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Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection
BackgroundThe semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty,...
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Predicting shallow water dynamics using echo-state networks with transfer learning
In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations. In particular, while most...
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Mathematical methods for maintenance and operation cost prediction based on transfer learning in State Grid
The electric power enterprise is an important basic energy industry for national development, and it is also the first basic industry of the national...
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Friend Recommendation System Using Transfer Learning in the Autoencoder
Social information helps the traditional recommendation system to provide more personalized services. Autoencoder provides an unsupervised...
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Video-Based Micro Expressions Recognition Using Deep Learning and Transfer Learning
Involuntary facial muscle movements produce micro-expressions. They are extremely subtle, very difficult to observe with the naked eyes and last for...
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Machine Learning Moment Closure Models for the Radiative Transfer Equation III: Enforcing Hyperbolicity and Physical Characteristic Speeds
This is the third paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation. In our...
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Syntax-based transfer learning for the task of biomedical relation extraction
BackgroundTransfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related,...
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Machine Reading Comprehension Model in Domain-Transfer Task
AbstractThe paper studies domain-transfer capabilities of the machine reading comprehension model (MRC) in named entity recognition task. In such a...
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Reviewing Deep Learning Methods in the Applied Problems of Economic Monitoring Based on Geospatial Data
Development of modern observation technologies, increase of the amount of open data, and development of new approaches to their processing open new...
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Causal Transfer Evidential Clustering
Classical prototype-based clustering algorithms usually cannot achieve satisfactory results when the data is insufficient. Transfer learning can be...
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Machine Learning to Control Network Powered by Computing Infrastructure
AbstractMachine learning (ML) methods are applied to optimal resource control for Network Powered by Computing Infrastructure (NPC)—a new generation...
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Multimodal Deep Learning
Multimodal deep learning has gained significant attention and shown great promise in various domains, including medical, manufacturing, Internet of...
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Optimal pivot path of the simplex method for linear programming based on reinforcement learning
Based on the existing pivot rules, the simplex method for linear programming is not polynomial in the worst case. Therefore, the optimal pivot of the...
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Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma
BackgroundTransfer learning is a common practice in image classification with deep learning where the available data is often limited for training a...
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Partial Learning Using Partially Explicit Discretization for Heterogeneous Transport Problem Simulation
AbstractThis article presents a novel approach for learning and simulating multicontinuum/multiscale problems with limited observations. The proposed...
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Outlier Robust Feature Correspondence by Learning Based Matching Process
Feature correspondence is a crucial aspect of various computer vision and robot vision tasks. Unlike traditional optimization-based matching...
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Simple and effective meta relational learning for few-shot knowledge graph completion
Conventional knowledge graph completion methods are effective for completing knowledge graphs (KGs), but they face significant challenges when...
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Higher-Order Iterative Learning Control Algorithms for Linear Systems
AbstractIterative learning control (ILC) algorithms appeared in connection with the problems of increasing the accuracy of performing repetitive...
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The Mori–Zwanzig formulation of deep learning
We develop a new formulation of deep learning based on the Mori–Zwanzig (MZ) formalism of irreversible statistical mechanics. The new formulation is...
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Constrained optimization based adversarial example generation for transfer attacks in network intrusion detection systems
Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious network packets without requiring feature engineering....
