pwc-archive/papers-with-abstracts huggingface dataset:
Emo-CNN for Perceiving Stress from Audio Signals: A Brain Chemistry Approach
Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier
Date: 2020-01-08 00:00:00
Tasks: feature selection

Emotion plays a key role in many applications like healthcare, to gather patients emotional behavior. There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is defining the very meaning of stress and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation and emotion detection is the limited amount of annotated data of stress. The existing labelled stress emotion datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC features in Convolutional Neural Network. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. To tackle the second and the more significant problem of subjectivity in stress labels, we use Lovheim's cube, which is a 3-dimensional projection of emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim's cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.
The Past and Present of Imitation Learning: A Citation Chain Study
Authors: Nishanth Kumar
Date: 2020-01-08 00:00:00
Tasks: Atari Games, Autonomous Driving, Imitation Learning

Imitation Learning is a promising area of active research. Over the last 30 years, Imitation Learning has advanced significantly and been used to solve difficult tasks ranging from Autonomous Driving to playing Atari games. In the course of this development, different methods for performing Imitation Learning have fallen into and out of favor. In this paper, I explore the development of these different methods and attempt to examine how the field has progressed. I focus my analysis on surveying 4 landmark papers that sequentially build upon each other to develop increasingly impressive Imitation Learning methods.
On Thompson Sampling for Smoother-than-Lipschitz Bandits
Authors: James A. Grant, David S. Leslie
Date: 2020-01-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little attention. We provide the first bounds on the regret of Thompson Sampling for continuum armed bandits under weak conditions on the function class containing the true function and sub-exponential observation noise. Our bounds are realised by analysis of the eluder dimension, a recently proposed measure of the complexity of a function class, which has been demonstrated to be useful in bounding the Bayesian regret of Thompson Sampling for simpler bandit problems under sub-Gaussian observation noise. We derive a new bound on the eluder dimension for classes of functions with Lipschitz derivatives, and generalise previous analyses in multiple regards.
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Authors: Yang Tang, Chaoqiang Zhao, Jianrui Wang, Chongzhen Zhang, Qiyu Sun, WeiXing Zheng, Wenli Du, Feng Qian, Juergen Kurths
Date: 2020-01-08 00:00:00
Tasks: Autonomous Navigation, Decision Making, Deep Learning, Deep Reinforcement Learning, Depth Estimation, Image Enhancement, Monocular Depth Estimation, Motion Planning, motion prediction, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Segmentation, Simultaneous Localization and Mapping, State Estimation, Trajectory Planning, Visual Navigation

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.
Bridging Knowledge Graphs to Generate Scene Graphs
Authors: Alireza Zareian, Svebor Karaman, Shih-Fu Chang
Date: 2020-01-07 00:00:00
Proceeding: ECCV 2020 8
Tasks: Graph Generation, Knowledge Graphs, Scene Graph Generation

Scene graphs are powerful representations that parse images into their abstract semantic elements, i.e., objects and their interactions, which facilitates visual comprehension and explainable reasoning. On the other hand, commonsense knowledge graphs are rich repositories that encode how the world is structured, and how general concepts interact. In this paper, we present a unified formulation of these two constructs, where a scene graph is seen as an image-conditioned instantiation of a commonsense knowledge graph. Based on this new perspective, we re-formulate scene graph generation as the inference of a bridge between the scene and commonsense graphs, where each entity or predicate instance in the scene graph has to be linked to its corresponding entity or predicate class in the commonsense graph. To this end, we propose a novel graph-based neural network that iteratively propagates information between the two graphs, as well as within each of them, while gradually refining their bridge in each iteration. Our Graph Bridging Network, GB-Net, successively infers edges and nodes, allowing to simultaneously exploit and refine the rich, heterogeneous structure of the interconnected scene and commonsense graphs. Through extensive experimentation, we showcase the superior accuracy of GB-Net compared to the most recent methods, resulting in a new state of the art. We publicly release the source code of our method.
Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well
Authors: Vipul Gupta, Santiago Akle Serrano, Dennis Decoste
Date: 2020-01-07 00:00:00
Proceeding: ICLR 2020 1

We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training. Our algorithm uses large mini-batches to compute an approximate solution quickly and then refines it by averaging the weights of multiple models computed independently and in parallel. The resulting models generalize equally well as those trained with small mini-batches but are produced in a substantially shorter time. We demonstrate the reduction in training time and the good generalization performance of the resulting models on the computer vision datasets CIFAR10, CIFAR100, and ImageNet.
VC-dimensions of nondeterministic finite automata for words of equal length
Authors: Bjørn Kjos-Hanssen, Clyde James Felix, Sun Young Kim, Ethan Lamb, Davin Takahashi
Date: 2020-01-07 00:00:00

