pwc-archive/papers-with-abstracts huggingface dataset:
Variance reduction for Markov chains with application to MCMC
Authors: D. Belomestny, L. Iosipoi, E. Moulines, A. Naumov, S. Samsonov
Date: 2019-10-08 00:00:00

In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce the overall finite sample variance. This feature is theoretically demonstrated by means of a deep non asymptotic analysis of a variance reduced functional as well as by a thorough simulation study. In particular we apply our method to various MCMC Bayesian estimation problems where it favourably compares to the existing variance reduction approaches.
Linking emotions to behaviors through deep transfer learning
Authors: Haoqi Li, Brian Baucom, Panayiotis Georgiou
Date: 2019-10-08 00:00:00
Tasks: Emotion Recognition, Transfer Learning

Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation. Domain experts integrate emotional information in a highly nonlinear manner, thus, it is challenging to explicitly quantify the relationship between emotions and behaviors. In this work, we employ deep transfer learning to analyze their inferential capacity and contextual importance. We first train a network to quantify emotions from acoustic signals and then use information from the emotion recognition network as features for behavior recognition. We treat this emotion-related information as behavioral primitives and further train higher level layers towards behavior quantification. Through our analysis, we find that emotion-related information is an important cue for behavior recognition. Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks' contextual view of the data. This demonstrates that the sequence of emotions is critical in behavior expression. To achieve these frameworks we employ hybrid architectures of convolutional networks and recurrent networks to extract emotion-related behavior primitives and facilitate automatic behavior recognition from speech.
Bregman Proximal Framework for Deep Linear Neural Networks
Authors: Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs
Date: 2019-10-08 00:00:00

A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applications this assumption is violated, including loss functions in deep learning. To overcome this issue, certain extensions based on generalized proximity measures known as Bregman distances were introduced. This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum based) CoCaIn BPG, which however rely on problem dependent Bregman distances. In this paper, we develop Bregman distances for using BPG methods to train Deep Linear Neural Networks. The main implications of our results are strong convergence guarantees for these algorithms. We also propose several strategies for their efficient implementation, for example, closed form updates and a closed form expression for the inertial parameter of CoCaIn BPG. Moreover, the BPG method requires neither diminishing step sizes nor line search, unlike its corresponding Euclidean version. We numerically illustrate the competitiveness of the proposed methods compared to existing state of the art schemes.
Prose for a Painting
Authors: Prerna Kashyap, Samrat Phatale, Iddo Drori
Date: 2019-10-08 00:00:00
Tasks: Style Transfer

Painting captions are often dry and simplistic which motivates us to describe a painting creatively in the style of Shakespearean prose. This is a difficult problem, since there does not exist a large supervised dataset from paintings to Shakespearean prose. Our solution is to use an intermediate English poem description of the painting and then apply language style transfer which results in Shakespearean prose describing the painting. We rate our results by human evaluation on a Likert scale, and evaluate the quality of language style transfer using BLEU score as a function of prose length. We demonstrate the applicability and limitations of our approach by generating Shakespearean prose for famous paintings. We make our models and code publicly available.
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
Authors: Weiyang Zhang, Wenshuo Wang, Ding Zhao
Date: 2019-10-08 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Motion Planning, regression

Generating multi-vehicle interaction scenarios can benefit motion planning and decision making of autonomous vehicles when on-road data is insufficient. This paper presents an efficient approach to generate varied multi-vehicle interaction scenarios that can both adapt to different road geometries and inherit the key interaction patterns in real-world driving. Towards this end, the available multi-vehicle interaction scenarios are temporally segmented into several interpretable fundamental building blocks, called traffic primitives, via the Bayesian nonparametric learning. Then, the changepoints of traffic primitives are transformed into the desired road to generate collision-free interaction trajectories through a sampling-based path planning algorithm. The Gaussian process regression is finally introduced to control the variance and smoothness of the generated multi-vehicle interaction trajectories. Experiments with simulation results of three typical multi-vehicle trajectories at different road conditions are carried out. The experimental results demonstrate that our proposed method can generate a bunch of human-like multi-vehicle interaction trajectories that can fit different road conditions remaining the key interaction patterns of agents in the provided scenarios, which is import to the development of autonomous vehicles.
Distilling Importance Sampling for Likelihood Free Inference
Authors: Dennis Prangle, Cecilia Viscardi
Date: 2019-10-08 00:00:00
Tasks: Bayesian Inference, Epidemiology

Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code which takes parameters, performs stochastic calculations, and outputs simulated data. In this work, we view the simulator as a function whose inputs are (1) the parameters and (2) a vector of pseudo-random draws. We attempt to infer all these inputs conditional on the observations. This is challenging as the resulting posterior can be high dimensional and involve strong dependence. We approximate the posterior using normalizing flows, a flexible parametric family of densities. Training data is generated by likelihood-free importance sampling with a large bandwidth value epsilon, which makes the target similar to the prior. The training data is "distilled" by using it to train an updated normalizing flow. The process is iterated, using the updated flow as the importance sampling proposal, and slowly reducing epsilon so the target becomes closer to the posterior. Unlike most other likelihood-free methods, we avoid the need to reduce data to low dimensional summary statistics, and hence can achieve more accurate results. We illustrate our method in two challenging examples, on queuing and epidemiology.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Authors: Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2019-10-08 00:00:00

Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of $\ell_p$-bounded and $\ell_p$-minimal adversarial perturbations. However, the vulnerability of DNNs to adversarial perturbations with specific statistical properties or frequency-domain characteristics has not been sufficiently explored. In this paper, we study the smoothness of perturbations and propose SmoothFool, a general and computationally efficient framework for computing smooth adversarial perturbations. Through extensive experiments, we validate the efficacy of the proposed method for both the white-box and black-box attack scenarios. In particular, we demonstrate that: (i) there exist extremely smooth adversarial perturbations for well-established and widely used network architectures, (ii) smoothness significantly enhances the robustness of perturbations against state-of-the-art defense mechanisms, (iii) smoothness improves the transferability of adversarial perturbations across both data points and network architectures, and (iv) class categories exhibit a variable range of susceptibility to smooth perturbations. Our results suggest that smooth APs can play a significant role in exploring the vulnerability extent of DNNs to adversarial examples.
Receding Horizon Curiosity
Authors: Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters
Date: 2019-10-08 00:00:00
Tasks: Efficient Exploration, Experimental Design, Inductive Bias

Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental design. In this paper, we present an effective trajectory-optimization-based approximate solution of this otherwise intractable problem that models optimal exploration in an unknown Markov decision process (MDP). By interleaving episodic exploration with Bayesian nonlinear system identification, our algorithm takes advantage of the inductive bias to explore in a directed manner, without assuming prior knowledge of the MDP. Empirical evaluations indicate a clear advantage of the proposed algorithm in terms of the rate of convergence and the final model fidelity when compared to intrinsic-motivation-based algorithms employing exploration bonuses such as prediction error and information gain. Moreover, our method maintains a computational advantage over a recent model-based active exploration (MAX) algorithm, by focusing on the information gain along trajectories instead of seeking a global exploration policy. A reference implementation of our algorithm and the conducted experiments is publicly available.
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting
Authors: Manish Bhattarai, Manel Martínez-Ramón
Date: 2019-10-08 00:00:00
Tasks: Decision Making, object-detection, Object Detection, Real-Time Object Detection

Intelligent detection and processing capabilities can be instrumental to improving the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. The objective of this research is to create an automated system that is capable of real-time, intelligent object detection and recognition and facilitates the improved situational awareness of firefighters during an emergency response. We have explored state of the art machine/deep learning techniques to achieve this objective. The goal for this work is to enhance the situational awareness of firefighters by effectively exploiting the information gathered from infrared cameras carried by firefighters. To accomplish this, we use a trained deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real time. In the midst of those critical circumstances created by structure fire, this system is able to accurately inform the decision making process of firefighters with real-time up-to-date scene information by extracting, processing, and analyzing crucial information. With the new information produced by the framework, firefighters are able to make more informed inferences about the circumstances for their safe navigation through such hazardous and potentially catastrophic environments.
FedMD: Heterogenous Federated Learning via Model Distillation
Authors: Daliang Li, Junpu Wang
Date: 2019-10-08 00:00:00
Tasks: Federated Learning, Knowledge Distillation, model, Transfer Learning

Federated learning enables the creation of a powerful centralized model without compromising data privacy of multiple participants. While successful, it does not incorporate the case where each participant independently designs its own model. Due to intellectual property concerns and heterogeneous nature of tasks and data, this is a widespread requirement in applications of federated learning to areas such as health care and AI as a service. In this work, we use transfer learning and knowledge distillation to develop a universal framework that enables federated learning when each agent owns not only their private data, but also uniquely designed models. We test our framework on the MNIST/FEMNIST dataset and the CIFAR10/CIFAR100 dataset and observe fast improvement across all participating models. With 10 distinct participants, the final test accuracy of each model on average receives a 20% gain on top of what's possible without collaboration and is only a few percent lower than the performance each model would have obtained if all private datasets were pooled and made directly available for all participants.
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs
Authors: Shantanu Prasad Burnwal, Kaneenika Sinha, Mathukumalli Vidyasagar
Date: 2019-10-08 00:00:00
Tasks: Matrix Completion

The objectives of this article are three-fold. Firstly, we present for the first time explicit constructions of an infinite family of \textit{unbalanced} Ramanujan bigraphs. Secondly, we revisit some of the known methods for constructing Ramanujan graphs and discuss the computational work required in actually implementing the various construction methods. The third goal of this article is to address the following question: can we construct a bipartite Ramanujan graph with specified degrees, but with the restriction that the edge set of this graph must be distinct from a given set of "prohibited" edges? We provide an affirmative answer in many cases, as long as the set of prohibited edges is not too large.
DEVDAN: Deep Evolving Denoising Autoencoder
Authors: Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Yew Soon Ong
Date: 2019-10-08 00:00:00
Tasks: Denoising

