pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
Authors: Sina Sharif Mansouri, Farhad Pourkamali-Anaraki, Miguel Castano Arranz, Ali-akbar Agha-mohammadi, Joel Burdick, George Nikolakopoulos
Date: 2020-06-07 00:00:00
Tasks: Clustering, Navigate

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology.
Efficient Poverty Mapping using Deep Reinforcement Learning
Authors: Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
Date: 2020-06-07 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. This creates a substantial hurdle to the efficient scaling and widespread adoption of high-resolution-based approaches. To reduce acquisition costs while maintaining accuracy, we propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images, prior to performing a deep learning task on the high-resolution images. We apply this approach to the task of poverty prediction in Uganda, building on an earlier approach that used object detection to count objects and use these counts to predict poverty. Our approach exceeds previous performance benchmarks on this task while using 80% fewer high-resolution images. Our approach could have application in many sustainability domains that require high-resolution imagery.
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Authors: Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha
Date: 2020-06-07 00:00:00
Tasks: counterfactual, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities. Our method aims to leverage these commonalities by asking the question: ``What is the expected utility of each agent when only considering a randomly selected sub-group of its observed entities?'' By posing this counterfactual question, we can recognize state-action trajectories within sub-groups of entities that we may have encountered in another task and use what we learned in that task to inform our prediction in the current one. We then reconstruct a prediction of the full returns as a combination of factors considering these disjoint groups of entities and train this ``randomly factorized" value function as an auxiliary objective for value-based multi-agent reinforcement learning. By doing so, our model can recognize and leverage similarities across tasks to improve learning efficiency in a multi-task setting. Our approach, Randomized Entity-wise Factorization for Imagined Learning (REFIL), outperforms all strong baselines by a significant margin in challenging multi-task StarCraft micromanagement settings.
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Authors: Qian Lou, Song Bian, Lei Jiang
Date: 2020-06-07 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Deep Reinforcement Learning, Privacy Preserving

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e.g., $\sim100$ seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost $93\%$ inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by $53\%\sim70\%$ with negligible loss of accuracy.
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars
Authors: Ran Emuna, Avinoam Borowsky, Armin Biess
Date: 2020-06-07 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, Deep Reinforcement Learning, Gaussian Processes, Self-Driving Cars, Unity

The technological and scientific challenges involved in the development of autonomous vehicles (AVs) are currently of primary interest for many automobile companies and research labs. However, human-controlled vehicles are likely to remain on the roads for several decades to come and may share with AVs the traffic environments of the future. In such mixed environments, AVs should deploy human-like driving policies and negotiation skills to enable smooth traffic flow. To generate automated human-like driving policies, we introduce a model-free, deep reinforcement learning approach to imitate an experienced human driver's behavior. We study a static obstacle avoidance task on a two-lane highway road in simulation (Unity). Our control algorithm receives a stochastic feedback signal from two sources: a model-driven part, encoding simple driving rules, such as lane-keeping and speed control, and a stochastic, data-driven part, incorporating human expert knowledge from driving data. To assess the similarity between machine and human driving, we model distributions of track position and speed as Gaussian processes. We demonstrate that our approach leads to human-like driving policies.
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Authors: Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine Udell
Date: 2020-06-07 00:00:00
Tasks: AutoML

Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system to address this challenge: an automated system to design a supervised learning pipeline. Our system uses matrix and tensor factorization as surrogate models to model the combinatorial pipeline search space. Under these models, we develop greedy experiment design protocols to efficiently gather information about a new dataset. Experiments on large corpora of real-world classification problems demonstrate the effectiveness of our approach.
Generating Realistic Stock Market Order Streams
Authors: Junyi Li, Xitong Wang, Yaoyang Lin, Arunesh Sinha, Micheal P. Wellman
Date: 2020-06-07 00:00:00
Proceeding: ICLR 2019 5

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.
Extensions and limitations of randomized smoothing for robustness guarantees
Authors: Jamie Hayes
Date: 2020-06-07 00:00:00

Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. It operates in a black-box and so certification is not constrained by the size of the classifier's architecture. Here, we extend the work of Li et al. \cite{li2018second}, studying how the choice of divergence between smoothing measures affects the final robustness guarantee, and how the choice of smoothing measure itself can lead to guarantees in differing threat models. To this end, we develop a method to certify robustness against any $\ell_p$ ($p\in\mathbb{N}_{>0}$) minimized adversarial perturbation. We then demonstrate a negative result, that randomized smoothing suffers from the curse of dimensionality; as $p$ increases, the effective radius around an input one can certify vanishes.
Machine learning dynamics of phase separation in correlated electron magnets
Authors: Puhan Zhang, Preetha Saha, Gia-Wei Chern
Date: 2020-06-07 00:00:00
Tasks: BIG-bench Machine Learning

We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. This model, also known as the ferromagnetic Kondo lattice model, is believed to be relevant for the colossal magnetoresistance phenomenon. Real-space simulations of such inhomogeneous states with exchange forces computed from the electron Hamiltonian can be prohibitively expensive for large systems. Here we show that linear-scaling exchange field computation can be achieved using neural networks trained by datasets from exact calculation on small lattices. Our Landau-Lifshitz dynamics simulations based on machine-learning potentials nicely reproduce not only the nonequilibrium relaxation process, but also correlation functions that agree quantitatively with exact simulations. Our work paves the way for large-scale dynamical simulations of correlated electron systems using machine-learning models.
Thoracic Disease Identification and Localization using Distance Learning and Region Verification
Authors: Cheng Zhang, Francine Chen, Yan-Ying Chen
Date: 2020-06-07 00:00:00
Tasks: Classification, General Classification

The identification and localization of diseases in medical images using deep learning models have recently attracted significant interest. Existing methods only consider training the networks with each image independently and most leverage an activation map for disease localization. In this paper, we propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Concretely, we adapt a distance learning framework for multi-label disease classification to differentiate subtle disease features. Additionally, we feed back the features of the predicted class-specific regions to a separate classifier during training to better verify the localized diseases. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance. Moreover, the distance learning and region verification modules can capture essential information for better localization than baseline models without these modules.
Learning Behaviors with Uncertain Human Feedback
Authors: Xu He, Haipeng Chen, Bo An
Date: 2020-06-07 00:00:00

Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers. For example, the reward of a sub-optimal action can be stochastic and sometimes exceeds that of the optimal action, which is common in games or real-world. Trainers are likely to provide positive feedback to sub-optimal actions, negative feedback to the optimal actions and even do not provide feedback in some confusing situations. Existing works, which utilize the Expectation Maximization (EM) algorithm and treat the feedback model as hidden parameters, do not consider uncertainties in the learning environment and human feedback. To address this challenge, we introduce a novel feedback model that considers the uncertainty of human feedback. However, this incurs intractable calculus in the EM algorithm. To this end, we propose a novel approximate EM algorithm, in which we approximate the expectation step with the Gradient Descent method. Experimental results in both synthetic scenarios and two real-world scenarios with human participants demonstrate the superior performance of our proposed approach.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
Authors: Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Vazirizade, Di Lu, Said El Said, Alex Jaimes, Hiba Baroud, Yevgeniy Vorobeychik, Mykel Kochenderfer, Abhishek Dubey
Date: 2020-06-07 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Management

In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, and (c) computer-aided dispatch for emergency response. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. We conclude by illustrating open challenges and opportunities for future research in this complex domain.
EnK: Encoding time-information in convolution
Authors: Avinash Kumar Singh, Chin-Teng Lin
Date: 2020-06-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series Analysis

