pwc-archive/papers-with-abstracts huggingface dataset:
Towards Anomaly Detection in Dashcam Videos
Authors: Sanjay Haresh, Sateesh Kumar, M. Zeeshan Zia, Quoc-Huy Tran
Date: 2020-04-11 00:00:00
Tasks: Anomaly Detection, One-Class Classification

Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms focused on lane departures or safe following distances are finding their way into these devices. Unfortunately, the long-tailed distribution of road hazards means that these hand-crafted pipelines are inadequate for driver safety systems. We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. Unfortunately, there exists almost no literature applying anomaly understanding to moving cameras, and correspondingly there is also a lack of relevant datasets. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i) one-class classification loss and (ii) reconstruction-based loss, for anomaly detection on RetroTrucks as well as on existing static-camera datasets. We introduce formulations for modeling object interactions in this context as priors. Our experiments indicate that our dataset is indeed more challenging than standard anomaly detection datasets, and previous anomaly detection methods do not perform well here out-of-the-box. In addition, we share insights into the behavior of these two important families of anomaly detection approaches on dashcam data.
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification
Authors: Rikima Mitsuhashi, Takahiro Shinagawa
Date: 2020-04-10 00:00:00
Tasks: Classification, Computer Security, Deep Learning, General Classification, Image Classification, Malware Classification, Transfer Learning

Analyzing a huge amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of their burden. Image-based malware classification with deep learning is an attractive approach for its simplicity, versatility, and affinity with the latest technologies. However, the impact of differences in deep learning models and the degree of transfer learning on the classification accuracy of malware variants has not been fully studied. In this paper, we conducted an exhaustive survey of deep learning models using 24 ImageNet pre-trained models and five fine-tuning parameters, totaling 120 combinations, on two platforms. As a result, we found that the highest classification accuracy was obtained by fine-tuning one of the latest deep learning models with a relatively low degree of transfer learning, and we achieved the highest classification accuracy ever in cross-validation on the Malimg and Drebin datasets. We also confirmed that this trend holds true for the recent malware variants using the VirusTotal 2020 Windows and Android datasets. The experimental results suggest that it is effective to periodically explore optimal deep learning models with the latest models and malware datasets by gradually reducing the degree of transfer learning from half.
Sequence Model Design for Code Completion in the Modern IDE
Authors: Gareth Ari Aye, Gail E. Kaiser
Date: 2020-04-10 00:00:00
Tasks: Code Completion, Language Modeling, Language Modelling, valid

Code completion plays a prominent role in modern integrated development environments (IDEs). Machine learning has become ubiquitous in analogous natural language writing and search software, surfacing more relevant autocompletions and search suggestions in fewer keystrokes. Prior research has reported training high-accuracy, deep neural networks for modeling source code, but little attention has been given to the practical constraints imposed by interactive developer tools. In particular, neural language models for source code modeling like the one described in Maybe Deep Neural Networks are the Best Choice for Modeling Source Code are framed around code completion, but only report accuracy of next-token prediction. However, in order for a language model (LM) to work well within real-world code completion systems, it must also always make suggestions that produce valid code that typechecks to support code completion's role in correctness-checking; return instantaneous results to help programmers code more efficiently in fewer keystrokes; and be small enough to fit comfortably on disk and in memory on developer workstations, since virtually all modern IDEs run locally and support offline usage. To meet these additional requirements, we propose a novel design for predicting top-k next tokens that combines static analysis' ability to enumerate all valid keywords and in-scope identifiers with the ability of a language model to place a probability distribution over them. Our model mixes character-level input representation with token output to represent out-of-vocabulary (OOV) tokens meaningfully and minimize prediction latency. OOV tokens can be predicted through detection of local repetition common in software. This design achieves state-of-art accuracy in source code modeling and fits the constraints imposed by real-world code completion implementations in modern IDEs.
FLIVVER: Fly Lobula Inspired Visual Velocity Estimation & Ranging
Authors: Bryson Lingenfelter, Arunava Nag, Floris van Breugel
Date: 2020-04-10 00:00:00
Tasks: Simultaneous Localization and Mapping, State Estimation

