April 2, 2020

3255 words 16 mins read

Paper Group ANR 131

Paper Group ANR 131

FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks. Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks. Optimizing Revenue while showing Relevant Assortments at Scale. Classification of Hyperspectral and LiDAR Data Using Coupled CNNs. Differential Dynamic Programming Neural Optimizer. Ill …

FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks

Title FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks
Authors Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff, Pooyan Jamshidi
Abstract One of the key challenges in designing machine learning systems is to determine the right balance amongst several objectives, which also oftentimes are incommensurable and conflicting. For example, when designing deep neural networks (DNNs), one often has to trade-off between multiple objectives, such as accuracy, energy consumption, and inference time. Typically, there is no single configuration that performs equally well for all objectives. Consequently, one is interested in identifying Pareto-optimal designs. Although different multi-objective optimization algorithms have been developed to identify Pareto-optimal configurations, state-of-the-art multi-objective optimization methods do not consider the different evaluation costs attending the objectives under consideration. This is particularly important for optimizing DNNs: the cost arising on account of assessing the accuracy of DNNs is orders of magnitude higher than that of measuring the energy consumption of pre-trained DNNs. We propose FlexiBO, a flexible Bayesian optimization method, to address this issue. We formulate a new acquisition function based on the improvement of the Pareto hyper-volume weighted by the measurement cost of each objective. Our acquisition function selects the next sample and objective that provides maximum information gain per unit of cost. We evaluated FlexiBO on 7 state-of-the-art DNNs for object detection, natural language processing, and speech recognition. Our results indicate that, when compared to other state-of-the-art methods across the 7 architectures we tested, the Pareto front obtained using FlexiBO has, on average, a 28.44% higher contribution to the true Pareto front and achieves 25.64% better diversity.
Tasks Object Detection, Speech Recognition
Published 2020-01-18
URL https://arxiv.org/abs/2001.06588v1
PDF https://arxiv.org/pdf/2001.06588v1.pdf
PWC https://paperswithcode.com/paper/flexibo-cost-aware-multi-objective

Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks

Title Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks
Authors Ali Balali, Masoud Asadpour, Ricardo Campos, Adam Jatowt
Abstract Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge bases, question answering, information retrieval and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the two above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path (SDP) in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods.
Tasks Information Retrieval, Question Answering
Published 2020-03-19
URL https://arxiv.org/abs/2003.08615v1
PDF https://arxiv.org/pdf/2003.08615v1.pdf
PWC https://paperswithcode.com/paper/joint-event-extraction-along-shortest

Optimizing Revenue while showing Relevant Assortments at Scale

Title Optimizing Revenue while showing Relevant Assortments at Scale
Authors Theja Tulabandhula, Deeksha Sinha
Abstract Scalable real-time assortment optimization has become essential in e-commerce operations due to the need for personalization and the availability of a large variety of items. While this can be done when there are simplistic assortment choices to be made, imposing constraints on the collection of feasible assortments gives more flexibility to incorporate insights of store-managers and historically well-performing assortments. We design fast and flexible algorithms based on variations of binary search that find the revenue of the (approximately) optimal assortment. In particular, we revisit the problem of large-scale assortment optimization under the multinomial logit choice model without any assumptions on the structure of the feasible assortments. We speed up the comparisons steps using novel vector space embeddings, based on advances in the fields of information retrieval and machine learning. For an arbitrary collection of assortments, our algorithms can find a solution in time that is sub-linear in the number of assortments and for the simpler case of cardinality constraints - linear in the number of items (existing methods are quadratic or worse). Empirical validations using the Billion Prices dataset and several retail transaction datasets show that our algorithms are competitive even when the number of items is $\sim 10^5$ ($100$x larger instances than previously studied).
Tasks Information Retrieval
Published 2020-03-06
URL https://arxiv.org/abs/2003.04736v1
PDF https://arxiv.org/pdf/2003.04736v1.pdf
PWC https://paperswithcode.com/paper/optimizing-revenue-while-showing-relevant

