Paper Group ANR 1726
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks. Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. Argument Generation with Retrieval, Planning, and Realization. Inspect Transfer Learning Architecture with Dilated Convolution. Classifying single-qubit nois …
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
| Title | BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks |
| Authors | A. Sufian, Anirudha Ghosh, Avijit Naskar, Farhana Sultana, Jaya Sil, M M Hafizur Rahman |
| Abstract | Images of handwritten digits are different from natural images as the orientation of a digit, as well as similarity of features of different digits, makes confusion. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. BDNet is a densely connected deep convolutional neural network model used to classify (recognize) Bengali handwritten numeral digits. It is end-to-end trained using ISI Bengali handwritten numeral dataset. During training, untraditional data preprocessing and augmentation techniques are used so that the trained model works on a different dataset. The model has achieved the test accuracy of 99.775%(baseline was 99.40%) on the test dataset of ISI Bengali handwritten numerals. So, the BDNet model gives 62.5% error reduction compared to previous state-of-the-art models. Here we have also created a dataset of 1000 images of Bengali handwritten numerals to test the trained model, and it giving promising results. Codes, trained model and our own dataset are available at: {https://github.com/Sufianlab/BDNet}. |
| Tasks | Handwritten Digit Recognition, Image Classification |
| Published | 2019-06-10 |
| URL | https://arxiv.org/abs/1906.03786v5 |
| https://arxiv.org/pdf/1906.03786v5.pdf | |
| PWC | https://paperswithcode.com/paper/bdnet-bengali-handwritten-numeral-digit |
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Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
| Title | Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination |
| Authors | Shauharda Khadka, Somdeb Majumdar, Santiago Miret, Stephen McAleer, Kagan Tumer |
| Abstract | A key challenge for Multiagent RL (Reinforcement Learning) is the design of agent-specific, local rewards that are aligned with sparse global objectives. In this paper, we introduce MERL (Multiagent Evolutionary RL), a hybrid algorithm that does not require an explicit alignment between local and global objectives. MERL uses fast, policy-gradient based learning for each agent by utilizing their dense local rewards. Concurrently, an evolutionary algorithm is used to recruit agents into a team by directly optimizing the sparser global objective. We explore problems that require coupling (a minimum number of agents required to coordinate for success), where the degree of coupling is not known to the agents. We demonstrate that MERL’s integrated approach is more sample-efficient and retains performance better with increasing coupling orders compared to MADDPG, the state-of-the-art policy-gradient algorithm for multiagent coordination. |
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| Published | 2019-06-18 |
| URL | https://arxiv.org/abs/1906.07315v2 |
| https://arxiv.org/pdf/1906.07315v2.pdf | |
| PWC | https://paperswithcode.com/paper/evolutionary-reinforcement-learning-for |
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Argument Generation with Retrieval, Planning, and Realization
| Title | Argument Generation with Retrieval, Planning, and Realization |
| Authors | Xinyu Hua, Zhe Hu, Lu Wang |
| Abstract | Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content. |
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| Published | 2019-06-09 |
| URL | https://arxiv.org/abs/1906.03717v1 |
| https://arxiv.org/pdf/1906.03717v1.pdf | |
| PWC | https://paperswithcode.com/paper/argument-generation-with-retrieval-planning |
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Inspect Transfer Learning Architecture with Dilated Convolution
| Title | Inspect Transfer Learning Architecture with Dilated Convolution |
| Authors | Syeda Noor Jaha Azim, Md. Aminur Rab Ratul |
| Abstract | There are many award-winning pre-trained Convolutional Neural Network (CNN), which have a common phenomenon of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to ILSVRC-2014, to show that slight modification in the basic architecture can enhance the accuracy result of the image classification task. In this paper, We present two improve architectures of pre-trained VGG-16 and VGG-19 networks that apply transfer learning when trained on a different dataset. I report a series of experimental result on various modification of the primary VGG networks and achieved significant out-performance on image classification task by: (1) freezing the first two blocks of the convolutional layers to prevent over-fitting and (2) applying different combination of dilation rate in the last three blocks of convolutional layer to reduce image resolution for feature extraction. Both the proposed architecture achieves a competitive result on CIFAR-10 and CIFAR-100 dataset. |
| Tasks | Image Classification, Transfer Learning |
| Published | 2019-11-20 |
| URL | https://arxiv.org/abs/1911.08769v1 |
| https://arxiv.org/pdf/1911.08769v1.pdf | |
| PWC | https://paperswithcode.com/paper/inspect-transfer-learning-architecture-with |
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Classifying single-qubit noise using machine learning
| Title | Classifying single-qubit noise using machine learning |
| Authors | Travis L. Scholten, Yi-Kai Liu, Kevin Young, Robin Blume-Kohout |
| Abstract | Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP. Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data. We identify the critical components of “machine-learned” QCVV techniques, and present a rubric for developing them. To demonstrate this approach, we focus on the problem of determining whether noise affecting a single qubit is coherent or stochastic (incoherent) using the data sets originally proposed for gate set tomography. We leverage known ML algorithms to train a classifier distinguishing these two kinds of noise. The accuracy of the classifier depends on how well it can approximate the “natural” geometry of the training data. We find GST data sets generated by a noisy qubit can reliably be separated by linear surfaces, although feature engineering can be necessary. We also show the classifier learned by a support vector machine (SVM) is robust under finite-sample noise. |
