April 2, 2020

3544 words 17 mins read

Paper Group ANR 185

Paper Group ANR 185

Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing. Near-optimal Regret Bounds for Stochastic Shortest Path. Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning. Non-Local Part-Aware Point Cloud Denoising. Classificati …

Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing

Title Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
Authors Carmelo Sferrazza, Thomas Bi, Raffaello D’Andrea
Abstract Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize when applied to various tasks. This paper focuses on both these aspects with regard to a vision-based tactile sensor, which aims to reconstruct the distribution of the three-dimensional contact forces applied on its soft surface. Accurate models for the soft materials and the camera projection, derived via state-of-the-art techniques in the respective domains, are employed to generate a dataset in simulation. A strategy is proposed to train a tailored deep neural network entirely from the simulation data. The resulting learning architecture is directly transferable across multiple tactile sensors without further training and yields accurate predictions on real data, while showing promising generalization capabilities to unseen contact conditions.
Published 2020-03-05
URL https://arxiv.org/abs/2003.02640v1
PDF https://arxiv.org/pdf/2003.02640v1.pdf
PWC https://paperswithcode.com/paper/learning-the-sense-of-touch-in-simulation-a

Near-optimal Regret Bounds for Stochastic Shortest Path

Title Near-optimal Regret Bounds for Stochastic Shortest Path
Authors Alon Cohen, Haim Kaplan, Yishay Mansour, Aviv Rosenberg
Abstract Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In the learning formulation of the problem, the agent is unaware of the environment dynamics (i.e., the transition function) and has to repeatedly play for a given number of episodes while reasoning about the problem’s optimal solution. Unlike other well-studied models in reinforcement learning (RL), the length of an episode is not predetermined (or bounded) and is influenced by the agent’s actions. Recently, Tarbouriech et al. (2019) studied this problem in the context of regret minimization and provided an algorithm whose regret bound is inversely proportional to the square root of the minimum instantaneous cost. In this work we remove this dependence on the minimum cost—we give an algorithm that guarantees a regret bound of $\widetilde{O}(B_\star S \sqrt{A K})$, where $B_\star$ is an upper bound on the expected cost of the optimal policy, $S$ is the set of states, $A$ is the set of actions and $K$ is the number of episodes. We additionally show that any learning algorithm must have at least $\Omega(B_\star \sqrt{S A K})$ regret in the worst case.
Published 2020-02-23
URL https://arxiv.org/abs/2002.09869v1
PDF https://arxiv.org/pdf/2002.09869v1.pdf
PWC https://paperswithcode.com/paper/near-optimal-regret-bounds-for-stochastic

Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning

Title Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning
Authors Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez
Abstract Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early. While fluid bolus therapy and vasopressors are common treatments, it is often unclear which interventions to give, in what amounts, and for how long. Observational data in the form of electronic health records can provide a source for helping inform these choices from past events, but often it is not possible to identify a single best strategy from observational data alone. In such situations, we argue it is important to expose the collection of plausible options to a provider. To this end, we develop SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement Learning, to identify distinct treatment options that are supported in the data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension presented. Our learned policies perform comparably to the observed physician behaviors, while providing different, plausible alternatives for treatment decisions.
Published 2020-01-09
URL https://arxiv.org/abs/2001.03224v1
PDF https://arxiv.org/pdf/2001.03224v1.pdf
PWC https://paperswithcode.com/paper/identifying-distinct-effective-treatments-for