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

Title Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Authors Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu
Abstract In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model.
Published 2020-02-04
URL https://arxiv.org/abs/2002.01144v1
PDF https://arxiv.org/pdf/2002.01144v1.pdf
PWC https://paperswithcode.com/paper/classification-of-hyperspectral-and-lidar

Differential Dynamic Programming Neural Optimizer

Title Differential Dynamic Programming Neural Optimizer
Authors Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Abstract Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorithmic development remains relatively limited. In this work, we make an attempt along this line by reformulating the training procedure from the trajectory optimization perspective. We first show that most widely-used algorithms for training DNNs can be linked to the Differential Dynamic Programming (DDP), a celebrated second-order trajectory optimization algorithm rooted in the Approximate Dynamic Programming. In this vein, we propose a new variant of DDP that can accept batch optimization for training feedforward networks, while integrating naturally with the recent progress in curvature approximation. The resulting algorithm features layer-wise feedback policies which improve convergence rate and reduce sensitivity to hyper-parameter over existing methods. We show that the algorithm is competitive against state-ofthe-art first and second order methods. Our work opens up new avenues for principled algorithmic design built upon the optimal control theory.
Published 2020-02-20
URL https://arxiv.org/abs/2002.08809v1
PDF https://arxiv.org/pdf/2002.08809v1.pdf
PWC https://paperswithcode.com/paper/differential-dynamic-programming-neural

Illumination adaptive person reid based on teacher-student model and adversarial training

Title Illumination adaptive person reid based on teacher-student model and adversarial training
Authors Ziyue Zhang, Richard YD Xu, Shuai Jiang, Yang Li, Congzhentao Huang, Chen Deng
Abstract Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation. However, the changes in the illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Its innovations are threefold: (1) A discriminative entropy loss to ensure the ReID features contain no lighting information. (2) A ReID Teacher model trained by images under “neutral” lighting conditions to guide ReID classification. (3) An illumination Teacher model trained by the differences between the illumination-adjusted and original images to guide illumination classification. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.
Tasks Person Re-Identification
Published 2020-02-05
URL https://arxiv.org/abs/2002.01625v2
PDF https://arxiv.org/pdf/2002.01625v2.pdf
PWC https://paperswithcode.com/paper/illumination-adaptive-person-reid-based-on

Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings

Title Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
Authors C. Shi, S. Zhang, W. Lu, R. Song
Abstract Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper is to construct confidence intervals (CIs) for a policy’s value in infinite horizon settings where the number of decision points diverges to infinity. We propose to model the action-value state function (Q-function) associated with a policy based on series/sieve method to derive its confidence interval. When the target policy depends on the observed data as well, we propose a SequentiAl Value Evaluation (SAVE) method to recursively update the estimated policy and its value estimator. As long as either the number of trajectories or the number of decision points diverges to infinity, we show that the proposed CI achieves nominal coverage even in cases where the optimal policy is not unique. Simulation studies are conducted to back up our theoretical findings. We apply the proposed method to a dataset from mobile health studies and find that reinforcement learning algorithms could help improve patient’s health status.
Tasks Decision Making
Published 2020-01-13
URL https://arxiv.org/abs/2001.04515v1
PDF https://arxiv.org/pdf/2001.04515v1.pdf
PWC https://paperswithcode.com/paper/statistical-inference-of-the-value-function

Quantifying daseinisation using Shannon entropy

Title Quantifying daseinisation using Shannon entropy
Authors Roman Zapatrin
Abstract Topos formalism for quantum mechanics is interpreted in a broader, information retrieval perspective. Contexts, its basic components, are treated as sources of information. Their interplay, called daseinisation, defined in purely logical terms, is reformulated in terms of two relations: exclusion and preclusion of queries. Then, broadening these options, daseinisation becomes a characteristic of proximity of contexts; to quantify it numerically, Shannon entropy is used.
Tasks Information Retrieval
Published 2020-02-26
URL https://arxiv.org/abs/2002.12456v1
PDF https://arxiv.org/pdf/2002.12456v1.pdf
PWC https://paperswithcode.com/paper/quantifying-daseinisation-using-shannon

