Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification by P. Jeneessha, Vinoth Kumar Balasubramanian

“…This paper aims to utilize domain knowledge and image data to improve the classification performance of pre-trained models namely Inception V3, DenseNet 121, ResNet 50, MobileNet V2, and VGG 16. …”
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Intra-day dispatch method via deep reinforcement learning based on pre-training and expert knowledge by Yanbo Chen, Qintao Du, Huayu Dong, Tao Huang, Jiahao Ma, Zitao Xu, Zhihao Wang

“…At the same time, expert knowledge is embedded in the deep reinforcement learning to guide the training of the agent. …”
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Distilling Diverse Knowledge for Deep Ensemble Learning by Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

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Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature by Chenyang Song, Zheni Zeng, Changyao Tian, Kuai Li, Yuan Yao, Suncong Zheng, Zhiyuan Liu, Maosong Sun

Subjects: “…Biomedical knowledge retrieval…”
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Optimizing Knowledge Transfer Graph for Deep Collaborative Learning by Soma Minami, Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

“…Knowledge transfer among multiple networks, using predicted probabilities or intermediate-layer activations, has evolved significantly through extensive manual design, ranging from simple teacher—student approaches (for example, knowledge distillation) to bidirectional cohort methods (for example, deep mutual learning). …”
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The Impact of Integrating Shallow and Deep Information on Knowledge Distillation by Yilin Miao, Yuhong Tang, Huangliang Ren, Jianjun Li

“…It effectively addresses issues such as gradient vanishing and degradation in deep neural networks, making the training process more manageable. …”
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Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators by Lucía Güitta-López, Vincenzo Suriani, Jaime Boal, Álvaro J. López-López, Daniele Nardi

“…Our architecture combines KGEs with visual observations, enabling the agent to exploit environmental knowledge during training. Experimental validation with robotic manipulators in environments featuring both fixed and randomized target attributes demonstrates that our method achieves up to 60% reduction in learning time and improves task accuracy by approximately 15 percentage points, without increasing training time or computational complexity. …”
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Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics by Shuhao Ma, Yu Cao, Ian D. Robertson, Chaoyang Shi, Jindong Liu, Zhi-Qiang Zhang

“…In this paper, we propose a knowledge-based deep learning framework for time-efficient inverse dynamic analysis, which can predict muscle activation and muscle forces from joint kinematic data directly while not requiring any label information during model training. …”
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M3AE-Distill: An Efficient Distilled Model for Medical Vision–Language Downstream Tasks by Xudong Liang, Jiang Xie, Mengfei Zhang, Zhuo Bi

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Deep Learning for Automatic Image Captioning in Poor Training Conditions by Caterina Masotti, Danilo Croce, Roberto Basili

“…The disadvantage that comes with this straightforward result is that this approach requires the existence of large-scale corpora, which are not available for many languages.This paper introduces a simple methodology to automatically acquire a large-scale corpus of 600 thousand image/sentences pairs in Italian. At the best of our knowledge, this corpus has been used to train one of the first neural captioning systems for the same language. …”
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Asymmetrical estimator for training encapsulated deep photonic neural networks by Yizhi Wang, Minjia Chen, Chunhui Yao, Jie Ma, Ting Yan, Richard Penty, Qixiang Cheng

“…Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs). …”
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Enhanced intelligent train operation algorithms for metro train based on expert system and deep reinforcement learning. by Yunhu Huang, Wenzhu Lai, Dewang Chen, Geng Lin, Jiateng Yin

“…In this paper, expert knowledge is combined with deep reinforcement learning algorithm (Proximal Policy Optimization, PPO) and two enhanced intelligent train operation algorithms (EITO) are proposed. …”
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Exercise Semantic Embedding for Knowledge Tracking in Open Domain by Zhi Cheng, Jinlong Li

Subjects: “…deep learning…”
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Deep learning for occupation recognition and knowledge discovery in rheumatology clinical notes by Alfredo Madrid-García, Inés Pérez-Sancristobal, Leticia Leon, Lydia Abásolo, Benjamín Fernández-Gutiérrez, Luis Rodríguez-Rodríguez

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Osteosarcoma knowledge graph question answering system: deep learning-based knowledge graph and large language model fusion by Lulu Zhang, Weisong Zhao, Zhiwei Cheng, Yafei Jiang, Kai Tian, Jia Shi, Zhenyu Jiang, Yingqi Hua

“…The extracted elements were synthesized to create the OSKG, resulting in a deep learning-based knowledge base to explore osteosarcoma pathogenesis and molecular mechanisms. …”
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Nurses’ knowledge, attitudes, and practices regarding deep vein thrombosis and the nursing management by Suzhen Hu, Jinyin Huang, Hua Wang, Yan Wang

“…To assess the knowledge, attitudes, and practices (KAP) of nurses regarding deep vein thrombosis (DVT) and its nursing management. …”
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Short video preloading via domain knowledge assisted deep reinforcement learning by Yuhong Xie, Yuan Zhang, Tao Lin, Zipeng Pan, Si-Ze Qian, Bo Jiang, Jinyao Yan

“…In this paper, we propose an end-to-end Deep reinforcement learning framework with Action Masking called DAM that leverages domain knowledge to learn an optimal policy for short video preloading. …”
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A multi-objective optimization approach for the virtual coupling train set driving strategy by Junting Lin, Maolin Li, Xiaohui Qiu

Subjects: “…High-speed trains…”
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Optimizing Deep Learning Models for Resource‐Constrained Environments With Cluster‐Quantized Knowledge Distillation by Niaz Ashraf Khan, A. M. Saadman Rafat

“…To address these issues, we propose Cluster‐Quantized Knowledge Distillation (CQKD), a novel framework that integrates structured pruning with knowledge distillation, incorporating cluster‐based weight quantization directly into the training loop. …”
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Method of adaptive knowledge distillation from multi-teacher to student deep learning models by Oleksandr Chaban, Eduard Manziuk, Pavlo Radiuk

“…In this work, we improve multi-teacher knowledge distillation by developing a holistic framework, enhanced multi-teacher knowledge distillation (EMTKD), that synergistically integrates three components: domain adaptation within teacher training, an instance-specific adap-tive weighting mechanism for knowledge fusion, and semi-supervised learning to leverage unlabeled data. …”
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