Non-Local Part-Aware Point Cloud Denoising

Title Non-Local Part-Aware Point Cloud Denoising
Authors Chao Huang, Ruihui Li, Xianzhi Li, Chi-Wing Fu
Abstract This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes. Different from existing works that explore small local patches, we design the non-local learning unit (NLU) customized with a graph attention module to adaptively capture non-local semantically-related features over the entire point cloud. To enhance the denoising performance, we cascade a series of NLUs to progressively distill the noise features from the noisy inputs. Further, besides the conventional surface reconstruction loss, we formulate a semantic part loss to regularize the predictions towards the relevant parts and enable denoising in a part-aware manner. Lastly, we performed extensive experiments to evaluate our method, both quantitatively and qualitatively, and demonstrate its superiority over the state-of-the-arts on both synthetic and real-scanned noisy inputs.
Tasks Denoising
Published 2020-03-14
URL https://arxiv.org/abs/2003.06631v1
PDF https://arxiv.org/pdf/2003.06631v1.pdf
PWC https://paperswithcode.com/paper/non-local-part-aware-point-cloud-denoising

Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection

Title Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection
Authors Xiaoheng Zhang, Yongming Li, Pin Wang, Xiaoheng Tan, Yuchuan Liu
Abstract Labeled speech data from patients with Parkinsons disease (PD) are scarce, and the statistical distributions of training and test data differ significantly in the existing datasets. To solve these problems, dimensional reduction and sample augmentation must be considered. In this paper, a novel PD classification algorithm based on sparse kernel transfer learning combined with a parallel optimization of samples and features is proposed. Sparse transfer learning is used to extract effective structural information of PD speech features from public datasets as source domain data, and the fast ADDM iteration is improved to enhance the information extraction performance. To implement the parallel optimization, the potential relationships between samples and features are considered to obtain high-quality combined features. First, features are extracted from a specific public speech dataset to construct a feature dataset as the source domain. Then, the PD target domain, including the training and test datasets, is encoded by convolution sparse coding, which can extract more in-depth information. Next, parallel optimization is implemented. To further improve the classification performance, a convolution kernel optimization mechanism is designed. Using two representative public datasets and one self-constructed dataset, the experiments compare over thirty relevant algorithms. The results show that when taking the Sakar dataset, MaxLittle dataset and DNSH dataset as target domains, the proposed algorithm achieves obvious improvements in classification accuracy. The study also found large improvements in the algorithms in this paper compared with nontransfer learning approaches, demonstrating that transfer learning is both more effective and has a more acceptable time cost.
Tasks Feature Selection, Transfer Learning
Published 2020-02-10
URL https://arxiv.org/abs/2002.03716v1
PDF https://arxiv.org/pdf/2002.03716v1.pdf
PWC https://paperswithcode.com/paper/classification-algorithm-of-speech-data-of

A Decentralized Policy with Logarithmic Regret for a Class of Multi-Agent Multi-Armed Bandit Problems with Option Unavailability Constraints and Stochastic Communication Protocols

Title A Decentralized Policy with Logarithmic Regret for a Class of Multi-Agent Multi-Armed Bandit Problems with Option Unavailability Constraints and Stochastic Communication Protocols
Authors Pathmanathan Pankayaraj, D. H. S. Maithripala, J. M. Berg
Abstract This paper considers a multi-armed bandit (MAB) problem in which multiple mobile agents receive rewards by sampling from a collection of spatially dispersed stochastic processes, called bandits. The goal is to formulate a decentralized policy for each agent, in order to maximize the total cumulative reward over all agents, subject to option availability and inter-agent communication constraints. The problem formulation is motivated by applications in which a team of autonomous mobile robots cooperates to accomplish an exploration and exploitation task in an uncertain environment. Bandit locations are represented by vertices of the spatial graph. At any time, an agent’s option consist of sampling the bandit at its current location, or traveling along an edge of the spatial graph to a new bandit location. Communication constraints are described by a directed, non-stationary, stochastic communication graph. At any time, agents may receive data only from their communication graph in-neighbors. For the case of a single agent on a fully connected spatial graph, it is known that the expected regret for any optimal policy is necessarily bounded below by a function that grows as the logarithm of time. A class of policies called upper confidence bound (UCB) algorithms asymptotically achieve logarithmic regret for the classical MAB problem. In this paper, we propose a UCB-based decentralized motion and option selection policy and a non-stationary stochastic communication protocol that guarantee logarithmic regret. To our knowledge, this is the first such decentralized policy for non-fully connected spatial graphs with communication constraints. When the spatial graph is fully connected and the communication graph is stationary, our decentralized algorithm matches or exceeds the best reported prior results from the literature.
Published 2020-03-29
URL https://arxiv.org/abs/2003.12968v2
PDF https://arxiv.org/pdf/2003.12968v2.pdf
PWC https://paperswithcode.com/paper/a-decentralized-policy-with-logarithmic

