pwc-archive/papers-with-abstracts huggingface dataset:
Eliminating Catastrophic Interference with Biased Competition
Authors: Amelia Elizabeth Pollard, Jonathan L. Shapiro
Date: 2020-07-03 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We present here a model to take advantage of the multi-task nature of complex datasets by learning to separate tasks and subtasks in and end to end manner by biasing competitive interactions in the network. This method does not require additional labelling or reformatting of data in a dataset. We propose an alternate view to the monolithic one-task-fits-all learning of multi-task problems, and describe a model based on a theory of neuronal attention from neuroscience, proposed by Desimone. We create and exhibit a new toy dataset, based on the MNIST dataset, which we call MNIST-QA, for testing Visual Question Answering architectures in a low-dimensional environment while preserving the more difficult components of the Visual Question Answering task, and demonstrate the proposed network architecture on this new dataset, as well as on COCO-QA and DAQUAR-FULL. We then demonstrate that this model eliminates catastrophic interference between tasks on a newly created toy dataset and provides competitive results in the Visual Question Answering space. We provide further evidence that Visual Question Answering can be approached as a multi-task problem, and demonstrate that this new architecture based on the Biased Competition model is capable of learning to separate and learn the tasks in an end-to-end fashion without the need for task labels.
Weak SINDy For Partial Differential Equations
Authors: Daniel A. Messenger, David M. Bortz
Date: 2020-07-06 00:00:00

Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system discovery that has been shown to successfully recover governing dynamical systems from data (Brunton et al., PNAS, '16; Rudy et al., Sci. Adv. '17). Recently, several groups have independently discovered that the weak formulation provides orders of magnitude better robustness to noise. Here we extend our Weak SINDy (WSINDy) framework introduced in (arXiv:2005.04339) to the setting of partial differential equations (PDEs). The elimination of pointwise derivative approximations via the weak form enables effective machine-precision recovery of model coefficients from noise-free data (i.e. below the tolerance of the simulation scheme) as well as robust identification of PDEs in the large noise regime (with signal-to-noise ratio approaching one in many well-known cases). This is accomplished by discretizing a convolutional weak form of the PDE and exploiting separability of test functions for efficient model identification using the Fast Fourier Transform. The resulting WSINDy algorithm for PDEs has a worst-case computational complexity of $\mathcal{O}(N^{D+1}\log(N))$ for datasets with $N$ points in each of $D+1$ dimensions (i.e. $\mathcal{O}(\log(N))$ operations per datapoint). Furthermore, our Fourier-based implementation reveals a connection between robustness to noise and the spectra of test functions, which we utilize in an \textit{a priori} selection algorithm for test functions. Finally, we introduce a learning algorithm for the threshold in sequential-thresholding least-squares (STLS) that enables model identification from large libraries, and we utilize scale-invariance at the continuum level to identify PDEs from poorly-scaled datasets. We demonstrate WSINDy's robustness, speed and accuracy on several challenging PDEs.
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
Authors: O. Kechagias-Stamatis, N. Aouf
Date: 2020-07-04 00:00:00
Tasks: Benchmarking, Survey

Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along with a direct comparison of the strengths and weaknesses of each method under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset we also highlight its weaknesses and suggest future research directions.
Gradient Origin Networks
Authors: Sam Bond-Taylor, Chris G. Willcocks
Date: 2020-07-06 00:00:00
Proceeding: ICLR 2021 1
Tasks: Image Generation, Image Reconstruction

This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder. This is achieved using empirical Bayes to calculate the expectation of the posterior, which is implemented by initialising a latent vector with zeros, then using the gradient of the log-likelihood of the data with respect to this zero vector as new latent points. The approach has similar characteristics to autoencoders, but with a simpler architecture, and is demonstrated in a variational autoencoder equivalent that permits sampling. This also allows implicit representation networks to learn a space of implicit functions without requiring a hypernetwork, retaining their representation advantages across datasets. The experiments show that the proposed method converges faster, with significantly lower reconstruction error than autoencoders, while requiring half the parameters.
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
Authors: Liwei Wang, Jing Huang, Yin Li, Kun Xu, Zhengyuan Yang, Dong Yu
Date: 2020-07-03 00:00:00
Proceeding: CVPR 2021 1
Tasks: Contrastive Learning, Knowledge Distillation, Object, object-detection, Object Detection, Phrase Grounding, Sentence, Visual Grounding

Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a contrastive learning framework that accounts for both region-phrase and image-sentence matching. Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed. Importantly, our region-phrase score function is learned by distilling from soft matching scores between the detected object names and candidate phrases within an image-sentence pair, while the image-sentence score function is supervised by ground-truth image-sentence pairs. The design of such score functions removes the need of object detection at test time, thereby significantly reducing the inference cost. Without bells and whistles, our approach achieves state-of-the-art results on visual phrase grounding, surpassing previous methods that require expensive object detectors at test time.
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
Authors: Fengbei Liu, Yaqub Jonmohamadi, Gabriel Maicas, Ajay K. Pandey, Gustavo Carneiro
Date: 2020-07-05 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Segmentation, Semantic Segmentation

Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging conditions (e.g., low texture, overexposure, etc.), automatic semantic segmentation is a challenging scenario, which justifies the scarce literature on this topic. In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy. To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. We fine-tune such model to produce both the semantic segmentation and self-supervised monocular depth using stereo arthroscopic images taken from inside the knee. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation.
HoughNet: Integrating near and long-range evidence for bottom-up object detection
Authors: Nermin Samet, Samet Hicsonmez, Emre Akbas
Date: 2020-07-05 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Generation, Object, object-detection, Object Detection

This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet's best model achieves 46.4 $AP$ (and 65.1 $AP_{50}$), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in another task, namely, "labels to photo" image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases. Code is available at https://github.com/nerminsamet/houghnet.
Estimation of Ground Contacts from Human Gait by a Wearable Inertial Measurement Unit using machine learning
Authors: Muhammad Junaid Umer, Qaiser Riaz
Date: 2020-07-05 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Robotics system for rehabilitation of movement disorders and motion assistance are gaining increased intention. In this scenario estimation of ground contact is an active area of research in robotics and healthcare. This article addresses the estimation and classification of right and left foot during the healthy human gait based on the IMU sensor data of chest and lower back. For this purpose we have collected an IMU data of 48 subjects by using two smartphones at chest and lower back of the human body and one smart watch at right ankle of the body. To show the robustness of our approach data was collected at six different surfaces (road tiles carpet grass concrete and soil). The recorded data of lower back and chest sensor was segmented into single steps on the basis of right ankle sensor data, then we computed a total of 408 features from time frequency and wavelet domain of each segmented step. For classification task we have trained two machine learning classifiers SVM and RF with 10 fold cross validation method. We performed classification experiments at individual surfaces, hard surfaces, soft surfaces and all surfaces, highest accuracy was achieved at individual surfaces with accuracy index of 98.88%. Furthermore, classification rate at hard soft and all surface are 95.60%, 94.38% and 95.05% respectively. The results shows that estimation of ground contact form normal human walk at different surfaces can be performed with high accuracy using 6D data of angular velocities and accelerations from chest and lower back location of the body.
Faster Graph Embeddings via Coarsening
Authors: Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang
Date: 2020-07-06 00:00:00
Proceeding: ICML 2020 1
Tasks: Link Prediction, Node Classification

Graph embeddings are a ubiquitous tool for machine learning tasks, such as node classification and link prediction, on graph-structured data. However, computing the embeddings for large-scale graphs is prohibitively inefficient even if we are interested only in a small subset of relevant vertices. To address this, we present an efficient graph coarsening approach, based on Schur complements, for computing the embedding of the relevant vertices. We prove that these embeddings are preserved exactly by the Schur complement graph that is obtained via Gaussian elimination on the non-relevant vertices. As computing Schur complements is expensive, we give a nearly-linear time algorithm that generates a coarsened graph on the relevant vertices that provably matches the Schur complement in expectation in each iteration. Our experiments involving prediction tasks on graphs demonstrate that computing embeddings on the coarsened graph, rather than the entire graph, leads to significant time savings without sacrificing accuracy.
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
Authors: Péter Mernyei, Cătălina Cangea
Date: 2020-07-06 00:00:00
Tasks: Articles, Benchmarking, Link Prediction, Node Classification