Let $NFA_b(q)$ denote the set of languages accepted by nondeterministic finite automata with $q$ states over an alphabet with $b$ letters. Let $B_n$ denote the set of words of length $n$. We give a quadratic lower bound on the VC dimension of \[ NFA_2(q)\cap B_n = \{L\cap B_n \mid L \in NFA_2(q)\} \] as a function of $q$. Next, the work of Gruber and Holzer (2007) gives an upper bound for the nondeterministic state complexity of finite languages contained in $B_n$, which we strengthen using our methods. Finally, we give some theoretical and experimental results on the dependence on $n$ of the VC dimension and testing dimension of $NFA_2(q)\cap B_n$.
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC
Authors: Keuntaek Lee, Jason Gibson, Evangelos A. Theodorou
Date: 2020-01-07 00:00:00
Tasks: Model Predictive Control, Optical Flow Estimation

Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics, which is a combination of optical flow and robot dynamics. Using the DOF dynamics, MPC explicitly incorporates the predicted movement of relevant pixels into the planned trajectory of a robot. Our implementation of DOF is memory-efficient, data-efficient, and computationally cheap so that it can be computed in real-time for use in an MPC framework. The suggested Pixel Model Predictive Control (PixelMPC) algorithm controls the robot to accomplish a high-speed racing task while maintaining visibility of the important features (gates). This improves the reliability of vision-based estimators for localization and can eventually lead to safe autonomous flight. The proposed algorithm is tested in a photorealistic simulation with a high-speed drone racing task.
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection
Authors: Zhijun Liang, Juan Rojas, Junfa Liu, Yisheng Guan
Date: 2020-01-07 00:00:00
Tasks: Graph Attention, Human-Object Interaction Detection, Object, Scene Understanding, Triplet

In scene understanding, robotics benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection infers the action predicate on a <human, predicate, object> triplet. Contextual information has been found critical in inferring interactions. However, most works only use local features from single human-object pair for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks. Similarly, few have learned to effectively leverage visual cues along with the intrinsic semantic regularities contained in HOIs. We contribute a dual-graph attention network that effectively aggregates contextual visual, spatial, and semantic information dynamically from primary human-object relations as well as subsidiary relations through attention mechanisms for strong disambiguating power. We achieve comparable results on two benchmarks: V-COCO and HICO-DET. Code is available at \url{https://github.com/birlrobotics/vs-gats}.
Multipurpose Intelligent Process Automation via Conversational Assistant
Authors: Alena Moiseeva, Dietrich Trautmann, Michael Heimann, Hinrich Schütze
Date: 2020-01-07 00:00:00
Tasks: Transfer Learning

Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language are potential application for IPA systems. Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i.e., customer support). In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes. Second, by interacting with users, it augments the resources with structured and to some extent labeled training data. We showcase the usage of the latter by re-implementing several components of our system with Transfer Learning (TL) methods.
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
Authors: Chelsea M. Myers, Evan Freed, Luis Fernando Laris Pardo, Anushay Furqan, Sebastian Risi, Jichen Zhu
Date: 2020-01-07 00:00:00
Tasks: counterfactual

AI algorithms are not immune to biases. Traditionally, non-experts have little control in uncovering potential social bias (e.g., gender bias) in the algorithms that may impact their lives. We present a preliminary design for an interactive visualization tool CEB to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias. This paper presents the design of CEB and initial findings of an expert panel (n=6) with AI, HCI, and Social science experts.
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware
Authors: Kirill Polzounov, Ramitha Sundar, Lee Redden
Date: 2020-01-07 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the timing (using a hardware timer), and ensuring the data sent to hardware is safe and correct, where safety corresponds to safe operating voltage and current for the specified hardware. Much of the recent success of Reinforcement learning (RL) has been made possible with training and testing tools like OpenAI Gym and Deepmind Control Suite. Unfortunately, tools for quickly testing and transferring high-frequency RL algorithms from simulation to real hardware environment remain mostly absent. We present Blue River Controls, a tool that allows to train and test reinforcement learning algorithms on real-world hardware. It features a simple interface based on OpenAI Gym, that works directly on both simulation and hardware. We use Quanser's Qube Servo2-USB platform, an underactuated rotary pendulum as an initial testing device. We also provide tools to simplify training RL algorithms on other hardware. Several baselines, from both classical controllers and pretrained RL agents are included to compare performance across tasks. Blue River Controls is available at this https URL: https://github.com/BlueRiverTech/quanser-openai-driver
Stochastic Local Interaction Model: Geostatistics without Kriging
Authors: Dionissios T. Hristopulos, Andreas Pavlides, Vasiliki D. Agou, Panagiota Gkafa
Date: 2020-01-07 00:00:00
Tasks: model, parameter estimation