Photonic tensor cores for machine learning

Title Photonic tensor cores for machine learning
Authors Mario Miscuglio, Volker J. Sorger
Abstract With an ongoing trend in computing hardware towards increased heterogeneity, domain-specific co-processors are emerging as alternatives to centralized paradigms. The tensor core unit (TPU) has shown to outperform graphic process units by almost 3-orders of magnitude enabled by higher signal throughout and energy efficiency. In this context, photons bear a number of synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non van-Neumann architectures. While several photonic neural network designs have been explored, a photonic TPU to perform matrix vector multiplication and summation is yet outstanding. Here we introduced an integrated photonics-based TPU by strategically utilizing a) photonic parallelism via wavelength division multiplexing, b) high 2 Peta-operations-per second throughputs enabled by 10s of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and c) zero power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show that the performance of this 8-bit photonic TPU can be 2-3 orders higher compared to an electrical TPU whilst featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.
Published 2020-02-01
URL https://arxiv.org/abs/2002.03780v1
PDF https://arxiv.org/pdf/2002.03780v1.pdf
PWC https://paperswithcode.com/paper/photonic-tensor-cores-for-machine-learning

BusTime: Which is the Right Prediction Model for My Bus Arrival Time?

Title BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
Authors Dairui Liu, Jingxiang Sun, Shen Wang
Abstract With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions. This type of applications help passengers to plan trips more efficiently without wasting unpredictable amount of waiting time at bus stops. Many studies focus on improving the prediction accuracy of various machine learning and statistical models, while much less work demonstrate their applicability of being deployed and used in realistic urban settings. This paper tries to fill this gap by proposing a general and practical evaluation framework for analysing various widely used prediction models (i.e. delay, k-nearest-neighbour, kernel regression, additive model, and recurrent neural network using long short term memory) for bus arrival time. In particular, this framework contains a raw bus GPS data pre-processing method that needs much less number of input data points while still maintain satisfactory prediction results. This pre-processing method enables various models to predict arrival time at bus stops only, by using a KD-tree based nearest point search method. Based on this framework, using raw bus GPS dataset in different scales from the city of Dublin, Ireland, we also present preliminary results for city managers by analysing the practical strengths and weaknesses in both training and predicting stages of commonly used prediction models.
Published 2020-03-20
URL https://arxiv.org/abs/2003.10373v1
PDF https://arxiv.org/pdf/2003.10373v1.pdf
PWC https://paperswithcode.com/paper/bustime-which-is-the-right-prediction-model

Simultaneous prediction and community detection for networks with application to neuroimaging

Title Simultaneous prediction and community detection for networks with application to neuroimaging
Authors Jesús Arroyo, Elizaveta Levina
Abstract Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. Increasingly the focus of network analysis is shifting towards using network information in some other prediction or inference task rather than just analyzing the network itself. In particular, in neuroimaging applications brain networks are available for multiple subjects and the goal is often to predict a phenotype of interest. Community structure is well known to be a feature of brain networks, typically corresponding to different regions of the brain responsible for different functions. There are standard parcellations of the brain into such regions, usually obtained by applying clustering methods to brain connectomes of healthy subjects. However, when the goal is predicting a phenotype or distinguishing between different conditions, these static communities from an unrelated set of healthy subjects may not be the most useful for prediction. Here we present a method for supervised community detection, aiming to find a partition of the network into communities that is most useful for predicting a particular response. We use a block-structured regularization penalty combined with a prediction loss function, and compute the solution with a combination of a spectral method and an ADMM optimization algorithm. We show that the spectral clustering method recovers the correct communities under a weighted stochastic block model. The method performs well on both simulated and real brain networks, providing support for the idea of task-dependent brain regions.
Tasks Community Detection
Published 2020-02-05
URL https://arxiv.org/abs/2002.01645v2
PDF https://arxiv.org/pdf/2002.01645v2.pdf
PWC https://paperswithcode.com/paper/simultaneous-prediction-and-community

