Paper Group ANR 936
Neutron drip line in the Ca region from Bayesian model averaging. Algorithmically generating new algebraic features of polynomial systems for machine learning. Overview for the Second Shared Task on Language Identification in Code-Switched Data. RAVEN: A Dataset for Relational and Analogical Visual rEasoNing. The Dynamics of Handwriting Improves th …
Neutron drip line in the Ca region from Bayesian model averaging
| Title | Neutron drip line in the Ca region from Bayesian model averaging |
| Authors | Léo Neufcourt, Yuchen Cao, Witold Nazarewicz, Erik Olsen, Frederi Viens |
| Abstract | The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around $^{60}$Ca [Tarasov, 2018] and the experimental determination of masses for $^{55-57}$Ca (Michimasa, 2018] provide unique information about the binding energy surface in this region. To assess the impact of these experimental discoveries on the nuclear landscape’s extent, we use global mass models and statistical machine learning to make predictions, with quantified levels of certainty, for bound nuclides between Si and Ti. Using a Bayesian model averaging analysis based on Gaussian-process-based extrapolations we introduce the posterior probability $p_{ex}$ for each nucleus to be bound to neutron emission. We find that extrapolations for drip-line locations, at which the nuclear binding ends, are consistent across the global mass models used, in spite of significant variations between their raw predictions. In particular, considering the current experimental information and current global mass models, we predict that $^{68}$Ca has an average posterior probability ${p_{ex}\approx76}$% to be bound to two-neutron emission while the nucleus $^{61}$Ca is likely to decay by emitting a neutron (${p_{ex}\approx 46}$ %). |
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| Published | 2019-01-22 |
| URL | https://arxiv.org/abs/1901.07632v2 |
| https://arxiv.org/pdf/1901.07632v2.pdf | |
| PWC | https://paperswithcode.com/paper/neutron-drip-line-in-the-ca-region-from |
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Algorithmically generating new algebraic features of polynomial systems for machine learning
| Title | Algorithmically generating new algebraic features of polynomial systems for machine learning |
| Authors | Dorian Florescu, Matthew England |
| Abstract | There are a variety of choices to be made in both computer algebra systems (CASs) and satisfiability modulo theory (SMT) solvers which can impact performance without affecting mathematical correctness. Such choices are candidates for machine learning (ML) approaches, however, there are difficulties in applying standard ML techniques, such as the efficient identification of ML features from input data which is typically a polynomial system. Our focus is selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm implemented in several CASs, and now also SMT-solvers. We created a framework to describe all the previously identified ML features for the problem and then enumerated all options in this framework to automatically generation many more features. We validate the usefulness of these with an experiment which shows that an ML choice for CAD variable ordering is superior to those made by human created heuristics, and further improved with these additional features. We expect that this technique of feature generation could be useful for other choices related to CAD, or even choices for other algorithms with polynomial systems for input. |
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| Published | 2019-06-03 |
| URL | https://arxiv.org/abs/1906.01455v1 |
| https://arxiv.org/pdf/1906.01455v1.pdf | |
| PWC | https://paperswithcode.com/paper/algorithmically-generating-new-algebraic |
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Overview for the Second Shared Task on Language Identification in Code-Switched Data
| Title | Overview for the Second Shared Task on Language Identification in Code-Switched Data |
| Authors | Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim, Abdelati Hawwari, Nicolas Rey-Villamizar, Mona Diab, Thamar Solorio |
| Abstract | We present an overview of the second shared task on language identification in code-switched data. For the shared task, we had code-switched data from two different language pairs: Modern Standard Arabic-Dialectal Arabic (MSA-DA) and Spanish-English (SPA-ENG). We had a total of nine participating teams, with all teams submitting a system for SPA-ENG and four submitting for MSA-DA. Through evaluation, we found that once again language identification is more difficult for the language pair that is more closely related. We also found that this year’s systems performed better overall than the systems from the previous shared task indicating overall progress in the state of the art for this task. |
| Tasks | Language Identification |
| Published | 2019-09-28 |
| URL | https://arxiv.org/abs/1909.13016v1 |
| https://arxiv.org/pdf/1909.13016v1.pdf | |
| PWC | https://paperswithcode.com/paper/overview-for-the-second-shared-task-on-1 |
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RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
| Title | RAVEN: A Dataset for Relational and Analogical Visual rEasoNing |
| Authors | Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, Song-Chun Zhu |
