Paper Group ANR 1739
Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction. GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus. Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation …
Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction
| Title | Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction |
| Authors | Hope Jasperson, Chas Bolton, Paul Johnson, Chris Marone, Maarten V. de Hoop |
| Abstract | When a rock is subjected to stress it deforms by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves called acoustic emissions (AE). Early research into AEs envisioned that these signals could be used in the future to predict rock falls, mine collapse, or even earthquakes. Today, nondestructive testing, a field of engineering, involves monitoring the spatio-temporal evolution of AEs with the goal of predicting time-to-failure for manufacturing tools and infrastructure. The monitoring process involves clustering AEs by damage mechanism (e.g. matrix cracking, delamination) to track changes within the material. In this study, we aim to adapt aspects of this process to the task of generalized earthquake prediction. Our data are generated in a laboratory setting using a biaxial shearing device and a granular fault gouge that mimics the conditions around tectonic faults. In particular, we analyze the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We use a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map is used to interactively cluster AEs according to damage mechanism. Finally, we use an event-based LSTM network to test the predictive power of each cluster. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure of the fault. |
| Tasks | Quantization |
| Published | 2019-12-12 |
| URL | https://arxiv.org/abs/1912.06087v1 |
| https://arxiv.org/pdf/1912.06087v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-classification-of-acoustic |
| Repo | |
| Framework | |
GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus
| Title | GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus |
| Authors | Shabnam Tafreshi, Mona Diab |
| Abstract | In this paper we present an emotion classifier model submitted to the SemEval-2019 Task 3: EmoContext. The task objective is to classify emotion (i.e. happy, sad, angry) in a 3-turn conversational data set. We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus. We utilize different word embeddings to empirically select the most suited one to represent our features. We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results. We achieved overall %56.05 f1-score and placed 144. |
| Tasks | Word Embeddings |
| Published | 2019-05-23 |
| URL | https://arxiv.org/abs/1905.09439v1 |
| https://arxiv.org/pdf/1905.09439v1.pdf | |
| PWC | https://paperswithcode.com/paper/gwu-nlp-lab-at-semeval-2019-task-3-emocontext |
| Repo | |
| Framework | |
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
| Title | Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation |
| Authors | Zhengxin Yang, Jinchao Zhang, Fandong Meng, Shuhao Gu, Yang Feng, Jie Zhou |
| Abstract | Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation usually compresses the context information into a representation via hierarchical attention networks. However, this method neither considers the relationship between context words nor distinguishes the roles of context words. To address this problem, we propose a query-guided capsule networks to cluster context information into different perspectives from which the target translation may concern. Experiment results show that our method can significantly outperform strong baselines on multiple data sets of different domains. |
| Tasks | |
| Published | 2019-09-02 |
| URL | https://arxiv.org/abs/1909.00564v2 |
| https://arxiv.org/pdf/1909.00564v2.pdf | |
| PWC | https://paperswithcode.com/paper/enhancing-context-modeling-with-a-query |
| Repo | |
| Framework | |
On Policy Gradients
| Title | On Policy Gradients |
| Authors | Mattis Manfred Kämmerer |
| Abstract | The goal of policy gradient approaches is to find a policy in a given class of policies which maximizes the expected return. Given a differentiable model of the policy, we want to apply a gradient-ascent technique to reach a local optimum. We mainly use gradient ascent, because it is theoretically well researched. The main issue is that the policy gradient with respect to the expected return is not available, thus we need to estimate it. As policy gradient algorithms also tend to require on-policy data for the gradient estimate, their biggest weakness is sample efficiency. For this reason, most research is focused on finding algorithms with improved sample efficiency. This paper provides a formal introduction to policy gradient that shows the development of policy gradient approaches, and should enable the reader to follow current research on the topic. |
| Tasks | |
| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.04817v1 |
| https://arxiv.org/pdf/1911.04817v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-policy-gradients |
| Repo | |
| Framework | |
Causal Inference via Conditional Kolmogorov Complexity using MDL Binning
| Title | Causal Inference via Conditional Kolmogorov Complexity using MDL Binning |
| Authors | Daniel Goldfarb, Scott Evans |
| Abstract | Recent developments have linked causal inference with Algorithmic Information Theory, and methods have been developed that utilize Conditional Kolmogorov Complexity to determine causation between two random variables. We present a method for inferring causal direction between continuous variables by using an MDL Binning technique for data discretization and complexity calculation. Our method captures the shape of the data and uses it to determine which variable has more information about the other. Its high predictive performance and robustness is shown on several real world use cases. |
| Tasks | Causal Inference |
| Published | 2019-10-31 |
