Explicitly Constrained Black-Box Optimization With Disconnected Feasible Domains Using Deep Generative Models by Naoki Sakamoto, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

“…To stabilize the training of the deep generative model as the decoder, we propose decomposing the decoder into sub-models, introducing skip connections to each sub-model, and training the sub-models sequentially with separate loss functions. …”
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Lightweight Explicit 3D Human Digitization via Normal Integration by Jiaxuan Liu, Jingyi Wu, Ruiyang Jing, Han Yu, Jing Liu, Liang Song

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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

“…The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. …”
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Stochastic Explicit Calibration Algorithm for Survival Models by Jeongho Park, Sangwook Kang, Gwangsu Kim

“…In this study, we introduce Stochastic Explicit Calibration (S-cal), an algorithm that employs random intervals instead of fixed bins, thereby advancing the calibration methods used in deep networks. …”
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Implicit versus explicit Bayesian priors for epistemic uncertainty estimation in clinical decision support. by Malte Blattmann, Adrian Lindenmeyer, Stefan Franke, Thomas Neumuth, Daniel Schneider

“…This shortcoming highlights the need for decision-support systems that quantify and communicate per-query epistemic (knowledge) uncertainty. Approximate Bayesian deep learning methods address this need by introducing principled uncertainty estimates over the model's function. …”
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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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A comprehensive survey of scoring functions for protein docking models by Azam Shirali, Vitalii Stebliankin, Ukesh Karki, Jimeng Shi, Prem Chapagain, Giri Narasimhan

“…Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. …”
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Geometry‐Enhanced Implicit Function for Detailed Clothed Human Reconstruction With RGB‐D Input by Pengpeng Liu, Zhi Zeng, Qisheng Wang, Min Chen, Guixuan Zhang

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Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China by Jun Zhu, Zhong-Xiu Sun

“…To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CEC_clay from CEC_soil using experimental soil data. …”
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A transformer based generative chemical language AI model for structural elucidation of organic compounds by Xiaofeng Tan

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End-to-end handwritten Ge’ez multiple numerals recognition using deep learning by Ruchika Malhotra, Maru Tesfaye Addis

“…To enable end-to-end training without explicit alignment, the model uses attention mechanisms and a connectionist temporal classification-based loss function. …”
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Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell by Mehwish Faiz, Saad Jawaid Khan, Fahad Azim, Nazia Ejaz, Fahad Shamim

“…Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. …”
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Enhanced Milne-Simpson's methods for autonomous and singular differential equations by Ajimot Folasade Adebisi, Saheed Aremu, Muideen Ogunniran, Kamiludeen Tijani, Adewole Ajileye

“…Unlike previous works that either apply neural networks as standalone solvers or generic correctors, our approach explicitly tailors the neural architecture to learn correction functions that complement the structural dynamics of Milne-Simpson’s output. …”
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AlphaMissense Predictions and ClinVar Annotations: A Deep Learning Approach to Uveal Melanoma by David J. Taylor Gonzalez, MD, Mak B. Djulbegovic, MD, MSc, Meghan Sharma, MD, MPH, Michael Antonietti, BS, Colin K. Kim, BS, Vladimir N. Uversky, PhD, DSc, Carol L. Karp, MD, Carol L. Shields, MD, Matthew W. Wilson, MD

“…We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM. …”
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Neural network distillation of orbital dependent density functional theory by Matija Medvidović, Jaylyn C. Umana, Iman Ahmadabadi, Domenico Di Sante, Johannes Flick, Angel Rubio

“…These goals are achieved by using a recently developed class of robust neural network models capable of modeling functionals, as opposed to functions, with explicitly enforced spatial symmetries. …”
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Integrating the Prior Shape Knowledge Into Deep Model and Feature Fusion for Topologically Effective Brain Tumor Segmentation by Salma Asif, Ahmad Raza Shahid, Kiran Aftab, Syed Ather Enam

“…Deep learning techniques totally rely on the loss function optimization and due to the lack of explicit form of prior knowledge, they may struggle to generate the accurate tumor shapes. …”
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Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining by Xinran Liu, Jianmin Yang, Changyu Lu, Enhua Zhang, Wenhao Xu

“…To address these challenges, this study introduces a robust FLS image mosaicking framework that functions without additional sensor input. The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. …”
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Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology by Ayako Yagahara, Masahito Uesugi, Hideto Yokoi

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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. …”
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Security of End-to-End medical images encryption system using trained deep learning encryption and decryption network by Saba Inam, Shamsa Kanwal, Anousha Anwar, Noor Fatima Mirza, Hessa Alfraihi

“…Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. …”
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