A Vegetable-Price Forecasting Method Based on Mixture of Experts by Chenyun Zhao, Xiaodong Wang, Anping Zhao, Yunpeng Cui, Ting Wang, Juan Liu, Ying Hou, Mo Wang, Li Chen, Huan Li, Jinming Wu, Tan Sun

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Mixture of Expert Large Language Model for Legal Case Element Recognition by YIN Hua, WU Zihao, LIU Tingting, ZHANG Jiajia, GAO Ziqian

“…This paper introduces a conversational mixture of expert element recognition LLM. The proposed model in this paper first designs specific prompts tailored to the characteristics of cases for the ChatGLM3-6B-base model. …”
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Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system by Md Hishamur Rahman, Shakil Mohammad Rifaat, Soumik Nafis Sadeek, Masnun Abrar, Dongjie Wang

“…Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting these spatio-temporal tasks in a city as well as across different cities. …”
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Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. by Jaron T Colas, John P O'Doherty, Scott T Grafton

“…In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.…”
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Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection by Zeyuan Yang, Yixuan Wang, Haokun Shi, Qiang Qiu

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Mixture of Experts Framework Based on Soft Actor-Critic Algorithm for Highway Decision-Making of Connected and Automated Vehicles by Fuxing Yao, Chao Sun, Bing Lu, Bo Wang, Haiyang Yu

“…This paper proposes a Mixture of Expert method (MoE) based on Soft Actor-Critic (SAC), where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state. …”
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Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding by Ukyo Honda, Tatsushi Oka, Peinan Zhang, Masato Mita

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MoE-NuSeg: Enhancing nuclei segmentation in histology images with a two-stage Mixture of Experts network by Xuening Wu, Yiqing Shen, Qing Zhao, Yanlan Kang, Wenqiang Zhang

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Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma by Noriaki Hashimoto, Hiroyuki Hanada, Hiroaki Miyoshi, Miharu Nagaishi, Kensaku Sato, Hidekata Hontani, Koichi Ohshima, Ichiro Takeuchi

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Simultaneous estimation of multiple soil properties from vis-NIR spectra using a multi-gate mixture-of-experts with data augmentation by Xiaoqing Wang, Mei-Wei Zhang, Ya-Nan Zhou, Lingli Wang, Ling-Tao Zeng, Yu-Pei Cui, Xiao-Lin Sun

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Patient-Adaptive Beat-Wise Temporal Transformer for Atrial Fibrillation Classification in Continuous Long-Term Cardiac Monitoring by Sangkyu Kim, Jiwoo Lim, Jaeseong Jang

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MELD3: Integrating Multi-Task Ensemble Learning for Driver Distraction Detection by Gokhan Azizoglu, Ahmet Nusret Toprak

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The cognitive reality monitoring network and theories of consciousness by Aurelio Cortese, Mitsuo Kawato

“…The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. …”
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Research on Predicting Super-Relational Data Links for Mine Hoists Within Hyper-Relational Knowledge Graphs by Xiaochao Dang, Xiaoling Shu, Fenfang Li, Xiaohui Dong

“…This paper proposes the HyLinker model, designed to improve the representation of entities and relations through modular components, including an entity neighbor aggregator, a relation qualifier aggregator, MoE-LSTM (Mixture of Experts Long Short-Term Memory), and a convolutional bidirectional interaction module. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…To tackle this challenge, we propose a Mixture-of-Experts (MoE) method that integrates speaker-related and emotion-related features for depression recognition. …”
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LoRA Fusion: Enhancing Image Generation by Dooho Choi, Jeonghyeon Im, Yunsick Sung

“…One emerging approach constructs several LoRA modules, but more than three typically decrease the generation performance of pre-trained models. The mixture-of-experts model solves the performance issue, but LoRA modules are not combined using text prompts; hence, generating images by combining LoRA modules does not dynamically reflect the user’s desired requirements. …”
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