| Abstract | Dramatic progress has been witnessed in basic vision tasks involving low-level perception, such as object recognition, detection, and tracking. Unfortunately, there is still an enormous performance gap between artificial vision systems and human intelligence in terms of higher-level vision problems, especially ones involving reasoning. Earlier attempts in equipping machines with high-level reasoning have hovered around Visual Question Answering (VQA), one typical task associating vision and language understanding. In this work, we propose a new dataset, built in the context of Raven’s Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation. Unlike previous works in measuring abstract reasoning using RPM, we establish a semantic link between vision and reasoning by providing structure representation. This addition enables a new type of abstract reasoning by jointly operating on the structure representation. Machine reasoning ability using modern computer vision is evaluated in this newly proposed dataset. Additionally, we also provide human performance as a reference. Finally, we show consistent improvement across all models by incorporating a simple neural module that combines visual understanding and structure reasoning. |
| Tasks | Object Recognition, Question Answering, Visual Question Answering, Visual Reasoning |
| Published | 2019-03-07 |
| URL | http://arxiv.org/abs/1903.02741v1 |
| http://arxiv.org/pdf/1903.02741v1.pdf | |
| PWC | https://paperswithcode.com/paper/raven-a-dataset-for-relational-and-analogical |
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The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia
| Title | The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia |
| Authors | Konrad Zolna, Thibault Asselborn, Caroline Jolly, Laurence Casteran, Marie-Ange~Nguyen-Morel, Wafa Johal, Pierre Dillenbourg |
| Abstract | Handwriting disorder (termed dysgraphia) is a far from a singular problem as nearly 8.6% of the population in France is considered dysgraphic. Moreover, research highlights the fundamental importance to detect and remediate these handwriting difficulties as soon as possible as they may affect a child’s entire life, undermining performance and self-confidence in a wide variety of school activities. At the moment, the detection of handwriting difficulties is performed through a standard test called BHK. This detection, performed by therapists, is laborious because of its high cost and subjectivity. We present a digital approach to identify and characterize handwriting difficulties via a Recurrent Neural Network model (RNN). The child under investigation is asked to write on a graphics tablet all the letters of the alphabet as well as the ten digits. Once complete, the RNN delivers a diagnosis in a few milliseconds and demonstrates remarkable efficiency as it correctly identifies more than 90% of children diagnosed as dysgraphic using the BHK test. The main advantage of our tablet-based system is that it captures the dynamic features of writing – something a human expert, such as a teacher, is unable to do. We show that incorporating the dynamic information available by the use of tablet is highly beneficial to our digital test to discriminate between typically-developing and dysgraphic children. |
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| Published | 2019-06-12 |
| URL | https://arxiv.org/abs/1906.07576v1 |
| https://arxiv.org/pdf/1906.07576v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-dynamics-of-handwriting-improves-the |
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Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
| Title | Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation |
| Authors | Junjie Liu, Dongchao Wen, Hongxing Gao, Wei Tao, Tse-Wei Chen, Kinya Osa, Masami Kato |
| Abstract | Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability). In this paper, we propose a knowledge representing (KR) framework mainly focusing on modeling the parameters distribution as prior knowledge. Firstly, we suggest a knowledge aggregation scheme in order to answer how to represent the prior knowledge from teacher network. Through aggregating the parameters distribution from teacher network into more abstract level, the scheme is able to alleviate the phenomenon of residual accumulation in the deeper layers. Secondly, as the critical issue of what the most important prior knowledge is for better distilling, we design a sparse recoding penalty for constraining the student network to learn with the penalized gradients. With the proposed penalty, the student network can effectively avoid the over-regularization during knowledge distilling and converge faster. The quantitative experiments exhibit that the proposed framework achieves the state-ofthe-arts performance, even though the target network does not have the expected capacity. Moreover, the framework is flexible enough for combining with other KD methods based on the posterior knowledge. |
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| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05329v1 |
| https://arxiv.org/pdf/1911.05329v1.pdf | |
| PWC | https://paperswithcode.com/paper/knowledge-representing-efficient-sparse |