| URL | https://arxiv.org/abs/1911.00332v2 |
| https://arxiv.org/pdf/1911.00332v2.pdf | |
| PWC | https://paperswithcode.com/paper/causal-inference-via-conditional-kolmogorov |
| Repo | |
| Framework | |
Multi-Kernel Correntropy for Robust Learning
| Title | Multi-Kernel Correntropy for Robust Learning |
| Authors | Badong Chen, Xin Wang, Zejian yuan, Pengju Ren, Jing Qin |
| Abstract | As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC). |
| Tasks | |
| Published | 2019-05-24 |
| URL | https://arxiv.org/abs/1905.10115v1 |
| https://arxiv.org/pdf/1905.10115v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-kernel-correntropy-for-robust-learning |
| Repo | |
| Framework | |
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
| Title | CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks |
| Authors | Alberto Marchisio, Giorgio Nanfa, Faiq Khalid, Muhammad Abdullah Hanif, Maurizio Martina, Muhammad Shafique |
| Abstract | Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. A large body of work has explored adversarial examples for CNNs, but their effectiveness on Capsule Networks has not yet been well studied. In our work, we perform an analysis to study the vulnerabilities in Capsule Networks to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate targeted imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB), mislead Capsule Networks. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN and a 9-layer CNN, and analyze the outcome, compared to the Capsule Networks to study differences in their behavior. |
| Tasks | Image Classification, Traffic Sign Recognition |
| Published | 2019-01-28 |
| URL | https://arxiv.org/abs/1901.09878v2 |
| https://arxiv.org/pdf/1901.09878v2.pdf | |
| PWC | https://paperswithcode.com/paper/capsattacks-robust-and-imperceptible |
| Repo | |
| Framework | |
A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation
| Title | A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation |
| Authors | Mohammad Aliannejadi, Dimitrios Rafailidis, Fabio Crestani |
| Abstract | The popularity of location-based social networks (LBSNs) has led to a tremendous amount of user check-in data. Recommending points of interest (POIs) plays a key role in satisfying users’ needs in LBSNs. While recent work has explored the idea of adopting collaborative ranking (CR) for recommendation, there have been few attempts to incorporate temporal information for POI recommendation using CR. In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users’ activities over time. The time-sensitive regularizer penalizes user and POIs that have been more time-sensitive in the past, helping the model to account for their long-term behavioral patterns while learning from user-POI interactions. Moreover, in the first phase, it attempts to rank visited POIs higher than the unvisited ones, and at the same time, apply the geographical influence. In the second phase, our algorithm tries to rank users’ favorite POIs higher on the recommendation list. Both phases employ a collaborative learning strategy that enables the model to capture complex latent associations from two different perspectives. Experiments on real-world datasets show that our proposed time-sensitive collaborative ranking model beats state-of-the-art POI recommendation methods. |
| Tasks | Collaborative Ranking |
| Published | 2019-09-16 |
| URL | https://arxiv.org/abs/1909.07131v1 |
| https://arxiv.org/pdf/1909.07131v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-joint-two-phase-time-sensitive-regularized |
| Repo | |
| Framework | |
Context agnostic trajectory prediction based on $λ$-architecture
| Title | Context agnostic trajectory prediction based on $λ$-architecture |
| Authors | Evangelos Psomakelis, Konstantinos Tserpes, Dimitris Zissisc, Dimosthenis Anagnostopoulos, Theodora Varvarigou |
| Abstract | Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as trajectory prediction. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a {\lambda}-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geolocation stream is provided. The experimental results presented prove that each part of the {\lambda}-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform. |
| Tasks | Trajectory Prediction |
| Published | 2019-09-29 |
| URL | https://arxiv.org/abs/1909.13241v1 |
| https://arxiv.org/pdf/1909.13241v1.pdf | |
| PWC | https://paperswithcode.com/paper/context-agnostic-trajectory-prediction-based |
| Repo | |
| Framework | |
Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications
| Title | Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications |
| Authors | Mario Manzo |
| Abstract | Graphs are widely adopted tools for encoding information. Generally, they are applied to disparate research fields where data needs to be represented in terms of local and spatial connections. In this context, a structure for ditigal image representation, called Attributed Relational SIFT-based Regions Graph (ARSRG), previously introduced, is presented. ARSRG has not been explored in detail in previous works and for this reason the goal is to investigate unknown aspects. The study is divided into two parts. A first, theoretical, introducing formal definitions, not yet specified previously, with purpose to clarify its structural configuration. A second, experimental, which provides fundamental elements about its adaptability and flexibility regarding different applications. The theoretical vision combined with the experimental one shows how the structure is adaptable to image representation including contents of different nature. |
| Tasks | |
| Published | 2019-12-20 |
| URL | https://arxiv.org/abs/1912.09972v1 |
| https://arxiv.org/pdf/1912.09972v1.pdf | |
| PWC | https://paperswithcode.com/paper/attributed-relational-sift-based-regions |
| Repo | |
| Framework | |
Deep Generative Design: Integration of Topology Optimization and Generative Models
| Title | Deep Generative Design: Integration of Topology Optimization and Generative Models |
