Literature review of deep learning research areas
Deep learning (DL) is a powerful machine learning field that has achieved considerable
success in many research areas. Especially in the last decade, the-state-of-the-art studies …
success in many research areas. Especially in the last decade, the-state-of-the-art studies …
Ensemble-based deep reinforcement learning for chatbots
Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge
in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this …
in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this …
Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach
Robust and reliable drivetrain is important for preventing electromechanical (eg, wind
turbine) downtime. In recent years, advanced machine learning (ML) techniques including …
turbine) downtime. In recent years, advanced machine learning (ML) techniques including …
A divide-and-conquer approach to neural natural language generation from structured data
Current approaches that generate text from linked data for complex real-world domains can
face problems including rich and sparse vocabularies as well as learning from examples of …
face problems including rich and sparse vocabularies as well as learning from examples of …
A data-efficient deep learning approach for deployable multimodal social robots
H Cuayáhuitl - Neurocomputing, 2020 - Elsevier
The deep supervised and reinforcement learning paradigms (among others) have the
potential to endow interactive multimodal social robots with the ability of acquiring skills …
potential to endow interactive multimodal social robots with the ability of acquiring skills …
SenticNet and Abstract Meaning Representation driven Attention-Gate semantic framework for aspect sentiment triplet extraction
X Sun, J Qi, Z Zhu, M Li, H Pei, J Meng - Engineering Applications of …, 2025 - Elsevier
Aspect sentiment triplet extraction aims to analyze aspect-level sentiment in the form of
triplets, including extracting aspect-opinion pairs and predicting the sentiment polarities of …
triplets, including extracting aspect-opinion pairs and predicting the sentiment polarities of …
Deep reinforcement learning for chatbots using clustered actions and human-likeness rewards
H Cuayáhuitl, D Lee, S Ryu, S Choi… - … joint conference on …, 2019 - ieeexplore.ieee.org
Training chatbots using the reinforcement learning paradigm is challenging due to high-
dimensional states, infinite action spaces and the difficulty in specifying the reward function …
dimensional states, infinite action spaces and the difficulty in specifying the reward function …
A dual transformer model for intelligent decision support for maintenance of wind turbines
J Chatterjee, N Dethlefs - 2020 International Joint Conference …, 2020 - ieeexplore.ieee.org
Wind energy is one of the fastest-growing sustainable energy sources in the world but relies
crucially on efficient and effective operations and maintenance to generate sufficient …
crucially on efficient and effective operations and maintenance to generate sufficient …
Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero-and Few-shot Learning with Domain Distribution Alignment
Online shopping has become a crucial way to encourage daily consumption, where the User-
generated, or crowdsourced product comments, can offer a broad range of feedback on e …
generated, or crowdsourced product comments, can offer a broad range of feedback on e …
Schema-guided natural language generation
Neural network based approaches to data-to-text natural language generation (NLG) have
gained popularity in recent years, with the goal of generating a natural language prompt that …
gained popularity in recent years, with the goal of generating a natural language prompt that …