pwc-archive/papers-with-abstracts huggingface dataset:
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning
Authors: Samarth Sinha, Ajay Mandlekar, Animesh Garg
Date: 2021-03-10 00:00:00
Tasks: Autonomous Driving, D4RL, Data Augmentation, Offline RL, Out-of-Distribution Generalization, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to learn useful skills from data that can then be deployed in the environment in real-world settings where interactions may be costly or dangerous, such as autonomous driving or factories. However, current algorithms overfit to the dataset they are trained on and exhibit poor out-of-distribution generalization to the environment when deployed. In this paper, we study the effectiveness of performing data augmentations on the state space, and study 7 different augmentation schemes and how they behave with existing offline RL algorithms. We then combine the best data performing augmentation scheme with a state-of-the-art Q-learning technique, and improve the function approximation of the Q-networks by smoothening out the learned state-action space. We experimentally show that using this Surprisingly Simple Self-Supervision technique in RL (S4RL), we significantly improve over the current state-of-the-art algorithms on offline robot learning environments such as MetaWorld [1] and RoboSuite [2,3], and benchmark datasets such as D4RL [4].
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Authors: Joe Watson, Jan Peters
Date: 2021-03-10 00:00:00

Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization. Control as inference is an approach that frames stochastic control as an equivalent inference problem, and has demonstrated desirable qualities over existing methods, namely in exploration and regularization. We look specifically at the input inference for control (i2c) algorithm, and derive three key characteristics that enable advanced trajectory optimization: An `expert' linear Gaussian controller that combines the benefits of open-loop optima and closed-loop variance reduction when optimizing for nonlinear systems, inherent adaptive risk sensitivity from the inference formulation, and covariance control functionality with only a minor algorithmic adjustment.
Affine-Mapping based Variational Ensemble Kalman Filter
Authors: Linjie Wen, Jinglai Li
Date: 2021-03-10 00:00:00

We propose an affine-mapping based variational Ensemble Kalman filter for sequential Bayesian filtering problems with generic observation models. Specifically, the proposed method is formulated as to construct an affine mapping from the prior ensemble to the posterior one, and the affine mapping is computed via a variational Bayesian formulation, i.e., by minimizing the Kullback-Leibler divergence between the transformed distribution through the affine mapping and the actual posterior. Some theoretical properties of resulting optimization problem are studied and a gradient descent scheme is proposed to solve the resulting optimization problem. With numerical examples we demonstrate that the method has competitive performance against existing methods.
What is Multimodality?
Authors: Letitia Parcalabescu, Nils Trost, Anette Frank
Date: 2021-03-10 00:00:00
Proceeding: ACL (mmsr, IWCS) 2021 6
Tasks: BIG-bench Machine Learning, Position

The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the machine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack
Authors: Yam Sharon, David Berend, Yang Liu, Asaf Shabtai, Yuval Elovici
Date: 2021-03-10 00:00:00
Tasks: Intrusion Detection, Network Intrusion Detection

Network intrusion attacks are a known threat. To detect such attacks, network intrusion detection systems (NIDSs) have been developed and deployed. These systems apply machine learning models to high-dimensional vectors of features extracted from network traffic to detect intrusions. Advances in NIDSs have made it challenging for attackers, who must execute attacks without being detected by these systems. Prior research on bypassing NIDSs has mainly focused on perturbing the features extracted from the attack traffic to fool the detection system, however, this may jeopardize the attack's functionality. In this work, we present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack that can bypass a variety of NIDSs. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. The trained LSTM is used to set the time differences between the malicious traffic packets (attack), without changing their content, such that they will "behave" like benign network traffic and will not be detected as an intrusion. We evaluate TANTRA on eight common intrusion attacks and three state-of-the-art NIDS systems, achieving an average success rate of 99.99\% in network intrusion detection system evasion. We also propose a novel mitigation technique to address this new evasion attack.
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
Authors: Anna Kuzina, Max Welling, Jakub M. Tomczak
Date: 2021-03-10 00:00:00

In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just get a drastically different code (unsupervised attack). We examine the influence of model modifications ($\beta$-VAE, NVAE) on the robustness of VAEs and suggest metrics to quantify it.
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
Authors: Amir Khazraei, Spencer Hallyburton, Qitong Gao, Yu Wang, Miroslav Pajic
Date: 2021-03-10 00:00:00
Tasks: Anomaly Detection

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have, our objective is to develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN); both models are trained offline, using a cost function that combines the attack effects on the estimation error and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating such effective sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.
Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images
Authors: Jack White, Alex Codoreanu, Ignacio Zuleta, Colm Lynch, Giovanni Marchisio, Stephen Petrie, Alan R. Duffy
Date: 2021-03-10 00:00:00
Tasks: Image Reconstruction, Super-Resolution