Randomized Spectral Clustering in Large-Scale Stochastic Block Models

Title Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Authors Hai Zhang, Xiao Guo, Xiangyu Chang
Abstract Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenge to it. In this paper, we study spectral clustering using randomized sketching algorithms from a statistical perspective, where we typically assume the network data are generated from a stochastic block model. To do this, we first use the recent developed sketching algorithms to derive two randomized spectral clustering algorithms, namely, the random projection-based and the random sampling-based spectral clustering. Then we study the theoretical bounds of the resulting algorithms in terms of the approximation error for the population adjacency matrix, the misclustering error, and the estimation error for the link probability matrix. It turns out that, under mild conditions, the randomized spectral clustering algorithms perform similarly to the original one. We also conduct numerical experiments to support the theoretical findings.
Tasks Community Detection
Published 2020-01-20
URL https://arxiv.org/abs/2002.00839v1
PDF https://arxiv.org/pdf/2002.00839v1.pdf
PWC https://paperswithcode.com/paper/randomized-spectral-clustering-in-large-scale

Measurement-driven Analysis of an Edge-Assisted Object Recognition System

Title Measurement-driven Analysis of an Edge-Assisted Object Recognition System
Authors A. Galanopoulos, V. Valls, G. Iosifidis, D. J. Leith
Abstract We develop an edge-assisted object recognition system with the aim of studying the system-level trade-offs between end-to-end latency and object recognition accuracy. We focus on developing techniques that optimize the transmission delay of the system and demonstrate the effect of image encoding rate and neural network size on these two performance metrics. We explore optimal trade-offs between these metrics by measuring the performance of our real time object recognition application. Our measurements reveal hitherto unknown parameter effects and sharp trade-offs, hence paving the road for optimizing this key service. Finally, we formulate two optimization problems using our measurement-based models and following a Pareto analysis we find that careful tuning of the system operation yields at least 33% better performance for real time conditions, over the standard transmission method.
Tasks Object Recognition
Published 2020-03-07
URL https://arxiv.org/abs/2003.03584v1
PDF https://arxiv.org/pdf/2003.03584v1.pdf
PWC https://paperswithcode.com/paper/measurement-driven-analysis-of-an-edge

Visual Machine Learning: Insight through Eigenvectors, Chladni patterns and community detection in 2D particulate structures

Title Visual Machine Learning: Insight through Eigenvectors, Chladni patterns and community detection in 2D particulate structures
Authors Raj Kishore, S. Swayamjyoti, Shreeja Das, Ajay K. Gogineni, Zohar Nussinov, D. Solenov, Kisor K. Sahu
Abstract Machine learning (ML) is quickly emerging as a powerful tool with diverse applications across an extremely broad spectrum of disciplines and commercial endeavors. Typically, ML is used as a black box that provides little illuminating rationalization of its output. In the current work, we aim to better understand the generic intuition underlying unsupervised ML with a focus on physical systems. The systems that are studied here as test cases comprise of six different 2-dimensional (2-D) particulate systems of different complexities. It is noted that the findings of this study are generic to any unsupervised ML problem and are not restricted to materials systems alone. Three rudimentary unsupervised ML techniques are employed on the adjacency (connectivity) matrix of the six studied systems: (i) using principal eigenvalue and eigenvectors of the adjacency matrix, (ii) spectral decomposition, and (iii) a Potts model based community detection technique in which a modularity function is maximized. We demonstrate that, while solving a completely classical problem, ML technique produces features that are distinctly connected to quantum mechanical solutions. Dissecting these features help us to understand the deep connection between the classical non-linear world and the quantum mechanical linear world through the kaleidoscope of ML technique, which might have far reaching consequences both in the arena of physical sciences and ML.
Tasks Community Detection
Published 2020-01-02
URL https://arxiv.org/abs/2001.00345v1
PDF https://arxiv.org/pdf/2001.00345v1.pdf
PWC https://paperswithcode.com/paper/visual-machine-learning-insight-through

