Development and assessment of machine learning models
Development and Interpretation of Machine Learning Models for Drug Discovery
... classification models and their predictions has been ...other machine learning approaches such as support vector machines or neural networks, analyzing classification models and rationalizing ...
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Machine Learning Models for Epigenomics
... inspired development of novel computational ...transfer learning method was developed that learns from a high-resolution Hi-C map in one cell-type to enhance the resolution of a Hi-C map in another ...
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Automated Retraining of Machine Learning Models
... This research was just an attempt to see how we could integrate the idea of retraining with AutoML. Retraining is essential in cases where the data and the nature of data changes frequently. Retraining can help the model ...
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Efficient machine learning: models and accelerations
... of machine learning is the adoption of very large models with millions of parameters ...deep learning systems, two orthogonal research and development trends have at- tracted enormous ...
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Abduction-Based Explanations for Machine Learning Models
... the development of efficient decision procedures and effective encodings, not only for NN-based classifiers, but also for other state-of-the-art machine learning ...
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Statistical and Machine Learning Models to Predict Programming Performance
... the development of several initiatives to assist ...Problem-Based Learning (PBL) workshops, the development of an intranet ap plication to provide a centralised repository of first year information, ...
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Semantic Models for Machine Learning
... data learning using kernel ...alternative learning to non-linear functions by projecting the data into a high dimensional feature space in order to increase the power of linear learning ...
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Copula models in machine learning
... copula models have been very popular in statistics and financial modelling (Genest et ...the machine learning ...graphical models, which also focus on high-dimensional dependency structures, ...
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Making machine learning models interpretable
... that machine learning practitioners try to model using their wide palette of methods and ...obtained models are meant to be a synthetic representation of the available, observed data that captures ...
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Performance Comparison of Machine Learning Models
... the machine learning ...different machine learning models is ...different machine learning ...different models have been compared to find the scope of ...
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Sparse machine learning models in bioinformatics
... the machine learning view- ...The machine learning techniques addressing the chal- lenges above can be categorized into two ...vector machine (SVM) ...linear models [5]. In ...
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CLASSIFICATION, MODELS AND APPLICATIONS OF MACHINE LEARNING
... is machine learning. Machine learning is actively being used today, perhaps in many more places than one would ...of machine learning are formed through a complex algorithm or ...
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Machine learning for the design and development of
... models generated using QSAR descriptors are then tuned for the discovery of design rules that may 59.. aid as foundational new knowledge for discovering functional chemical spaces havin[r] ...
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Learning Deep Transformer Models for Machine Translation
... 2.2 On the Importance of Pre-Norm for Deep Residual Network The situation is quite different when we switch to deeper models. More specifically, we find that pre- norm is more efficient for training than post-norm ...
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Screening for Prediabetes Using Machine Learning Models
... respectively, the ANN and SVM models for prediabetes. The KNHANES is a cross-sectional survey that includes approximately 800 questions; it is conducted by the Division of Chronic Disease Surveillance, Korea ...
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Fundamental Factor Models Using Machine Learning
... the machine learning method by controlling basic analytical method and ideas because the field of active management of measurement itself is a subject of deep ...the machine learning method is ...
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Towards Interpretability and Robustness of Machine Learning Models
... CHAPTER 6. A QUERY-EFFICIENT DECISION-BASED ATTACK 78 It is more realistic to evaluate the vulnerability of a machine learning system under the decision-based attack with a limited budget of model queries. ...
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Explaining Machine Learning Models by Generating Counterfactuals
... We address the issue of explaining various machine-learning models by generating counterfactuals for given data points. Counterfactual is a transformation, which shows how to alternate an input ...
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Mechanistic Models and the Explanatory Limits of Machine Learning
... in learning and reasoning in molecular biology (Kindfield 1998; Trujillo ...mechanistic models as narratives, namely being able to infer how a course of events, decomposed into steps, may change if we ...
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Big Data, Machine Learning, Causal Models
... – Causality cannot easily be determined by experts – Variables and Structure may change with new. data sets[r] ...
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