We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding to Computer Science articles, with edges based on hyperlinks and 10 classes representing different branches of the field. We use the dataset to evaluate semi-supervised node classification and single-relation link prediction models. Our experiments show that these methods perform well on a new domain, with structural properties different from earlier benchmarks. The dataset is publicly available, along with the implementation of the data pipeline and the benchmark experiments, at https://github.com/pmernyei/wiki-cs-dataset .
Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops
Authors: Shengyang Chen, Chih-Yung Wen, Yajing Zou, Wu Chen
Date: 2020-07-05 00:00:00
Tasks: Pose Estimation

In this paper, we present a novel stereo visual inertial pose estimation method. Compared to the widely used filter-based or optimization-based approaches, the pose estimation process is modeled as a control system. Designed feedback or feedforward loops are introduced to achieve the stable control of the system, which include a gradient decreased feedback loop, a roll-pitch feed forward loop and a bias estimation feedback loop. This system, named FLVIS (Feedforward-feedback Loop-based Visual Inertial System), is evaluated on the popular EuRoc MAV dataset. FLVIS achieves high accuracy and robustness with respect to other state-of-the-art visual SLAM approaches. The system has also been implemented and tested on a UAV platform. The source code of this research is public to the research community.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning
Authors: Tianyu Shi, Jiawei Wang, Yuankai Wu, Luis Miranda-Moreno, Lijun Sun
Date: 2020-07-06 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning (RL)

Connected and automated vehicles (CAVs) have attracted more and more attention recently. The fast actuation time allows them having the potential to promote the efficiency and safety of the whole transportation system. Due to technical challenges, there will be a proportion of vehicles that can be equipped with automation while other vehicles are without automation. Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow. One of state of the art method is using reinforcement learning to learn intelligent decision making policy. However, direct reinforcement learning framework cannot improve the performance of the whole system. In this article, we demonstrate that considering the problem in multi-agent setting with shared policy can help achieve better system performance than non-shared policy in single-agent setting. Furthermore, we find that utilization of attention mechanism on interaction features can capture the interplay between each agent in order to boost cooperation. To the best of our knowledge, while previous automated driving studies mainly focus on enhancing individual's driving performance, this work serves as a starting point for research on system-level multi-agent cooperation performance using graph information sharing. We conduct extensive experiments in car-following and unsignalized intersection settings. The results demonstrate that CAVs controlled by our method can achieve the best performance against several state of the art baselines.
Block Model Guided Unsupervised Feature Selection
Authors: Zilong Bai, Hoa Nguyen, Ian Davidson
Date: 2020-07-05 00:00:00
Tasks: Clustering, feature selection, model

Feature selection is a core area of data mining with a recent innovation of graph-driven unsupervised feature selection for linked data. In this setting we have a dataset $\mathbf{Y}$ consisting of $n$ instances each with $m$ features and a corresponding $n$ node graph (whose adjacency matrix is $\mathbf{A}$) with an edge indicating that the two instances are similar. Existing efforts for unsupervised feature selection on attributed networks have explored either directly regenerating the links by solving for $f$ such that $f(\mathbf{y}_i,\mathbf{y}_j) \approx \mathbf{A}_{i,j}$ or finding community structure in $\mathbf{A}$ and using the features in $\mathbf{Y}$ to predict these communities. However, graph-driven unsupervised feature selection remains an understudied area with respect to exploring more complex guidance. Here we take the novel approach of first building a block model on the graph and then using the block model for feature selection. That is, we discover $\mathbf{F}\mathbf{M}\mathbf{F}^T \approx \mathbf{A}$ and then find a subset of features $\mathcal{S}$ that induces another graph to preserve both $\mathbf{F}$ and $\mathbf{M}$. We call our approach Block Model Guided Unsupervised Feature Selection (BMGUFS). Experimental results show that our method outperforms the state of the art on several real-world public datasets in finding high-quality features for clustering.
Consistency analysis of bilevel data-driven learning in inverse problems
Authors: Neil K. Chada, Claudia Schillings, Xin T. Tong, Simon Weissmann
Date: 2020-07-06 00:00:00
Tasks: Bilevel Optimization, Denoising, Image Denoising