Appraisal Theories for Emotion Classification in Text

Title Appraisal Theories for Emotion Classification in Text
Authors Jan Hofmann, Enrica Troiano, Kai Sassenberg, Roman Klinger
Abstract Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events (for instance, that somebody experiences fear when they discover a snake because of the appraisal as being an unpleasant and non-controllable situation), even without having access to explicit reports what an experiencer of an emotion is feeling (for instance expressing this with the words “I am afraid."). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and effort associated with discovering the snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories.
Tasks Emotion Classification, Text Classification
Published 2020-03-31
URL https://arxiv.org/abs/2003.14155v2
PDF https://arxiv.org/pdf/2003.14155v2.pdf
PWC https://paperswithcode.com/paper/appraisal-theories-for-emotion-classification

Correspondence Networks with Adaptive Neighbourhood Consensus

Title Correspondence Networks with Adaptive Neighbourhood Consensus
Authors Shuda Li, Kai Han, Theo W. Costain, Henry Howard-Jenkins, Victor Prisacariu
Abstract In this paper, we tackle the task of establishing dense visual correspondences between images containing objects of the same category. This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations. We propose a convolutional neural network architecture, called adaptive neighbourhood consensus network (ANC-Net), that can be trained end-to-end with sparse key-point annotations, to handle this challenge. At the core of ANC-Net is our proposed non-isotropic 4D convolution kernel, which forms the building block for the adaptive neighbourhood consensus module for robust matching. We also introduce a simple and efficient multi-scale self-similarity module in ANC-Net to make the learned feature robust to intra-class variations. Furthermore, we propose a novel orthogonal loss that can enforce the one-to-one matching constraint. We thoroughly evaluate the effectiveness of our method on various benchmarks, where it substantially outperforms state-of-the-art methods.
Published 2020-03-26
URL https://arxiv.org/abs/2003.12059v1
PDF https://arxiv.org/pdf/2003.12059v1.pdf
PWC https://paperswithcode.com/paper/correspondence-networks-with-adaptive

Learning Inverse Rendering of Faces from Real-world Videos

Title Learning Inverse Rendering of Faces from Real-world Videos
Authors Yuda Qiu, Zhangyang Xiong, Kai Han, Zhongyuan Wang, Zixiang Xiong, Xiaoguang Han
Abstract In this paper we examine the problem of inverse rendering of real face images. Existing methods decompose a face image into three components (albedo, normal, and illumination) by supervised training on synthetic face data. However, due to the domain gap between real and synthetic face images, a model trained on synthetic data often does not generalize well to real data. Meanwhile, since no ground truth for any component is available for real images, it is not feasible to conduct supervised learning on real face images. To alleviate this problem, we propose a weakly supervised training approach to train our model on real face videos, based on the assumption of consistency of albedo and normal across different frames, thus bridging the gap between real and synthetic face images. In addition, we introduce a learning framework, called IlluRes-SfSNet, to further extract the residual map to capture the global illumination effects that give the fine details that are largely ignored in existing methods. Our network is trained on both real and synthetic data, benefiting from both. We comprehensively evaluate our methods on various benchmarks, obtaining better inverse rendering results than the state-of-the-art.
Published 2020-03-26
URL https://arxiv.org/abs/2003.12047v1
PDF https://arxiv.org/pdf/2003.12047v1.pdf
PWC https://paperswithcode.com/paper/learning-inverse-rendering-of-faces-from-real
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