| Tasks | Feature Engineering |
| Published | 2019-08-30 |
| URL | https://arxiv.org/abs/1908.11762v1 |
| https://arxiv.org/pdf/1908.11762v1.pdf | |
| PWC | https://paperswithcode.com/paper/classifying-single-qubit-noise-using-machine |
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MultiWiki: Interlingual Text Passage Alignment in Wikipedia
| Title | MultiWiki: Interlingual Text Passage Alignment in Wikipedia |
| Authors | Simon Gottschalk, Elena Demidova |
| Abstract | In this article we address the problem of text passage alignment across interlingual article pairs in Wikipedia. We develop methods that enable the identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences and build a basis for qualitative analysis of the articles. An important challenge in this context is the trade-off between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, we conduct a user study at the example of the German, Russian and the English Wikipedia and collect a user-annotated benchmark. Then we propose MultiWiki – a method that adopts an integrated approach to the text passage alignment using semantic similarity measures and greedy algorithms and achieves precise results with respect to the user-defined alignment. MultiWiki demonstration is publicly available and currently supports four language pairs. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08675v1 |
| https://arxiv.org/pdf/1905.08675v1.pdf | |
| PWC | https://paperswithcode.com/paper/multiwiki-interlingual-text-passage-alignment |
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Don’t Blame Distributional Semantics if it can’t do Entailment
| Title | Don’t Blame Distributional Semantics if it can’t do Entailment |
| Authors | Matthijs Westera, Gemma Boleda |
| Abstract | Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-05-17 |
| URL | https://arxiv.org/abs/1905.07356v1 |
| https://arxiv.org/pdf/1905.07356v1.pdf | |
| PWC | https://paperswithcode.com/paper/dont-blame-distributional-semantics-if-it |
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The Book of Why: Review
| Title | The Book of Why: Review |
| Authors | Joseph Y. Halpern |
| Abstract | This is a review of “The Book of Why”, by Judea Pearl. |
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| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1909.13485v1 |
| https://arxiv.org/pdf/1909.13485v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-book-of-why-review |
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DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
| Title | DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs |
| Authors | Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, Dacheng Tao |
| Abstract | Due to the high storage and search efficiency, hashing has become prevalent for large-scale similarity search. Particularly, deep hashing methods have greatly improved the search performance under supervised scenarios. In contrast, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable supervisory similarity signals. To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals. Specifically, we investigate the relationship between the initial noisy similarity signals learned from local structures and the semantic similarity labels assigned by a Bayes optimal classifier. We show that under a mild assumption, some data pairs, of which labels are consistent with those assigned by the Bayes optimal classifier, can be potentially distilled. Inspired by this fact, we design a simple yet effective strategy to distill data pairs automatically and further adopt a Bayesian learning framework to learn hash functions from the distilled data set. Extensive experimental results on three widely used benchmark datasets show that the proposed DistillHash consistently accomplishes the state-of-the-art search performance. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-05-09 |
| URL | https://arxiv.org/abs/1905.03465v1 |
| https://arxiv.org/pdf/1905.03465v1.pdf | |
| PWC | https://paperswithcode.com/paper/190503465 |
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Learning dynamic word embeddings with drift regularisation
| Title | Learning dynamic word embeddings with drift regularisation |
| Authors | Syrielle Montariol, Alexandre Allauzen |
| Abstract | Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the Dynamic Bernoulli Embeddings model to learn dynamic word embeddings, in order to identify notable properties of the model. The comparison is made on the New York Times Annotated Corpus in English and a set of articles from the French newspaper Le Monde covering the same period. This allows us to define a pipeline to analyse the evolution of words use across two languages. |
| Tasks | Word Embeddings |
| Published | 2019-07-22 |
| URL | https://arxiv.org/abs/1907.09169v1 |
| https://arxiv.org/pdf/1907.09169v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-dynamic-word-embeddings-with-drift |
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A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
| Title | A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks |
| Authors | Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal |
| Abstract | Evaluation of Bayesian deep learning (BDL) methods is challenging. We often seek to evaluate the methods’ robustness and scalability, assessing whether new tools give better' uncertainty estimates than old ones. These evaluations are paramount for practitioners when choosing BDL tools on-top of which they build their applications. Current popular evaluations of BDL methods, such as the UCI experiments, are lacking: Methods that excel with these experiments often fail when used in application such as medical or automotive, suggesting a pertinent need for new benchmarks in the field. We propose a new BDL benchmark with a diverse set of tasks, inspired by a real-world medical imaging application on \emph{diabetic retinopathy diagnosis}. Visual inputs (512x512 RGB images of retinas) are considered, where model uncertainty is used for medical pre-screening---i.e. to refer patients to an expert when model diagnosis is uncertain. Methods are then ranked according to metrics derived from expert-domain to reflect real-world use of model uncertainty in automated diagnosis. We develop multiple tasks that fall under this application, including out-of-distribution detection and robustness to distribution shift. We then perform a systematic comparison of well-tuned BDL techniques on the various tasks. From our comparison we conclude that some current techniques which solve benchmarks such as UCI overfit’ their uncertainty to the dataset—when evaluated on our benchmark these underperform in comparison to simpler baselines. The code for the benchmark, its baselines, and a simple API for evaluating new BDL tools are made available at https://github.com/oatml/bdl-benchmarks. |
| Tasks | Out-of-Distribution Detection |
| Published | 2019-12-22 |
| URL | https://arxiv.org/abs/1912.10481v1 |
| https://arxiv.org/pdf/1912.10481v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-systematic-comparison-of-bayesian-deep |
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Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records
| Title | Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records |
| Authors | Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen, Linyuan Jing, Aalpen A. Patel, Joshua V. Stough, Christopher M. Haggerty, Brandon K. Fornwalt |
| Abstract | We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data. Our approach builds on prior multi-modal machine learning models by now enabling visualization of how individual factors contribute to the overall outcome risk, assuming other factors remain constant, which was previously impossible. We demonstrate the value of this approach using a large multi-modal clinical dataset including both EHR data and 31,278 echocardiographic videos of the heart from 26,793 patients. We generated separate models for (i) clinical data only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric variables derived from the videos, which we call echocardiography-derived measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable multi-modal model maintained performance compared to non-interpretable models (Random Forest, XGBoost), and also performed significantly better than a model using a single modality (average AUC=0.82). Clinically relevant insights and multi-modal variable importance rankings were also facilitated by the new model, which have previously been impossible. |
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| Published | 2019-01-23 |
| URL | http://arxiv.org/abs/1901.08125v1 |
| http://arxiv.org/pdf/1901.08125v1.pdf | |
| PWC | https://paperswithcode.com/paper/interpretable-neural-networks-for-predicting |
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Structural Self-adaptation for Decentralized Pervasive Intelligence
| Title | Structural Self-adaptation for Decentralized Pervasive Intelligence |
| Authors | Jovan Nikolic, Evangelos Pournaras |
| Abstract | Communication structure plays a key role in the learning capability of decentralized systems. Structural self-adaptation, by means of self-organization, changes the order as well as the input information of the agents’ collective decision-making. This paper studies the role of agents’ repositioning on the same communication structure, i.e. a tree, as the means to expand the learning capacity in complex combinatorial optimization problems, for instance, load-balancing power demand to prevent blackouts or efficient utilization of bike sharing stations. The optimality of structural self-adaptations is rigorously studied by constructing a novel large-scale benchmark that consists of 4000 agents with synthetic and real-world data performing 4 million structural self-adaptations during which almost 320 billion learning messages are exchanged. Based on this benchmark dataset, 124 deterministic structural criteria, applied as learning meta-features, are systematically evaluated as well as two online structural self-adaptation strategies designed to expand learning capacity. Experimental evaluation identifies metrics that capture agents with influential information and their optimal positioning. Significant gain in learning performance is observed for the two strategies especially under low-performing initialization. Strikingly, the strategy that triggers structural self-adaptation in a more exploratory fashion is the most cost-effective. |
| Tasks | Combinatorial Optimization, Decision Making |
| Published | 2019-04-21 |
| URL | http://arxiv.org/abs/1904.09681v2 |
| http://arxiv.org/pdf/1904.09681v2.pdf | |
| PWC | https://paperswithcode.com/paper/structural-self-adaptation-for-decentralized |
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Speech Recognition with no speech or with noisy speech
| Title | Speech Recognition with no speech or with noisy speech |
| Authors | Gautam Krishna, Co Tran, Jianguo Yu, Ahmed H Tewfik |
| Abstract | The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy. |
| Tasks | EEG, Speech Recognition |
| Published | 2019-03-02 |
| URL | http://arxiv.org/abs/1903.00739v1 |
| http://arxiv.org/pdf/1903.00739v1.pdf | |
| PWC | https://paperswithcode.com/paper/speech-recognition-with-no-speech-or-with |
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Classifying the reported ability in clinical mobility descriptions
| Title | Classifying the reported ability in clinical mobility descriptions |
| Authors | Denis Newman-Griffis, Ayah Zirikly, Guy Divita, Bart Desmet |
| Abstract | Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9% macro F1 score on our task, and yields nearly 80% recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research. |
| Tasks | Natural Language Inference |
| Published | 2019-06-07 |
| URL | https://arxiv.org/abs/1906.03348v1 |
| https://arxiv.org/pdf/1906.03348v1.pdf | |
| PWC | https://paperswithcode.com/paper/classifying-the-reported-ability-in-clinical |
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