The mechanism by which a tiny insect or insect-sized robot could estimate its absolute velocity and distance to nearby objects remains unknown. However, this ability is critical for behaviors that require estimating wind direction during flight, such as odor-plume tracking. Neuroscience and behavior studies with insects have shown that they rely on the perception of image motion, or optic flow, to estimate relative motion, equivalent to a ratio of their velocity and distance to objects in the world. The key open challenge is therefore to decouple these two states from a single measurement of their ratio. Although modern SLAM (Simultaneous Localization and Mapping) methods provide a solution to this problem for robotic systems, these methods typically rely on computations that insects likely cannot perform, such as simultaneously tracking multiple individual visual features, remembering a 3D map of the world, and solving nonlinear optimization problems using iterative algorithms. Here we present a novel algorithm, FLIVVER, which combines the geometry of dynamic forward motion with inspiration from insect visual processing to \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our algorithm provides a clear hypothesis for how insects might estimate absolute velocity, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation, which could be applied to insect-sized robots.
Efficient Sampled Softmax for Tensorflow
Authors: Maciej Skorski
Date: 2020-04-10 00:00:00

This short paper discusses an efficient implementation of \emph{sampled softmax loss} for Tensorflow. The speedup over the default implementation is achieved due to simplification of the graph for the forward and backward passes.
Convolutional Neural Networks for Real-Time Localization and Classification in Feedback Digital Microscopy
Authors: Martin Fränzl, Frank Cichos
Date: 2020-04-10 00:00:00
Tasks: General Classification

We present an adapted single-shot convolutional neural network (YOLOv2) for the real-time localization and classification of particles in optical microscopy. As compared to previous works, we focus on the real-time detection capabilities of the system to allow for manipulation of microscopic objects in large heterogeneous ensembles with the help of feedback control. The network is capable of localizing and classifying several hundreds of microscopic objects even at very low signal-to-noise ratios for images as large as 416x416 pixels with an inference time of about 10 ms. We demonstrate the real-time detection performance by manipulating active particles propelled by laser-induced self-thermophoresis. In order to make our framework readily available for others, we provide all scripts and source code. The network is implemented in Python/Keras using the TensorFlow backend. A C library supporting GPUs is provided for the real-time inference.
Attend and Decode: 4D fMRI Task State Decoding Using Attention Models
Authors: Sam Nguyen, Brenda Ng, Alan D. Kaplan, Priyadip Ray
Date: 2020-04-10 00:00:00

Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is challenging due to the high dimensionality (hundreds of million sampling points per datum) and complex spatio-temporal blood flow patterns inherent in the data. In this work, we propose to tackle the fMRI task state decoding problem by casting it as a 4D spatio-temporal classification problem. We present a novel architecture called Brain Attend and Decode (BAnD), that uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms for temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the large-scale Human Connectome Project-Young Adult (HCP-YA) dataset. We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model. The pre-trained features from BAnD are useful on similar tasks while finetuning them yields competitive results on unseen tasks/conditions.
Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model
Authors: Gang Yao, Ryan Saltus, Ashwin Dani
Date: 2020-04-10 00:00:00
Tasks: Object

In this paper, a B-spline chained multiple random matrices representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed model, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.
End-to-end Learning Improves Static Object Geo-localization in Monocular Video
Authors: Mohamed Chaabane, Lionel Gueguen, Ameni Trabelsi, Ross Beveridge, Stephen O'Hara
Date: 2020-04-10 00:00:00
Tasks: geo-localization, Multi-Object Tracking, Object Tracking, Pose Estimation, Position

Accurately estimating the position of static objects, such as traffic lights, from the moving camera of a self-driving car is a challenging problem. In this work, we present a system that improves the localization of static objects by jointly-optimizing the components of the system via learning. Our system is comprised of networks that perform: 1) 5DoF object pose estimation from a single image, 2) association of objects between pairs of frames, and 3) multi-object tracking to produce the final geo-localization of the static objects within the scene. We evaluate our approach using a publicly-available data set, focusing on traffic lights due to data availability. For each component, we compare against contemporary alternatives and show significantly-improved performance. We also show that the end-to-end system performance is further improved via joint-training of the constituent models.
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review
Authors: Yaodong Cui, Ren Chen, Wenbo Chu, Long Chen, Daxin Tian, Ying Li, Dongpu Cao
Date: 2020-04-10 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Learning, Depth Completion, object-detection, Object Detection, Semantic Segmentation

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions.
Joint translation and unit conversion for end-to-end localization
Authors: Georgiana Dinu, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan
Date: 2020-04-10 00:00:00
Proceeding: WS 2020 7
Tasks: Data Augmentation, Translation

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which leads to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.
Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves
Authors: Yang-Hui He, Edward Hirst, Toby Peterken
Date: 2020-04-10 00:00:00
Tasks: BIG-bench Machine Learning