Modern deep Super-Resolution (SR) networks have established themselves as valuable techniques in image reconstruction and enhancement. However, these networks are normally trained and tested on benchmark image data that lacks the typical image degrading noise present in real images. In this paper, we test the feasibility of using deep SR in real remote sensing payloads by assessing SR performance in reconstructing realistically degraded satellite images. We demonstrate that a state-of-the-art SR technique called Enhanced Deep Super-Resolution Network (EDSR), without domain specific pre-training, can recover encoded pixel data on images with poor ground sampling distance, provided the ground resolved distance is sufficient. However, this recovery varies amongst selected geographical types. Our results indicate that custom training has potential to further improve reconstruction of overhead imagery, and that new satellite hardware should prioritise optical performance over minimising pixel size as deep SR can overcome a lack of the latter but not the former.
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Authors: Wenting Zhao, Shufeng Kong, Junwen Bai, Daniel Fink, Carla Gomes
Date: 2021-03-09 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global change. A central goal for conservation planning and monitoring is the ability to accurately predict the occurrence of species communities and how these communities change over space and time. This in turn leads to a challenging and long-standing problem in the field of computer science - how to perform ac-curate multi-label classification with hundreds of labels? The key challenge of this problem is its exponential-sized output space with regards to the number of labels to be predicted.Therefore, it is essential to facilitate the learning process by exploiting correlations (or dependency) among labels. Previous methods mostly focus on modelling the correlation on label pairs; however, complex relations between real-world objects often go beyond second order. In this paper, we pro-pose a novel framework for multi-label classification, High-order Tie-in Variational Autoencoder (HOT-VAE), which per-forms adaptive high-order label correlation learning. We experimentally verify that our model outperforms the existing state-of-the-art approaches on a bird distribution dataset on both conventional F1 scores and a variety of ecological metrics. To show our method is general, we also perform empirical analysis on seven other public real-world datasets in several application domains, and Hot-VAE exhibits superior performance to previous methods.
The AI Index 2021 Annual Report
Authors: Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, John Etchemendy, Deep Ganguli, Barbara Grosz, Terah Lyons, James Manyika, Juan Carlos Niebles, Michael Sellitto, Yoav Shoham, Jack Clark, Raymond Perrault
Date: 2021-03-09 00:00:00

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices
Authors: Toygun Basaklar, Yigit Tuncel, Shruti Yadav Narayana, Suat Gumussoy, Umit Y. Ogras
Date: 2021-03-08 00:00:00

Hyperdimensional computing (HDC) has emerged as a new light-weight learning algorithm with smaller computation and energy requirements compared to conventional techniques. In HDC, data points are represented by high-dimensional vectors (hypervectors), which are mapped to high-dimensional space (hyperspace). Typically, a large hypervector dimension ($\geq1000$) is required to achieve accuracies comparable to conventional alternatives. However, unnecessarily large hypervectors increase hardware and energy costs, which can undermine their benefits. This paper presents a technique to minimize the hypervector dimension while maintaining the accuracy and improving the robustness of the classifier. To this end, we formulate the hypervector design as a multi-objective optimization problem for the first time in the literature. The proposed approach decreases the hypervector dimension by more than $32\times$ while maintaining or increasing the accuracy achieved by conventional HDC. Experiments on a commercial hardware platform show that the proposed approach achieves more than one order of magnitude reduction in model size, inference time, and energy consumption. We also demonstrate the trade-off between accuracy and robustness to noise and provide Pareto front solutions as a design parameter in our hypervector design.
Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records
Authors: Ye Ye, Andrew Gu
Date: 2021-03-08 00:00:00
Tasks: Transfer Learning

During an infectious disease pandemic, it is critical to share electronic medical records or models (learned from these records) across regions. Applying one region's data/model to another region often have distribution shift issues that violate the assumptions of traditional machine learning techniques. Transfer learning can be a solution. To explore the potential of deep transfer learning algorithms, we applied two data-based algorithms (domain adversarial neural networks and maximum classifier discrepancy) and model-based transfer learning algorithms to infectious disease detection tasks. We further studied well-defined synthetic scenarios where the data distribution differences between two regions are known. Our experiments show that, in the context of infectious disease classification, transfer learning may be useful when (1) the source and target are similar and the target training data is insufficient and (2) the target training data does not have labels. Model-based transfer learning works well in the first situation, in which case the performance closely matched that of the data-based transfer learning models. Still, further investigation of the domain shift in real world research data to account for the drop in performance is needed.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
Authors: Jeremy Beauchamp, Razvan Bunescu, Cindy Marling, Zhongen Li, Chang Liu
Date: 2021-03-06 00:00:00
Tasks: Management, Time Series Analysis, Time Series Forecasting

