Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis

Luo, Xin; Yuan, Ye; Chen, Sili; Zeng, Nianyin; Wang, Zidong

doi:10.1109/tkde.2020.3033324

Cited by 186 publications

(29 citation statements)

References 63 publications

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“…When it is promoted to the application field, the robustness of the algorithm is also one of the most concerning core issues. Commonly, the data of reinforcement learning are often incomplete, so we refer the readers to the following literature (Shang et al, 2019 ; Luo et al, 2020 ; Wu D. et al, 2020 ; Wu et al, 2020 ; Liu et al, 2021 ) for more details.…”

Section: Dexterous Manipulation For Multi-fingered Robotic Hands With...mentioning

confidence: 99%

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Wang

2022

Front. Neurorobot.

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With the increasing demand for the dexterity of robotic operation, dexterous manipulation of multi-fingered robotic hands with reinforcement learning is an interesting subject in the field of robotics research. Our purpose is to present a comprehensive review of the techniques for dexterous manipulation with multi-fingered robotic hands, such as the model-based approach without learning in early years, and the latest research and methodologies focused on the method based on reinforcement learning and its variations. This work attempts to summarize the evolution and the state of the art in this field and provide a summary of the current challenges and future directions in a way that allows future researchers to understand this field.

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Section: Dexterous Manipulation For Multi-fingered Robotic Hands With...mentioning

confidence: 99%

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Wang

2022

Front. Neurorobot.

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“…This indicates the superior learning capability of the TG-DNN than the data-driven DNN trained with very scarce data. It should be noted that other efforts have also been made to deal with missing data values, such as nonnegative latent factor model [54], [55], which outperforms the state-of-the-art predictors.…”

Section: Learning With Scarce Training Datamentioning

confidence: 99%

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Liu

Xia

Shi

et al. 2021

IEEE/CAA J. Autom. Sinica

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Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift's law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.

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“…A possible research path involves the combination of CRFs with BiLSTMs, which can achieve state-ofthe-art performance for specific language resources [10] and be potentially useful in the identification of entities of the ENAMEX group, e.g., [7,31]. In addition, methodological approaches involving latent factor analysis may also contribute to improve computational efficiency and prediction accuracy of trained models for unseen data [32][33][34][35][36] .…”

Section: Influence Of Self-training On Model Performancementioning

confidence: 99%

Iterative Named Entity Recognition with Conditional Random Fields

et al. 2021

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Named entity recognition (NER) constitutes an important step in the processing of unstructured text content for the extraction of information as well as for the computer-supported analysis of large amounts of digital data via machine learning methods. However, NER often relies on domain-specific knowledge, being conducted manually in a time- and human-resource-intensive process. These can be reduced with statistical models performing NER automatically. The current work investigates whether Conditional Random Fields (CRF) can be efficiently trained for NER in German texts, by means of an iterative procedure combining self-learning with a manual annotation–active learning–component. The training dataset increases continuously with the iterative procedure. Whilst self-learning did not markedly improve the performance of the CRF for NER, the manual annotation of sentences with the lowest probability of correct prediction clearly improved the model F1-score and simultaneously reduced the amount of manual annotation required to train the model. A model with an F1-score of 0.885 was able to be trained in 11.4 h.

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Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis

Cited by 186 publications

References 63 publications

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Iterative Named Entity Recognition with Conditional Random Fields

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