Recent development in deep learning techniques has attracted attention in decoding and classification in EEG signals. Despite several efforts utilizing different features of EEG signals, a significant research challenge is to use time-dependent features in combination with local and global features. There have been several efforts to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information by incorporating hand-crafted features, slicing the input data in a smaller time-windows, and recurrent convolution. However, these approaches partially solve the problem, but simultaneously hinder the CNN's capability to learn from unknown information that might be present in the data. To solve this, we have proposed a novel time encoding kernel (EnK) approach, which introduces the increasing time information during convolution operation in CNN. The encoded information by EnK lets CNN learn time-dependent features in-addition to local and global features. We performed extensive experiments on several EEG datasets: cognitive conflict (CC), physical-human robot collaboration (pHRC), P300 visual-evoked potentials, movement-related cortical potentials (MRCP). EnK outperforms the state-of-art by 12\% (F1 score). Moreover, the EnK approach required only one additional parameter to learn and can be applied to a virtually any CNN architectures with minimal efforts. These results support our methodology and show high potential to improve CNN performance in the context of time-series data in general.
Finger Texture Biometric Characteristic: a Survey
Authors: Raid R. O. Al-Nima, Tingting Han, Taolue Chen, Satnam Dlay, Jonathon Chambers
Date: 2020-06-07 00:00:00
Tasks: Survey

\begin{abstract} In recent years, the Finger Texture (FT) has attracted considerable attention as a biometric characteristic. It can provide efficient human recognition performance, because it has different human-specific features of apparent lines, wrinkles and ridges distributed along the inner surface of all fingers. Also, such pattern structures are reliable, unique and remain stable throughout a human's life. Efficient biometric systems can be established based only on FTs. In this paper, a comprehensive survey of the relevant FT studies is presented. We also summarise the main drawbacks and obstacles of employing the FT as a biometric characteristic, and provide useful suggestions to further improve the work on FT. \end{abstract}
Uncertainty-Aware Deep Classifiers using Generative Models
Authors: Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki
Date: 2020-06-07 00:00:00
Tasks: Anomaly Detection

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Authors: Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Date: 2020-06-07 00:00:00
Tasks: BIG-bench Machine Learning

Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. However, backprop is often criticised for lacking biological plausibility. Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates. The power of backprop, however, lies not in its instantiation in MLPs, but rather in the concept of automatic differentiation which allows for the optimisation of any differentiable program expressed as a computation graph. Here, we demonstrate that predictive coding converges asymptotically (and in practice rapidly) to exact backprop gradients on arbitrary computation graphs using only local learning rules. We apply this result to develop a straightforward strategy to translate core machine learning architectures into their predictive coding equivalents. We construct predictive coding CNNs, RNNs, and the more complex LSTMs, which include a non-layer-like branching internal graph structure and multiplicative interactions. Our models perform equivalently to backprop on challenging machine learning benchmarks, while utilising only local and (mostly) Hebbian plasticity. Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry, and may also contribute to the development of completely distributed neuromorphic architectures.
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
Authors: Shiying Xiong, Xingzhe He, Yunjin Tong, Runze Liu, Bo Zhu
Date: 2020-06-07 00:00:00

We introduce Roe Neural Networks (RoeNets) that can predict the discontinuity of the hyperbolic conservation laws (HCLs) based on short-term discontinuous and even continuous training data. Our methodology is inspired by Roe approximate Riemann solver (P. L. Roe, J. Comput. Phys., vol. 43, 1981, pp. 357--372), which is one of the most fundamental HCLs numerical solvers. In order to accurately solve the HCLs, Roe argues the need to construct a Roe matrix that fulfills "Property U", including diagonalizable with real eigenvalues, consistent with the exact Jacobian, and preserving conserved quantities. However, the construction of such matrix cannot be achieved by any general numerical method. Our model made a breakthrough improvement in solving the HCLs by applying Roe solver under a neural network perspective. To enhance the expressiveness of our model, we incorporate pseudoinverses into a novel context to enable a hidden dimension so that we are flexible with the number of parameters. The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches. We demonstrate that our model can generate highly accurate predictions of evolution of convection without dissipation and the discontinuity of hyperbolic systems from smooth training data.