One fundamental problem when solving inverse problems is how to find regularization parameters. This article considers solving this problem using data-driven bilevel optimization, i.e. we consider the adaptive learning of the regularization parameter from data by means of optimization. This approach can be interpreted as solving an empirical risk minimization problem, and we analyze its performance in the large data sample size limit for general nonlinear problems. We demonstrate how to implement our framework on linear inverse problems, where we can further show the inverse accuracy does not depend on the ambient space dimension. To reduce the associated computational cost, online numerical schemes are derived using the stochastic gradient descent method. We prove convergence of these numerical schemes under suitable assumptions on the forward problem. Numerical experiments are presented illustrating the theoretical results and demonstrating the applicability and efficiency of the proposed approaches for various linear and nonlinear inverse problems, including Darcy flow, the eikonal equation, and an image denoising example.
A Survey on Sensor Technologies for Unmanned Ground Vehicles
Authors: Qi Liu, Shihua Yuan, Zirui Li
Date: 2020-07-04 00:00:00
Tasks: Survey

Unmanned ground vehicles have a huge development potential in both civilian and military fields, and have become the focus of research in various countries. In addition, high-precision, high-reliability sensors are significant for UGVs' efficient operation. This paper proposes a brief review on sensor technologies for UGVs. Firstly, characteristics of various sensors are introduced. Then the strengths and weaknesses of different sensors as well as their application scenarios are compared. Furthermore, sensor applications in some existing UGVs are summarized. Finally, the hotspots of sensor technologies are forecasted to point the development direction.
Novel min-max reformulations of Linear Inverse Problems
Authors: Mohammed Rayyan Sheriff, Debasish Chatterjee
Date: 2020-07-05 00:00:00
Tasks: Dictionary Learning, Recommendation Systems

In this article, we dwell into the class of so-called ill-posed Linear Inverse Problems (LIP) which simply refers to the task of recovering the entire signal from its relatively few random linear measurements. Such problems arise in a variety of settings with applications ranging from medical image processing, recommender systems, etc. We propose a slightly generalized version of the error constrained linear inverse problem and obtain a novel and equivalent convex-concave min-max reformulation by providing an exposition to its convex geometry. Saddle points of the min-max problem are completely characterized in terms of a solution to the LIP, and vice versa. Applying simple saddle point seeking ascend-descent type algorithms to solve the min-max problems provides novel and simple algorithms to find a solution to the LIP. Moreover, the reformulation of an LIP as the min-max problem provided in this article is crucial in developing methods to solve the dictionary learning problem with almost sure recovery constraints.
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
Authors: Yufei Wang, Tianwei Ni
Date: 2020-07-03 00:00:00
Tasks: Benchmarking, MuJoCo

Exploration-exploitation dilemma has long been a crucial issue in reinforcement learning. In this paper, we propose a new approach to automatically balance between these two. Our method is built upon the Soft Actor-Critic (SAC) algorithm, which uses an "entropy temperature" that balances the original task reward and the policy entropy, and hence controls the trade-off between exploitation and exploration. It is empirically shown that SAC is very sensitive to this hyperparameter, and the follow-up work (SAC-v2), which uses constrained optimization for automatic adjustment, has some limitations. The core of our method, namely Meta-SAC, is to use metagradient along with a novel meta objective to automatically tune the entropy temperature in SAC. We show that Meta-SAC achieves promising performances on several of the Mujoco benchmarking tasks, and outperforms SAC-v2 over 10% in one of the most challenging tasks, humanoid-v2.