Classical geostatistical methods face serious computational challenges if they are confronted with large sets of spatially distributed data. We present a simplified stochastic local interaction (SLI) model for computationally efficient spatial prediction that can handle large data. The SLI method constructs a spatial interaction matrix (precision matrix) that accounts for the data values, their locations, and the sampling density variations without user input. We show that this precision matrix is strictly positive definite. The SLI approach does not require matrix inversion for parameter estimation, spatial prediction, and uncertainty estimation, leading to computational procedures that are significantly less intensive computationally than kriging. The precision matrix involves compact kernel functions (spherical, quadratic, etc.) which enable the application of sparse matrix methods, thus improving computational time and memory requirements. We investigate the proposed SLI method with a data set that includes approximately 11500 drill-hole data of coal thickness from Campbell County (Wyoming, USA). We also compare SLI with ordinary kriging (OK) in terms of estimation performance, using cross validation analysis, and computational time. According to the validation measures used, SLI performs slightly better in estimating seam thickness than OK in terms of cross-validation measures. In terms of computation time, SLI prediction is 3 to 25 times (depending on the size of the kriging neighborhood) faster than OK for the same grid size.
Infinite-Horizon Differentiable Model Predictive Control
Authors: Sebastian East, Marco Gallieri, Jonathan Masci, Jan Koutnik, Mark Cannon
Date: 2020-01-07 00:00:00
Proceeding: ICLR 2020 1
Tasks: Imitation Learning, model, Model Predictive Control

This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning. The infinite-horizon cost is enforced using a terminal cost function obtained from the discrete-time algebraic Riccati equation (DARE), so that the learned controller can be proven to be stabilizing in closed-loop. A central contribution is the derivation of the analytical derivative of the solution of the DARE, thereby allowing the use of differentiation-based learning methods. A further contribution is the structure of the MPC optimization problem: an augmented Lagrangian method ensures that the MPC optimization is feasible throughout training whilst enforcing hard constraints on state and input, and a pre-stabilizing controller ensures that the MPC solution and derivatives are accurate at each iteration. The learning capabilities of the framework are demonstrated in a set of numerical studies.
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics
Authors: Simon Donig, Maria Christoforaki, Bernhard Bermeitinger, Siegfried Handschuh
Date: 2020-01-07 00:00:00
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification

In the last years, image classification processes like neural networks in the area of art-history and Heritage Informatics have experienced a broad distribution (Lang and Ommer 2018). These methods face several challenges, including the handling of comparatively small amounts of data as well as high-dimensional data in the Digital Humanities. Here, a Convolutional Neural Network (CNN) is used that output is not, as usual, a series of flat text labels but a series of semantically loaded vectors. These vectors result from a Distributional Semantic Model (DSM) which is generated from an in-domain text corpus. ----- In den letzten Jahren hat die Verwendung von Bildklassifizierungsverfahren wie neuronalen Netzwerken auch im Bereich der historischen Bildwissenschaften und der Heritage Informatics weite Verbreitung gefunden (Lang und Ommer 2018). Diese Verfahren stehen dabei vor einer Reihe von Herausforderungen, darunter dem Umgangmit den vergleichsweise kleinen Datenmengen sowie zugleich hochdimensionalen Da-tenr\"aumen in den digitalen Geisteswissenschaften. Meist bilden diese Methoden dieKlassifizierung auf einen vergleichsweise flachen Raum ab. Dieser flache Zugang verliert im Bem\"uhen um ontologische Eindeutigkeit eine Reihe von relevanten Dimensionen, darunter taxonomische, mereologische und assoziative Beziehungen zwischenden Klassen beziehungsweise dem nicht formalisierten Kontext. Dabei wird ein Convolutional Neural Network (CNN) genutzt, dessen Ausgabe im Trainingsprozess, anders als herk\"ommlich, nicht auf einer Serie flacher Textlabel beruht, sondern auf einer Serie von Vektoren. Diese Vektoren resultieren aus einem Distributional Semantic Model (DSM), welches aus einem Dom\"ane-Textkorpus generiert wird.
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Authors: Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht
Date: 2020-01-06 00:00:00
Tasks: BIG-bench Machine Learning, Image Reconstruction

Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
Generating Semantic Adversarial Examples via Feature Manipulation
Authors: Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
Date: 2020-01-06 00:00:00
Tasks: Adversarial Attack, Disentanglement

The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier. An alternative approach is to have perturbations in the latent space. However, such perturbations are hard to control due to the lack of interpretability and disentanglement. In this paper, we propose a more practical adversarial attack by designing structured perturbation with semantic meanings. Our proposed technique manipulates the semantic attributes of images via the disentangled latent codes. The intuition behind our technique is that images in similar domains have some commonly shared but theme-independent semantic attributes, e.g. thickness of lines in handwritten digits, that can be bidirectionally mapped to disentangled latent codes. We generate adversarial perturbation by manipulating a single or a combination of these latent codes and propose two unsupervised semantic manipulation approaches: vector-based disentangled representation and feature map-based disentangled representation, in terms of the complexity of the latent codes and smoothness of the reconstructed images. We conduct extensive experimental evaluations on real-world image data to demonstrate the power of our attacks for black-box classifiers. We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.