Remembering for the right reasons: Explanations reduce catastrophic forgetting by Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell

“…We have evaluated our approach in the standard and few‐shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. …”
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Design of Logistics Sorting Algorithm Based on Deep Learning and Sampling Evaluation by Chun Feng

“…The accuracy rate and all-class average precision value were higher than other target detection models, and the fluctuation of the value taken was smaller, which was suitable for logistics parcel localization. The position generation model, based on a sampling evaluation, yielded significantly different values compared to other algorithms. …”
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MALDI-TOF mass spectrometry profiling of bovine skim milk for subclinical mastitis detection by Matteo Cuccato, Sara Divari, Paola Sacchi, Flavia Girolami, Francesca Tiziana Cannizzo

“…Classification algorithms (i.e., quick classifier, genetic algorithm, and supervised neural network) were applied for generating models able to classify new spectra (i.e., samples) into the two classes. …”
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Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

“…By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. …”
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Interactive design generation and optimization from generative adversarial networks in spatial computing by Xiaochen Hu, Cun Lin, Tianyi Chen, Weibo Chen

“…The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. …”
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Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics by Uraquitan Lima Filho, Tiago Alexandre Pais, Ricardo Jorge Pais

“…In comparison to other models, our generated models rendered performances with competitive sensitivities (72–85%), specificities (76–85%), accuracies (75–85%), and Receiver Operating Characteristic curves with superior Areas Under the Curve (ROC-AUC of 77–86%). …”
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Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses by Junqiang Zhang, Souma Chowdhury, Jie Zhang, Weiyang Tong, Achille Messac

“…This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. …”
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A procedural footprint enhancement of global topographic surface with multiple levels of detail by Lukáš Brůha, Jan Kolář

“…Such properties of the method close up the gap between a mere exploratory visualization of static, pre-generated models and the models supporting geospatial analysis, which is deemed crucial for applications in Geographic Information Systems, Building Information Modelling and other software industries. …”
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Unscented Kalman Filter-Based Robust State and Parameter Estimation for Free Radical Polymerization of Styrene with Variable Parameters by Zhenhui Zhang, Zhengjiang Zhang, Zhihui Hong

“…However, when the parameters change with system switching, the traditional UKF-SPE cannot detect and track the parameter changes in time, and inaccurate parameters generate modeling errors. To deal with the problem, a UKF-based robust SPE method (UKF-RSPE) for the free radical polymerization of styrene with variable parameters is proposed, introducing a parameter testing criterion based on hypothesis testing and moving windows to directly detect whether the parameters have changed. …”
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Are Torque-Driven Simulation Models of Human Movement Limited by an Assumption of Monoarticularity? by Martin G. C. Lewis, Maurice R. Yeadon, Mark A. King

“…The two-joint torque generator model of squat and countermovement jumps matched measured jump performances more closely (6% and 10% different, respectively) than the single-joint simulation model (10% and 24% different, respectively). …”
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Resourceful Event-Predictive Inference: The Nature of Cognitive Effort by Martin V. Butz, Martin V. Butz

“…To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model (REPI) is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics—to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. …”
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Multi-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism by Tianbo Ji, Chenyang Lyu, Zhichao Cao, Peng Cheng

“…However, neural generation models suffer from the global and local semantic semantic drift problems. …”
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GAN River-I: A process-based low NTG meandering reservoir model dataset for machine learning studies by Chao Sun, Vasily Demyanov, Daniel Arnold

“…Each dataset comprises an ensemble of meandering models representing various plausible patterns and, therefore, can be used as a geologically plausible benchmark for testing generative models’ performance. We provide three data file formats, including image, Ndarray and GSLIB, to adapt to different researchers’ preferences.…”
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Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data by Kaixin Hu, Daguang Han, Guocheng Qin, Yin Zhou, Long Chen, Chunli Ying, Tong Guo, Yanhui Liu

“…The quality of the generated models is gauged using a point cloud deviation chromatogram. …”
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Survey on Synthetic Data Generation, Evaluation Methods and GANs by Alvaro Figueira, Bruno Vaz

“…Generative adversarial networks (GANs) are a state-of-the-art deep generative models that can generate novel synthetic samples that follow the underlying data distribution of the original dataset. …”
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Correlation-based analysis and generation of multiple spike trains using Hawkes models with an exogenous input by Michael Krumin, Inna Reutsky, Shy Shoham

“…However, the diversity of correlation structures that can be explained by the feed-forward, non-recurrent, generative models used in these studies is limited. Hence, methods based on such models occasionally fail when analyzing correlation structures that are observed in neural activity. …”
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Evaluation of the performance of artificial neural networks integrated with whale optimization and ant colony optimization algorithms in estimating the drilling rate of penetration... by Ehsan Brenjkar

“…In the following, 12.25” hole-section information of two wells containing a similar sequence of drilled formations was used to train and test the models, and then the generated models were validated by the third well information. …”
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Integration of multisource data to support the identification of lateritic regolith in Eastern - Bahia, northeastern Brazil by Edgar Romeo Herrera de Figueiredo Iza, Rodrigo Soares Vieira dos Santos, Basílio Elesbão da Cruz Filho

“…The observed correlation between the generated models and the direct data validates the effectiveness of the techniques used.…”
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The Online Frequency Security Assessment of a Power System Considering the Time-Varying Characteristics of Renewable Energy Inertia by Zefeng Peng, Yulin Lu, Yingmin Zhang, Wenjun Deng, Qi Zeng

“…Firstly, a unified virtual synchronous generator model is established to identify the virtual inertia time constant of the renewable energy station in real time; then, under the pre-defined frequency safety verification event, the maximum deviation of the system frequency is periodically calculated and judged based on the G model to realize the online frequency safety assessment. …”
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Model Selection Path and Construction of Model Confidence Set under High-Dimensional Variables by Faguang Wen, Jiming Jiang, Yihui Luan

“…To further confirm that MSP can successfully generate model confidence sets that maintain the given confidence level as the sample size increases, we conduct extensive simulation tests with high-dimensional variables. …”
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