The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves an in-depth study because it characterizes a fixed network capacity that cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of the discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.
Improved Res2Net model for Person re-identification
Authors: Zongjing Cao, Hyo Jong Lee
Date: 2019-10-08 00:00:00
Tasks: Large-Scale Person Re-Identification, model, Person Re-Identification

Person re-identification has become a very popular research topic in the computer vision community owing to its numerous applications and growing importance in visual surveillance. Person re-identification remains challenging due to occlusion, illumination and significant intra-class variations across different cameras. In this paper, we propose a multi-task network base on an improved Res2Net model that simultaneously computes the identification loss and verification loss of two pedestrian images. Given a pair of pedestrian images, the system predicts the identities of the two input images and whether they belong to the same identity. In order to obtain deeper feature information of pedestrians, we propose to use the latest Res2Net model for feature extraction of each input image. Experiments on several large-scale person re-identification benchmark datasets demonstrate the accuracy of our approach. For example, rank-1 accuracies are 83.18% (+1.38) and 93.14% (+0.84) for the DukeMTMC and Market-1501 datasets, respectively. The proposed method shows encouraging improvements compared with state-of-the-art methods.
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
Authors: Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos
Date: 2019-10-08 00:00:00
Tasks: Action Recognition

Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a.k.a., "spikes") in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, feature representation for NVS is far behind its APS-based counterparts, resulting in lower performance in high-level computer vision tasks. To fully utilize its sparse and asynchronous nature, we propose a compact graph representation for NVS, which allows for end-to-end learning with graph convolution neural networks. We couple this with a novel end-to-end feature learning framework that accommodates both appearance-based and motion-based tasks. The core of our framework comprises a spatial feature learning module, which utilizes residual-graph convolutional neural networks (RG-CNN), for end-to-end learning of appearance-based features directly from graphs. We extend this with our proposed Graph2Grid block and temporal feature learning module for efficiently modelling temporal dependencies over multiple graphs and a long temporal extent. We show how our framework can be configured for object classification, action recognition and action similarity labeling. Importantly, our approach preserves the spatial and temporal coherence of spike events, while requiring less computation and memory. The experimental validation shows that our proposed framework outperforms all recent methods on standard datasets. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we introduce, evaluate and make available the American Sign Language letters (ASL-DVS), as well as human action dataset (UCF101-DVS, HMDB51-DVS and ASLAN-DVS).
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG
Authors: Ran Xin, Usman A. Khan, Soummya Kar
Date: 2019-10-08 00:00:00
Tasks: Stochastic Optimization

Decentralized stochastic optimization has recently benefited from gradient tracking methods \cite{DSGT_Pu,DSGT_Xin} providing efficient solutions for large-scale empirical risk minimization problems. In Part I \cite{GT_SAGA} of this work, we develop \textbf{\texttt{GT-SAGA}} that is based on a decentralized implementation of SAGA \cite{SAGA} using gradient tracking and discuss regimes of practical interest where \textbf{\texttt{GT-SAGA}} outperforms existing decentralized approaches in terms of the total number of local gradient computations. In this paper, we describe \textbf{\texttt{GT-SVRG}} that develops a decentralized gradient tracking based implementation of SVRG \cite{SVRG}, another well-known variance-reduction technique. We show that the convergence rate of \textbf{\texttt{GT-SVRG}} matches that of \textbf{\texttt{GT-SAGA}} for smooth and strongly-convex functions and highlight different trade-offs between the two algorithms in various settings.
A systematic review of smartphone-based human activity recognition for health research
Authors: Marcin Straczkiewicz, Peter James, Jukka-Pekka Onnela
Date: 2019-10-07 00:00:00
Tasks: Activity Recognition, Articles, Human Activity Recognition

Background: Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarize the existing approaches to smartphone-based HAR. Methods: We systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Results: We identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices and their alternatives. Conclusions: Smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Authors: Varun Ravi Kumar, Sandesh Athni Hiremath, Stefan Milz, Christian Witt, Clement Pinnard, Senthil Yogamani, Patrick Mader
Date: 2019-10-07 00:00:00
Tasks: Autonomous Driving, Self-Supervised Learning

Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view ($>180^{\circ}$). However, they come at the cost of strong non-linear distortions which require more complex algorithms. In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. Obtaining accurate and dense depth supervision is difficult in practice, but self-supervised learning approaches show promising results and could potentially overcome the problem. We present a novel self-supervised scale-aware framework for learning Euclidean distance and ego-motion from raw monocular fisheye videos without applying rectification. While it is possible to perform piece-wise linear approximation of fisheye projection surface and apply standard rectilinear models, it has its own set of issues like re-sampling distortion and discontinuities in transition regions. To encourage further research in this area, we will release our dataset as part of the WoodScape project \cite{yogamani2019woodscape}. We further evaluated the proposed algorithm on the KITTI dataset and obtained state-of-the-art results comparable to other self-supervised monocular methods. Qualitative results on an unseen fisheye video demonstrate impressive performance https://youtu.be/Sgq1WzoOmXg.