We apply machine-learning to the study of dessins d'enfants. Specifically, we investigate a class of dessins which reside at the intersection of the investigations of modular subgroups, Seiberg-Witten curves and extremal elliptic K3 surfaces. A deep feed-forward neural network with simple structure and standard activation functions without prior knowledge of the underlying mathematics is established and imposed onto the classification of extension degree over the rationals, known to be a difficult problem. The classifications reached 0.92 accuracy with 0.03 standard error relatively quickly. The Seiberg-Witten curves for those with rational coefficients are also tabulated.
A Review on Deep Learning Techniques for Video Prediction
Authors: Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, Antonis Argyros
Date: 2020-04-10 00:00:00
Tasks: Decision Making, Deep Learning, Prediction, Representation Learning, Self-Supervised Learning, Video Prediction

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We firstly define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Authors: Austin Talbot, David Dunson, Kafui Dzirasa, David Carlson
Date: 2020-04-10 00:00:00
Tasks: Dimensionality Reduction

Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states. We model multi-region electrical activity as a superposition of activity from latent networks, where the weights on the latent networks relate to an outcome of interest. In order to improve on drawbacks of latent factor modeling in this context, we focus on supervised autoencoders (SAEs), which can improve predictive performance while maintaining a generative model. We explain why SAEs yield improved predictions, describe the distributional assumptions under which SAEs are an appropriate modeling choice, and provide modeling constraints to ensure biological relevance of the learned network. We use the analysis strategy to find a network associated with stress that characterizes a genotype associated with bipolar disorder. This discovered network aligns with a previously used stimulation technique, providing experimental validation of our approach.
Implicit Multiagent Coordination at Unsignalized Intersections via Multimodal Inference Enabled by Topological Braids
Authors: Christoforos Mavrogiannis, Jonathan A. DeCastro, Siddhartha S. Srinivasa
Date: 2020-04-10 00:00:00
Tasks: Decision Making, Trajectory Prediction

We focus on navigation among rational, non-communicating agents at unsignalized street intersections. Following collision-free motion under such settings demands nuanced implicit coordination among agents. Often, the structure of these domains constrains multiagent trajectories to belong to a finite set of modes. Our key insight is that empowering agents with a model of these modes can enable effective coordination, realized implicitly via intent signals encoded in agents' actions. In this paper, we represent modes of joint behavior in a compact and interpretable fashion using the formalism of topological braids. We design a decentralized planning algorithm that generates actions aimed at reducing the uncertainty over the mode of the emerging multiagent behavior. This mechanism enables agents that individually run our algorithm to collectively reject unsafe intersection crossings. We validate our approach in a simulated case study featuring challenging multiagent scenarios at a four-way unsignalized intersection. Our model is shown to reduce frequency of collisions by >65% over a set of baselines explicitly reasoning over trajectories, while maintaining comparable time efficiency.
Hamiltonian Dynamics for Real-World Shape Interpolation
Authors: Marvin Eisenberger, Daniel Cremers
Date: 2020-04-10 00:00:00
Proceeding: ECCV 2020 8
Tasks: Local Distortion

We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise. To that end, we use recent progress on dynamic thin shell simulation and divergence-free shape deformation and combine them to address the inverse problem of finding a plausible intermediate sequence for two input shapes. In comparison to prior work that mainly focuses on small distortion of consecutive frames, we explicitly model volume preservation and momentum conservation, as well as an anisotropic local distortion model. We argue that, in order to get a robust interpolation for imperfect inputs, we need to model the input noise explicitly which results in an alignment based formulation. Finally, we show a qualitative and quantitative improvement over prior work on a broad range of synthetic and scanned data. Besides being more robust to noisy inputs, our method yields exactly volume preserving intermediate shapes, avoids self-intersections and is scalable to high resolution scans.
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels
Authors: Imène R. Goumiri, Benjamin W. Priest, Michael D. Schneider
Date: 2020-04-10 00:00:00
Tasks: Gaussian Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawbacks for challenging problems. DNNs learn complex nonlinear embeddings, but do not naturally quantify uncertainty and are often data-inefficient to train. GPs infer posterior distributions over functions, but popular kernels exhibit limited expressivity on complex and high-dimensional data. Fortunately, recently discovered conjugate and neural tangent kernel functions encode the behavior of overparameterized neural networks in the kernel domain. We demonstrate that these kernels can be efficiently applied to regression and reinforcement learning problems by analyzing a baseline case study. We apply GPs with neural network dual kernels to solve reinforcement learning tasks for the first time. We demonstrate, using the well-understood mountain-car problem, that GPs empowered with dual kernels perform at least as well as those using the conventional radial basis function kernel. We conjecture that by inheriting the probabilistic rigor of GPs and the powerful embedding properties of DNNs, GPs using NN dual kernels will empower future reinforcement learning models on difficult domains.