Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are ...hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. In this work, we identify and formalize a series of independent challenges that embody the difficulties that must be addressed for RL to be commonly deployed in real-world systems. For each challenge, we define it formally in the context of a Markov Decision Process, analyze the effects of the challenge on state-of-the-art learning algorithms, and present some existing attempts at tackling it. We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems. Our proposed challenges are implemented in a suite of continuous control environments called realworldrl-suite which we propose an as an open-source benchmark.
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain ...both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
Over the past two decades, support vector machines (SVMs) have become a popular supervised machine learning model, and plenty of distinct algorithms are designed separately based on different KKT ...conditions of the SVM model for classification/regression with different losses, including convex and or nonconvex loss. In this paper, we propose an algorithm that can train different SVM models in a
unified
scheme. First, we introduce a definition of the least squares type of difference of convex loss (LS-DC) and show that the most commonly used losses in the SVM community are LS-DC loss or can be approximated by LS-DC loss. Based on the difference of convex algorithm (DCA), we then propose a unified algorithm called
UniSVM
which can solve the SVM model with any convex or nonconvex LS-DC loss, wherein only a vector is computed by the specifically chosen loss. UniSVM has a dominant advantage over all existing algorithms for training robust SVM models with nonconvex losses because it has a closed-form solution per iteration, while the existing algorithms always need to solve an L1SVM/L2SVM per iteration. Furthermore, by the low-rank approximation of the kernel matrix, UniSVM can solve large-scale nonlinear problems efficiently. To verify the efficacy and feasibility of the proposed algorithm, we perform many experiments on small artificial problems and large benchmark tasks both with and without outliers for classification and regression for comparison with state-of-the-art algorithms. The experimental results demonstrate that UniSVM can achieve comparable performance in less training time. The foremost advantage of UniSVM is that its core code in Matlab is less than 10 lines; hence, it can be easily grasped by users or researchers.
We study the problem of training deep fully connected neural networks with Rectified Linear Unit (ReLU) activation function and cross entropy loss function for binary classification using gradient ...descent. We show that with proper random weight initialization, gradient descent can find the global minima of the training loss for an over-parameterized deep ReLU network, under certain assumption on the training data. The key idea of our proof is that Gaussian random initialization followed by gradient descent produces a sequence of iterates that stay inside a small perturbation region centered at the initial weights, in which the training loss function of the deep ReLU networks enjoys nice local curvature properties that ensure the global convergence of gradient descent. At the core of our proof technique is (1) a milder assumption on the training data; (2) a sharp analysis of the trajectory length for gradient descent; and (3) a finer characterization of the size of the perturbation region. Compared with the concurrent work (Allen-Zhu et al. in A convergence theory for deep learning via over-parameterization,
2018a
; Du et al. in Gradient descent finds global minima of deep neural networks,
2018a
) along this line, our result relies on milder over-parameterization condition on the neural network width, and enjoys faster global convergence rate of gradient descent for training deep neural networks.
Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social ...distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availability of annotated data. To address this issue, we introduce the Moral Foundations Twitter Corpus, a collection of 35,108 tweets that have been curated from seven distinct domains of discourse and hand annotated by at least three trained annotators for 10 categories of moral sentiment. To facilitate investigations of annotator response dynamics, we also provide psychological and demographic metadata for each annotator. Finally, we report moral sentiment classification baselines for this corpus using a range of popular methodologies.
Natural Language Processing utilization in Healthcare Hudaa, Syihaabul; Setiyadi, Dwi Bambang Putut; Lydia, E. Laxmi ...
International journal of engineering and advanced technology,
10/2019, Volume:
8, Issue:
6s2
Journal Article
Open access
The significance of consolidating Natural Language Processing (NLP) techniques in clinical informatics research has been progressively perceived over the previous years, and has prompted ...transformative advances. Ordinarily, clinical NLP frameworks are created and assessed on word, sentence, or record level explanations that model explicit traits and highlights, for example, archive content (e.g., persistent status, or report type), record segment types (e.g., current meds, past restorative history, or release synopsis), named substances and ideas (e.g., analyses, side effects, or medicines) or semantic qualities (e.g., nullification, seriousness, or fleetingness). While some NLP undertakings consider expectations at the individual or gathering client level, these assignments still establish a minority. Here we give an expansive synopsis and layout of the difficult issues engaged with characterizing suitable natural and outward assessment strategies for NLP look into that will be utilized for clinical results research, and the other way around. A specific spotlight is set on psychological wellness investigate, a zone still generally understudied by the clinical NLP look into network, however where NLP techniques are of prominent importance. Ongoing advances in clinical NLP strategy improvement have been huge, yet we propose more accentuation should be put on thorough assessment for the field to progress further. To empower this, we give noteworthy recommendations, including an insignificant convention that could be utilized when announcing clinical NLP strategy improvement and its assessment.
Advancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural ...language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing professionals, and policymakers.
Summary
Maize exhibits marked growth and yield response to supplemental nitrogen (N). Here, we report the functional characterization of a maize NIN‐like protein ZmNLP5 as a central hub in a ...molecular network associated with N metabolism. Predominantly expressed and accumulated in roots and vascular tissues, ZmNLP5 was shown to rapidly respond to nitrate treatment. Under limited N supply, compared with that of wild‐type (WT) seedlings, the zmnlp5 mutant seedlings accumulated less nitrate and nitrite in the root tissues and ammonium in the shoot tissues. The zmnlp5 mutant plants accumulated less nitrogen than the WT plants in the ear leaves and seed kernels. Furthermore, the mutants carrying the transgenic ZmNLP5 cDNA fragment significantly increased the nitrate content in the root tissues compared with that of the zmnlp5 mutants. In the zmnlp5 mutant plants, loss of the ZmNLP5 function led to changes in expression for a significant number of genes involved in N signalling and metabolism. We further show that ZmNLP5 directly regulates the expression of nitrite reductase 1.1 (ZmNIR1.1) by binding to the nitrate‐responsive cis‐element at the 5′ UTR of the gene. Interestingly, a natural loss‐of‐function allele of ZmNLP5 in Mo17 conferred less N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants. Our findings show that ZmNLP5 is involved in mediating the plant response to N in maize.
Significance Statement
In this study, we report the functional characterization of ZmNLP5 for its role in modulating the N response, suggesting that ZmNLP5 is a potential candidate for improving N use efficiency in maize production.
Abstract
Because the computer cannot directly understand the text corpus in the NLP task, the first thing to do is to represent the characteristics of the natural language numerically, and the word ...vector technology provides a good way to express it. Because Word2vec considers context and has fewer dimensions, it is now more popular words embedded. However, due to the particularity of Chinese, word2vec cannot accurately identify the polysemy of words. In this paper, a lightweight and effective method is used to merge vocabulary into character representation. This approach avoids designing complex sequence modeling architectures. for any neural network model, simply fine-tuning the character input layer can introduce vocabulary information. The model also uses the modified LSTM to bridge the enormous LSTM and the Transformer model. The interaction between input and context provides a richer modeling space that significantly improves testing on all four public datasets.
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