DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies

He, Wenbin; Lee, Teng-Yok; Baar, Jeroen van; Wittenburg, Kent; Shen, Han‐Wei

doi:10.1109/pacificvis48177.2020.7127

Cited by 13 publications

(14 citation statements)

References 20 publications

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“…This change is not trivial since there are cases where the representation of two different inputs can be misaligned, for example, when data include sequences of various lengths. In these cases, a solution is to use Dynamic Time Warping (DTW) algorithms [SC07, SC78] to align sub‐representations and then use a standard distance across them [HLvB*20].…”

Section: Papers Categorizationmentioning

confidence: 99%

State of the Art of Visual Analytics for eXplainable Deep Learning

Rosa

Blasilli

Bourqui

et al. 2023

Computer Graphics Forum

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The use and creation of machine-learning-based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black-box model. During the last few years, Visual Analytics has provided several proposals to cope with these drawbacks, supporting the emerging eXplainable Deep Learning field. This survey aims to (i) systematically report the contributions of Visual Analytics for eXplainable Deep Learning; (ii) spot gaps and challenges; (iii) serve as an anthology of visual analytical solutions ready to be exploited and put into operation by the Deep Learning community (architects, trainers and end users) and (iv) prove the degree of maturity, ease of integration and results for specific domains. The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community. An interactive explorable version of this survey is available online at https:// awarediag-sapienza.github.io/ VA4XDL.

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Section: Papers Categorizationmentioning

confidence: 99%

State of the Art of Visual Analytics for eXplainable Deep Learning

Rosa

Blasilli

Bourqui

et al. 2023

Computer Graphics Forum

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“…Because of its excellent performance, LSTM is used for a large number of sequence learning tasks, such as robot control [32], speech recognition [33], time series prediction [34,35], and market prediction [36,37].…”

Section: Lstm and Parameter Optimizationmentioning

confidence: 99%

An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

Rong

Zhou

Cao

et al. 2022

Mathematical Problems in Engineering

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Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.

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“…Then at the detail level, it uses segment clustering and a pattern mining algorithm to help experts identify common as well as suspicious patterns in the event-sequences of the agents in Qnetworks. As another example, He et al [87] proposed DynamicsExplorer to diagnose an LSTM trained to control a ball-in-maze game. To support quick identification of where training failures arise, it visualizes ball trajectories with a trajectory variability plot, as well as their clusters using a parallel coordinates plot.…”

Section: Analyzing Training Dynamicsmentioning

confidence: 99%

A survey of visual analytics techniques for machine learning

et al. 2020

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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

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DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies

Cited by 13 publications

References 20 publications

State of the Art of Visual Analytics for eXplainable Deep Learning

State of the Art of Visual Analytics for eXplainable Deep Learning

An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

A survey of visual analytics techniques for machine learning

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