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Simulating Content Consistent Vehicle Datasets with Attribute Descent
| Title | Simulating Content Consistent Vehicle Datasets with Attribute Descent |
| Authors | Yue Yao, Liang Zheng, Xiaodong Yang, Milind Naphade, Tom Gedeon |
| Abstract | We simulate data using a graphic engine to augment real-world datasets, with application to vehicle re-identification (re-ID). In order for data augmentation to be effective, the simulated data should be similar to the real data in key attributes like illumination and viewpoint. We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it contains 1,209 vehicles of various models in 3D with fully editable attributes. We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets. Specifically, we manipulate each attribute in VehicleX, aiming to minimize the discrepancy between VehicleX and real data in terms of the Fr’echet Inception Distance (FID). This attribute descent algorithm allows content-level domain adaptation (DA), which has advantages over existing DA methods working on the pixel level or feature level. We mix adapted VehicleX data with three vehicle re-ID datasets individually, and observe consistent improvement when the proposed attribute descent is applied. With the augmented datasets, we report competitive accuracy compared with state-of-the-art results. The VehicleX engine and code of this paper will be released. |
| Tasks | Data Augmentation, Domain Adaptation, Vehicle Re-Identification |
| Published | 2019-12-18 |
| URL | https://arxiv.org/abs/1912.08855v1 |
| https://arxiv.org/pdf/1912.08855v1.pdf | |
| PWC | https://paperswithcode.com/paper/simulating-content-consistent-vehicle |
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Recurrence is required to capture the representational dynamics of the human visual system
| Title | Recurrence is required to capture the representational dynamics of the human visual system |
| Authors | Tim C Kietzmann, Courtney J Spoerer, Lynn Sörensen, Radoslaw M Cichy, Olaf Hauk, Nikolaus Kriegeskorte |
| Abstract | The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multi-region cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream. |
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| Published | 2019-03-14 |
| URL | https://arxiv.org/abs/1903.05946v2 |
| https://arxiv.org/pdf/1903.05946v2.pdf | |
| PWC | https://paperswithcode.com/paper/recurrence-required-to-capture-the-dynamic |
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Background Segmentation for Vehicle Re-Identification
| Title | Background Segmentation for Vehicle Re-Identification |
| Authors | Mingjie Wu, Yongfei Zhang, Tianyu Zhang, Wenqi Zhang |
| Abstract | Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance.Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information.However, background interference in vehicle re-identification have not been explored.In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance. To the best of our knowledge, this paper is the first to consider the background interference problem in vehicle re-identification. We construct a vehicle segmentation dataset and develop a vehicle Re-ID framework with a background interference removal (BIR) mechanism to improve the vehicle Re-ID performance as well as robustness against complex background in large-scale spatio-temporal scenes. Extensive experiments demonstrate the effectiveness of our proposed framework, with an average 9% gain on mAP over state-of-the-art vehicle Re-ID algorithms. |
| Tasks | Vehicle Re-Identification |
| Published | 2019-10-15 |
| URL | https://arxiv.org/abs/1910.06613v1 |
| https://arxiv.org/pdf/1910.06613v1.pdf | |
| PWC | https://paperswithcode.com/paper/background-segmentation-for-vehicle-re |
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Voice Conversion for Whispered Speech Synthesis
| Title | Voice Conversion for Whispered Speech Synthesis |
| Authors | Marius Cotescu, Thomas Drugman, Goeric Huybrechts, Jaime Lorenzo-Trueba, Alexis Moinet |
| Abstract | We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispered speech. We investigate using Gaussian Mixture Models (GMM) and Deep Neural Networks (DNN) to model the mapping between acoustic features of normal speech and those of whispered speech. We evaluate naturalness and speaker similarity of the converted whisper on an internal corpus and on the publicly available wTIMIT corpus. We show that applying VC techniques is significantly better than using rule-based signal processing methods and it achieves results that are indistinguishable from copy-synthesis of natural whisper recordings. We investigate the ability of the DNN model to generalize on unseen speakers, when trained with data from multiple speakers. We show that excluding the target speaker from the training set has little or no impact on the perceived naturalness and speaker similarity of the converted whisper. The proposed DNN method is used in the newly released Whisper Mode of Amazon Alexa. |
| Tasks | Speech Synthesis, Voice Conversion |
| Published | 2019-12-11 |
| URL | https://arxiv.org/abs/1912.05289v2 |
| https://arxiv.org/pdf/1912.05289v2.pdf | |
| PWC | https://paperswithcode.com/paper/voice-conversion-for-whispered-speech |
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Learning to Stop in Structured Prediction for Neural Machine Translation
