Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[HTML][HTML] Deep learning in wastewater treatment: a critical review
Modeling wastewater processes supports tasks such as process prediction, soft sensing,
data analysis and computer assisted design of wastewater systems. Wastewater treatment …
data analysis and computer assisted design of wastewater systems. Wastewater treatment …
Deep multimodal data fusion
Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(eg, images, texts, or data collected from different sensors), feature engineering (eg …
(eg, images, texts, or data collected from different sensors), feature engineering (eg …
Delivering arbitrary-modal semantic segmentation
Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …
arbitrary number of modalities remains underexplored. To delve into this problem, we create …
Multi-modal learning with missing modality via shared-specific feature modelling
The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …
Changer: Feature interaction is what you need for change detection
Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …
temporal images as input and predicts “where” the change has occurred. Different from other …
Multimodal token fusion for vision transformers
Many adaptations of transformers have emerged to address the single-modal vision tasks,
where self-attention modules are stacked to handle input sources like images. Intuitively …
where self-attention modules are stacked to handle input sources like images. Intuitively …
Dynamic neural networks: A survey
Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …
models which have fixed computational graphs and parameters at the inference stage …
MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection
Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …
stability of working devices (eg, water treatment system and spacecraft), whose data are …
What makes multi-modal learning better than single (provably)
The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …
different modalities outperform their uni-modal counterparts, since more information is …
Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification
Integration of heterogeneous and high-dimensional data (eg, multiomics) is becoming
increasingly important. Existing multimodal classification algorithms mainly focus on …
increasingly important. Existing multimodal classification algorithms mainly focus on …