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Found 9 papers, 0 papers with code
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization
Connectionist temporal classification (CTC) and attention-based encoder decoder (AED) joint training has been widely applied in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A BERT-based Unsupervised Grammatical Error Correction Framework
Grammatical error correction (GEC) is a challenging task of natural language processing techniques.
MNL-Bandits under Inventory and Limited Switches Constraints
Optimizing the assortment of products to display to customers is a key to increasing revenue for both offline and online retailers.
Quantitative Evaluation of Common Cause Failures in High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants
This paper proposes a Platform for Risk Assessment of DIC (PRADIC) that is developed by Idaho National Laboratory (INL).
An Integrated Risk Assessment Process of Safety-Related Digital I&C Systems in Nuclear Power Plants
Existing analyses of CCFs in I&C systems mainly focus on hardware failures.
Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data
Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions.
Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities.
Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach.