Regret Minimization in Stochastic Contextual Dueling Bandits

Title Regret Minimization in Stochastic Contextual Dueling Bandits
Authors Aadirupa Saha, Aditya Gopalan
Abstract We consider the problem of stochastic $K$-armed dueling bandit in the contextual setting, where at each round the learner is presented with a context set of $K$ items, each represented by a $d$-dimensional feature vector, and the goal of the learner is to identify the best arm of each context sets. However, unlike the classical contextual bandit setup, our framework only allows the learner to receive item feedback in terms of their (noisy) pariwise preferences–famously studied as dueling bandits which is practical interests in various online decision making scenarios, e.g. recommender systems, information retrieval, tournament ranking, where it is easier to elicit the relative strength of the items instead of their absolute scores. However, to the best of our knowledge this work is the first to consider the problem of regret minimization of contextual dueling bandits for potentially infinite decision spaces and gives provably optimal algorithms along with a matching lower bound analysis. We present two algorithms for the setup with respective regret guarantees $\tilde O(d\sqrt{T})$ and $\tilde O(\sqrt{dT \log K})$. Subsequently we also show that $\Omega(\sqrt {dT})$ is actually the fundamental performance limit for this problem, implying the optimality of our second algorithm. However the analysis of our first algorithm is comparatively simpler, and it is often shown to outperform the former empirically. Finally, we corroborate all the theoretical results with suitable experiments.
Tasks Decision Making, Information Retrieval, Recommendation Systems
Published 2020-02-20
URL https://arxiv.org/abs/2002.08583v1
PDF https://arxiv.org/pdf/2002.08583v1.pdf
PWC https://paperswithcode.com/paper/regret-minimization-in-stochastic-contextual

Regularizing Reasons for Outfit Evaluation with Gradient Penalty

Title Regularizing Reasons for Outfit Evaluation with Gradient Penalty
Authors Xingxing Zou, Zhizhong Li, Ke Bai, Dahua Lin, Waikeung Wong
Abstract In this paper, we build an outfit evaluation system which provides feedbacks consisting of a judgment with a convincing explanation. The system is trained in a supervised manner which faithfully follows the domain knowledge in fashion. We create the EVALUATION3 dataset which is annotated with judgment, the decisive reason for the judgment, and all corresponding attributes (e.g. print, silhouette, and material \etc.). In the training process, features of all attributes in an outfit are first extracted and then concatenated as the input for the intra-factor compatibility net. Then, the inter-factor compatibility net is used to compute the loss for judgment. We penalize the gradient of judgment loss of so that our Grad-CAM-like reason is regularized to be consistent with the labeled reason. In inference, according to the obtained information of judgment, reason, and attributes, a user-friendly explanation sentence is generated by the pre-defined templates. The experimental results show that the obtained network combines the advantages of high precision and good interpretation.
Published 2020-02-02
URL https://arxiv.org/abs/2002.00460v1
PDF https://arxiv.org/pdf/2002.00460v1.pdf
PWC https://paperswithcode.com/paper/regularizing-reasons-for-outfit-evaluation

Partially Observed Dynamic Tensor Response Regression

Title Partially Observed Dynamic Tensor Response Regression
Authors Jie Zhou, Will Wei Sun, Jingfei Zhang, Lexin Li
Abstract In modern data science, dynamic tensor data is prevailing in numerous applications. An important task is to characterize the relationship between such dynamic tensor and external covariates. However, the tensor data is often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rank, sparsity and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient non-convex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations, and two real applications, a neuroimaging dementia study and a digital advertising study.
Published 2020-02-22
URL https://arxiv.org/abs/2002.09735v2
PDF https://arxiv.org/pdf/2002.09735v2.pdf
PWC https://paperswithcode.com/paper/partially-observed-dynamic-tensor-response

NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent

Title NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent
Authors Ali Anaissi, Basem Suleiman, Seid Miad Zandavi
Abstract Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $. $CANDECOMP/PARAFAC$ (CP) decomposition has been extensively studied and applied to approximate $\mathcal{X}$ by $N$ loading matrices $A^{(1)}, \dots, A^{(N)}$ where $N$ represents the order of the tensor. We propose a new efficient CP decomposition solver named NeCPD for non-convex problem in multi-way online data based on stochastic gradient descent (SGD) algorithm. SGD is very useful in online setting since it allows us to update $\mathcal{X}^{(t+1)}$ in one single step. In terms of global convergence, it is well known that SGD stuck in many saddle points when it deals with non-convex problems. We study the Hessian matrix to identify theses saddle points, and then try to escape them using the perturbation approach which adds little noise to the gradient update step. We further apply Nesterov’s Accelerated Gradient (NAG) method in SGD algorithm to optimally accelerate the convergence rate and compensate Hessian computational delay time per epoch. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets show that our method provides more accurate results compared with existing online tensor analysis methods.
Published 2020-03-18
URL https://arxiv.org/abs/2003.08844v1
PDF https://arxiv.org/pdf/2003.08844v1.pdf
PWC https://paperswithcode.com/paper/necpd-an-online-tensor-decomposition-with

The Differentially Private Lottery Ticket Mechanism

Title The Differentially Private Lottery Ticket Mechanism
Authors Lovedeep Gondara, Ke Wang, Ricardo Silva Carvalho
Abstract We propose the differentially private lottery ticket mechanism (DPLTM). An end-to-end differentially private training paradigm based on the lottery ticket hypothesis. Using “high-quality winners”, selected via our custom score function, DPLTM significantly improves the privacy-utility trade-off over the state-of-the-art. We show that DPLTM converges faster, allowing for early stopping with reduced privacy budget consumption. We further show that the tickets from DPLTM are transferable across datasets, domains, and architectures. Our extensive evaluation on several public datasets provides evidence to our claims.
Published 2020-02-16
URL https://arxiv.org/abs/2002.11613v1
PDF https://arxiv.org/pdf/2002.11613v1.pdf
PWC https://paperswithcode.com/paper/the-differentially-private-lottery-ticket

On-Device Information Extraction from SMS using Hybrid Hierarchical Classification

Title On-Device Information Extraction from SMS using Hybrid Hierarchical Classification
Authors Shubham Vatsal, Naresh Purre, Sukumar Moharana, Gopi Ramena, Debi Prasanna Mohanty
Abstract Cluttering of SMS inbox is one of the serious problems that users today face in the digital world where every online login, transaction, along with promotions generate multiple SMS. This problem not only prevents users from searching and navigating messages efficiently but often results in users missing out the relevant information associated with the corresponding SMS like offer codes, payment reminders etc. In this paper, we propose a unique architecture to organize and extract the appropriate information from SMS and further display it in an intuitive template. In the proposed architecture, we use a Hybrid Hierarchical Long Short Term Memory (LSTM)-Convolutional Neural Network (CNN) to categorize SMS into multiple classes followed by a set of entity parsers used to extract the relevant information from the classified message. The architecture using its preprocessing techniques not only takes into account the enormous variations observed in SMS data but also makes it efficient for its on-device (mobile phone) functionalities in terms of inference timing and size.
Published 2020-02-03
URL https://arxiv.org/abs/2002.02755v1
PDF https://arxiv.org/pdf/2002.02755v1.pdf
PWC https://paperswithcode.com/paper/on-device-information-extraction-from-sms

Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks

Title Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
Authors Joseph Suarez, Yilun Du, Igor Mordach, Phillip Isola
Abstract Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.
Tasks Policy Gradient Methods
Published 2020-01-31
URL https://arxiv.org/abs/2001.12004v1
PDF https://arxiv.org/pdf/2001.12004v1.pdf
PWC https://paperswithcode.com/paper/neural-mmo-v13-a-massively-multiagent-game
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