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at "what if" scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.
Robust and Low Complexity Beam Tracking with Monopulse Signal for UAV Communications
Authors: Ha-Lim Song, Young-Chai Ko
Date: 2020-07-02 00:00:00

UAV communications based on an antenna array entail a beam tracking technology for reliable link acquisition. Unlike conventional cellular communication, beam tracking in UAV communication addresses new issues such as mobility and abrupt channel disconnection from UAV's perturbation. To deal with these issues, we propose a beam tracking scheme based on extended Kalman filter (EKF) using a monopulse signal, which can provide (1) higher robustness by offering a reliable link in the estimated spatial direction and (2) lower complexity compared with the existing conventional beam tracking schemes. We point out the limitations of using a beamformed signal as a measurement model for a Kalman filter (KF) based scheme and instead utilize the monopulse signal as a more plausible model. For the performance evaluation, we derive the upper bound of the mean square error for spatial angle estimation of UAV and confirm that the proposed scheme is stable with a certain bounded error. We also show from various simulations that the proposed scheme can efficiently track UAV and detect beam disconnection every time frame using a beamformed signal.
PyTorch CurveBall - A second-order optimizer for deep networks
Authors: João F. Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi
Date: 2018-05-21 00:00:00

We propose a fast second-order method that can be used as a drop-in replacementfor current deep learning solvers. Compared to stochastic gradient descent (SGD),it only requires two additional forward-mode automatic differentiation operationsper iteration, which has a computational cost comparable to two standard forwardpasses and is easy to implement.   Our method addresses long-standing issueswith current second-order solvers, which invert an approximate Hessian matrixevery iteration exactly or by conjugate-gradient methods, a procedure that is bothcostly and sensitive to noise. Instead, we propose to keep a single estimate of thegradient projected by the inverse Hessian matrix, and update it once per iteration.This estimate has the same size and is similar to the momentum variable thatis commonly used in SGD. No estimate of the Hessian is maintained.  We firstvalidate our method, calledCURVEBALL, on small problems with known closed-form solutions (noisy Rosenbrock function and degenerate 2-layer linear networks),where current deep learning solvers seem to struggle. We then train several largemodels on CIFAR and ImageNet, including ResNet and VGG-f networks, where wedemonstrate faster convergence with no hyperparameter tuning. Code is available.
Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
Authors: Joaquim Dias Garcia, Alexandre Street, Tito Homem-de-Mello, Francisco D. Muñoz
Date: 2021-02-26 00:00:00
Tasks: Bilevel Optimization, Decision Making, Demand Forecasting

Forecasting and decision-making are generally modeled as two sequential steps with no feedback, following an open-loop approach. In this paper, we present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized through a bilevel optimization problem. We present our methodology in a general format and prove that the solution converges to the best estimator in terms of the expected cost of the selected application. Then, we propose two solution methods: an exact method based on the KKT conditions of the second-level problem and a scalable heuristic approach suitable for decomposition methods. The proposed methodology is applied to the relevant problem of defining dynamic reserve requirements and conditional load forecasts, offering an alternative approach to current ad hoc procedures implemented in industry practices. We benchmark our methodology with the standard sequential least-squares forecast and dispatch planning process. We apply the proposed methodology to an illustrative system and to a wide range of instances, from dozens of buses to large-scale realistic systems with thousands of buses. Our results show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.
Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modelling, and Hardware-in-the-Loop Evaluation
Authors: Stefan Zelenbaba, Benjamin Rainer, Markus Hofer, David Löschenbrand, Laura Bernadó, Anja Dakić, Thomas Zemen
Date: 2021-03-11 00:00:00
Tasks: Autonomous Vehicles

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than $3.6\,\mathrm{dB}$, $78\,\mathrm{ns}$, and $52\,\mathrm{Hz}$, respectively, for $80\%$ of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for $86\%$ of the simulation duration.
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach
Authors: Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
Date: 2021-03-11 00:00:00
Tasks: Causal Inference, Econometrics, Time Series, Time Series Analysis

The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. In this paper, we propose a novel approach, C-ARIMA, to define and estimate the causal effect of an intervention in a time series setting under the RCM. We first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention; we then check the validity of the proposed method with an extensive simulation study, comparing its performance against a standard intervention analysis approach. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The CausalArima R package provides an implementation of our proposed approach.