| Title | Learning to Stop in Structured Prediction for Neural Machine Translation |
| Authors | Mingbo Ma, Renjie Zheng, Liang Huang |
| Abstract | Beam search optimization resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training, and the model naturally prefers the longer hypotheses during the testing time in practice since they use the raw score instead of the probability-based score. We propose a novel ranking method which enables an optimal beam search stopping criteria. We further introduce a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. Experiments of neural machine translation on both synthetic data and real languages (German-to-English and Chinese-to-English) demonstrate our proposed methods lead to better length and BLEU score. |
| Tasks | Machine Translation, Structured Prediction |
| Published | 2019-04-01 |
| URL | https://arxiv.org/abs/1904.01032v3 |
| https://arxiv.org/pdf/1904.01032v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-stop-in-structured-prediction-for |
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Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates
| Title | Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates |
| Authors | Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson, Cameron Browne |
| Abstract | In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games. |
| Tasks | Board Games |
| Published | 2019-05-14 |
| URL | https://arxiv.org/abs/1905.05809v1 |
| https://arxiv.org/pdf/1905.05809v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-policies-from-self-play-with-policy |
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Offensive Language and Hate Speech Detection for Danish
| Title | Offensive Language and Hate Speech Detection for Danish |
| Authors | Gudbjartur Ingi Sigurbergsson, Leon Derczynski |
| Abstract | The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset containing user-generated comments from \textit{Reddit} and \textit{Facebook}. It contains user generated comments from various social media platforms, and to our knowledge, it is the first of its kind. Our dataset is annotated to capture various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of $0.74$, and the best performing system for Danish achieves a macro averaged F1-score of $0.70$. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of $0.62$, while the best performing system for Danish achieves a macro averaged F1-score of $0.73$. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of $0.56$, and the best performing system for Danish achieves a macro averaged F1-score of $0.63$. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying. |
| Tasks | Hate Speech Detection |
| Published | 2019-08-13 |
| URL | https://arxiv.org/abs/1908.04531v1 |
| https://arxiv.org/pdf/1908.04531v1.pdf | |
| PWC | https://paperswithcode.com/paper/offensive-language-and-hate-speech-detection |
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Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish
| Title | Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish |
| Authors | Renard Korzeniowski, Rafał Rolczyński, Przemysław Sadownik, Tomasz Korbak, Marcin Możejko |
| Abstract | This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT model to our classification task, and using the TPOT library to find the optimal pipeline. We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience. Our team placed second in subtask 2 with a shallow model found by TPOT: a~logistic regression classifier with non-trivial feature engineering. |
| Tasks | Automated Feature Engineering, Feature Engineering, Hate Speech Detection |
| Published | 2019-06-17 |
| URL | https://arxiv.org/abs/1906.09325v1 |
| https://arxiv.org/pdf/1906.09325v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-unsupervised-pre-training-and |
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On Investigation of Unsupervised Speech Factorization Based on Normalization Flow
| Title | On Investigation of Unsupervised Speech Factorization Based on Normalization Flow |
| Authors | Haoran Sun, Yunqi Cai, Lantian Li, Dong Wang |
| Abstract | Speech signals are complex composites of various information, including phonetic content, speaker traits, channel effect, etc. Decomposing this complicated mixture into independent factors, i.e., speech factorization, is fundamentally important and plays the central role in many important algorithms of modern speech processing tasks. In this paper, we present a preliminary investigation on unsupervised speech factorization based on the normalization flow model. This model constructs a complex invertible transform, by which we can project speech segments into a latent code space where the distribution is a simple diagonal Gaussian. Our preliminary investigation on the TIMIT database shows that this code space exhibits favorable properties such as denseness and pseudo linearity, and perceptually important factors such as phonetic content and speaker trait can be represented as particular directions within the code space. |
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| Published | 2019-10-29 |
| URL | https://arxiv.org/abs/1910.13288v1 |
| https://arxiv.org/pdf/1910.13288v1.pdf | |
| PWC | https://paperswithcode.com/paper/191013288 |
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