| Authors | Sangeun Oh, Yongsu Jung, Seongsin Kim, Ikjin Lee, Namwoo Kang |
| Abstract | Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods. |
| Tasks | Anomaly Detection |
| Published | 2019-03-01 |
| URL | https://arxiv.org/abs/1903.01548v2 |
| https://arxiv.org/pdf/1903.01548v2.pdf | |
| PWC | https://paperswithcode.com/paper/generative-design-exploration-by-integrating |
| Repo | |
| Framework | |
DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds
| Title | DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds |
| Authors | Siddharth Srivastava, Brejesh Lall |
| Abstract | Learning local descriptors is an important problem in computer vision. While there are many techniques for learning local patch descriptors for 2D images, recently efforts have been made for learning local descriptors for 3D points. The recent progress towards solving this problem in 3D leverages the strong feature representation capability of image based convolutional neural networks by utilizing RGB-D or multi-view representations. However, in this paper, we propose to learn 3D local descriptors by directly processing unstructured 3D point clouds without needing any intermediate representation. The method constitutes a deep network for learning permutation invariant representation of 3D points. To learn the local descriptors, we use a multi-margin contrastive loss which discriminates between similar and dissimilar points on a surface while also leveraging the extent of dissimilarity among the negative samples at the time of training. With comprehensive evaluation against strong baselines, we show that the proposed method outperforms state-of-the-art methods for matching points in 3D point clouds. Further, we demonstrate the effectiveness of the proposed method on various applications achieving state-of-the-art results. |
| Tasks | Metric Learning |
| Published | 2019-03-27 |
| URL | http://arxiv.org/abs/1904.00817v1 |
| http://arxiv.org/pdf/1904.00817v1.pdf | |
| PWC | https://paperswithcode.com/paper/deeppoint3d-learning-discriminative-local |
| Repo | |
| Framework | |
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
| Title | Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis |
| Authors | Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde |
| Abstract | A remarkable recent discovery in machine learning has been that deep neural networks can achieve impressive performance (in terms of both lower training error and higher generalization capacity) in the regime where they are massively over-parameterized. Consequently, over the past year, the community has devoted growing interest in analyzing optimization and generalization properties of over-parameterized networks, and several breakthrough works have led to important theoretical progress. However, the majority of existing work only applies to supervised learning scenarios and hence are limited to settings such as classification and regression. In contrast, the role of over-parameterization in the unsupervised setting has gained far less attention. In this paper, we study the gradient dynamics of two-layer over-parameterized autoencoders with ReLU activation. We make very few assumptions about the given training dataset (other than mild non-degeneracy conditions). Starting from a randomly initialized autoencoder network, we rigorously prove the linear convergence of gradient descent in two learning regimes, namely: (i) the weakly-trained regime where only the encoder is trained, and (ii) the jointly-trained regime where both the encoder and the decoder are trained. Our results indicate the considerable benefits of joint training over weak training for finding global optima, achieving a dramatic decrease in the required level of over-parameterization. We also analyze the case of weight-tied autoencoders (which is a commonly used architectural choice in practical settings) and prove that in the over-parameterized setting, training such networks from randomly initialized points leads to certain unexpected degeneracies. |
| Tasks | |
| Published | 2019-11-27 |
| URL | https://arxiv.org/abs/1911.11983v2 |
| https://arxiv.org/pdf/1911.11983v2.pdf | |
| PWC | https://paperswithcode.com/paper/benefits-of-jointly-training-autoencoders-an |
| Repo | |
| Framework | |
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
| Title | Learning Word Ratings for Empathy and Distress from Document-Level User Responses |
| Authors | João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, Lyle Ungar |
| Abstract | Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and is missing deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting words. |
| Tasks | Emotion Recognition |
| Published | 2019-12-02 |
| URL | https://arxiv.org/abs/1912.01079v1 |
| https://arxiv.org/pdf/1912.01079v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-word-ratings-for-empathy-and |
| Repo | |
| Framework | |
Meta Answering for Machine Reading
| Title | Meta Answering for Machine Reading |
| Authors | Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, Lierni Sestorain Saralegu |
| Abstract | We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BERT, providing candidate answers to questions, and possibly some context. To validate the realism of our formulation, we ask humans to play the role of a meta-answerer. With just a small snippet of text around an answer, humans can outperform the machine reader, improving recall. Similarly, a simple machine meta-answerer outperforms the environment, improving both precision and recall on the Natural Questions dataset. The system relies on joint training of answer scoring and the selection of conditioning information. |
| Tasks | Question Answering, Reading Comprehension |
| Published | 2019-11-11 |
| URL | https://arxiv.org/abs/1911.04156v1 |
| https://arxiv.org/pdf/1911.04156v1.pdf | |
| PWC | https://paperswithcode.com/paper/meta-answering-for-machine-reading |
| Repo | |
| Framework | |
