Helium Ingress in Irradiated X-750
Authors: Chris Anderson, Jacob Klein, Heygaan Rajakumar, Colin Judge, Laurent K Beland
Date: 2019-12-09 00:00:00
Tasks: Classification, General Classification

Imaging nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and labour intensive manual process. It is a prime candidate for automation. Here, a region-based convolutional neural network is adapted to detect helium bubbles in micrographs of neutron-irradiated Inconel X-750 reactor spacer springs. We demonstrate that this neural network produces analyses of similar accuracy and reproducibility to that produced by humans. Further, we show this method as being four orders of magnitude faster than manual analysis allowing for generation of significant quantities of data. The proposed method can be used with micrographs of different Fresnel contrasts and magnification levels.
Self-supervised Object Motion and Depth Estimation from Video
Authors: Qi Dai, Vaishakh Patil, Simon Hecker, Dengxin Dai, Luc van Gool, Konrad Schindler
Date: 2019-12-09 00:00:00
Tasks: Depth Estimation, Instance Segmentation, motion prediction, Object, Optical Flow Estimation, Prediction, Self-Supervised Learning, Semantic Segmentation

We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-of-freedom rigid-body transformation. The instance segmentation mask is leveraged to introduce the information of object. Compared with methods which predict dense optical flow map to model the motion, our approach significantly reduces the number of values to be estimated. Our system eliminates the scale ambiguity of motion prediction through imposing a novel geometric constraint loss term. Experiments on KITTI driving dataset demonstrate our system is capable to capture the object motion without external annotation. Our system outperforms previous self-supervised approaches in terms of 3D scene flow prediction, and contribute to the disparity prediction in dynamic area.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans
Authors: Taimur Hassan, Salman H. Khan, Samet Akcay, Mohammed Bennamoun, Naoufel Werghi
Date: 2019-12-09 00:00:00

In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and inefficient process. Hence, many researchers have developed Xray imagery-based autonomous systems to address these shortcomings. However, to the best of our knowledge, there is no framework, up to now, that can recognize heavily occluded and cluttered baggage items from multi-vendor X-ray scans. This paper presents a cascaded structure tensor framework which can automatically extract and recognize suspicious items irrespective of their position and orientation in the multi-vendor X-ray scans. The proposed framework is unique, as it intelligently extracts each object by iteratively picking contour based transitional information from different orientations and uses only a single feedforward convolutional neural network for the recognition. The proposed framework has been rigorously tested on publicly available GDXray and SIXray datasets containing a total of 1,067,381 X-ray scans where it significantly outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 and 0.9595 for extracting and recognizing suspicious items from GDXray and SIXray scans, respectively. Furthermore, the proposed framework has achieved 15.78% better time