Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects by Ling Zhao, Xun Lv, Lili Zhu, Binyan Luo, Hang Cao, Jiahao Cui, Haifeng Li, Jian Peng

“…The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Objectives: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. …”
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Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Results The results demonstrate the superiority of the deep neural network model in terms of the coefficient of determination and MSE index. …”
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In-vivo high-resolution χ-separation at 7T by Jiye Kim, Minjun Kim, Sooyeon Ji, Kyeongseon Min, Hwihun Jeong, Hyeong-Geol Shin, Chungseok Oh, Robert J. Fox, Ken E. Sakaie, Mark J. Lowe, Se-Hong Oh, Sina Straub, Seong-Gi Kim, Jongho Lee

“…To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2′ map. …”
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Prediction of viscosity of blast furnace slag based on NRBO-DNN model by Zhe Li, Meng Wang, Rui Xu, Juanjuan Jiang, Jie Li, Zunqian Zhang, Aimin Yang

“…Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. …”
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AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

“…Methods: We conducted a retrospective multicenter study on patients undergoing liver resection across three cohorts (N = 514). We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. …”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Goal:</italic> In recent years, deep neural networks have consistently outperformed previously proposed methods in the domain of medical segmentation. …”
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Channel state information–based multi-level fingerprinting for indoor localization with deep learning by Tao Li, Hai Wang, Yuan Shao, Qiang Niu

“…In the offline training phase, deep neural networks are used to train the optimal weights. …”
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Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods by Hongqing Song, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, Qipeng Liu

“…Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. …”
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Complex Risk Statistics with Scenario Analysis by Fei Sun, Yichuan Dong

“…Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representations for such risk.…”
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DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation by Mingxin Gan, Hang Zhang

“…To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. …”
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Classification of earth surface image segmentation methods by D. V. Kypriyanava, D. Y. Pertsau, M. M. Tatur

“…Such approaches as template matching, machine learning and deep neural networks, as well as application of knowledge about analyzed objects are considered. …”
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Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT by Lal Khan, Atika Qazi, Hsien-Tsung Chang, Mousa Alhajlah, Awais Mahmood

“…Abstract Sentiment analysis (SA) as a research field has gained popularity among the researcher throughout the globe over the past 10 years. Deep neural networks (DNN) and word vector models are employed nowadays and perform well in sentiment analysis. …”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. …”
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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments by Shixi Tang, Jinan Gu, Keming Tang, Wei Ding, Zhengyang Shang

“…The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. …”
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Part of Speech Tagging: Shallow or Deep Learning? by Robert Östling

“… Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. …”
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Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid by N. A. Serebryakov

“…The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. …”
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Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach by Cheng Cheng, Elayaraja Aruchunan, Muhamad Hifzhudin Noor Aziz

“…This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. …”
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Image Quality Assessments by Leveraging Diverse Visual Tasks by Joonhee Lee, Dongwon Park, Se Young Chun

“…While recent advances in deep neural networks (DNNs) have sparked much research on IQA, with the hope for IQA to mimic humans effectively, there has been a lack of systematic and analytical research on understanding what factors humans prioritize during IQA. …”
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