4results about How to "Reduce training" patented technology

This application discloses a cross-domain multimodal recommendation method and system based on multi-agent attribute-driven architecture. Embodiments of this application can be applied to product recommendation scenarios in an e-commerce platform. The method includes: receiving multimodal features; processing the multimodal features using a first operation to generate reconstructed original multimodal features; the reconstructed original multimodal features include attribute-related representations and attribute-independent representations; then, processing the reconstructed original multimodal features using a second operation to generate a modal cue vector; then, processing the modal cue vector and the attribute-related representation to generate a new feature vector; and finally, processing the new feature vector using a sequence encoder to generate attribute-level multimodal sequence states. By sharing basic embeddings, the parameter overhead is significantly reduced, solving the problem that the parameter size of traditional cross-domain multimodal encoders increases linearly with the number of domains.

The application discloses a kind of based on knowledge graph's cancer cell line drug response prediction large language model construction method.The method fuses the structured information of biological knowledge graph and the semantic reasoning ability of large language model, realizes the multi-level modeling and explainable prediction of drug-cell reaction mechanism.The method comprises the following steps: first, integrate multi-source data to construct multi-modal dataset;Second, establish the drug response knowledge graph containing drug, gene and other entities;Then, fine-tune the model using LoRA technology and inject graph embedding, realize cross-modal alignment;Further, dynamic knowledge retrieval and enhancement are carried out using RAG technology;Finally, output drug sensitivity prediction and natural language explanation.The application significantly improves the prediction accuracy and explainability, and can reveal the key gene pathway, providing an efficient intelligent tool for precision cancer treatment.

The application relates to the technical field of artificial intelligence, in particular to an AI model full life cycle collaborative management method and system. The method is characterized in that: a plurality of source heterogeneous data are acquired, dynamic data management is performed on the plurality of source heterogeneous data, a standardized data set is formed, a metadata graph is constructed, and unified correlation management of data, models and training processes is realized. AI model construction and configuration are completed according to a preset development mode, and model training is performed based on the configuration to schedule computing resources, so that a target AI model is generated. After the model completes training, deployment and inference service publishing are performed, and the model running performance is continuously monitored, and data management updating or model retraining is automatically triggered when the performance changes, so that closed-loop collaborative management of the AI model full life cycle is realized. The application effectively guarantees the stability, accuracy and business adaptation capability of the long-term operation of the AI model, and improves the automation level and overall efficiency of the AI model full life cycle management.

The application provides a prompt learning method based on unsupervised knowledge distillation, comprising: a supervised fine-tuning stage, taking a first visual language model as a teacher model, freezing first pre-training parameters of the teacher model, and performing supervised fine-tuning on the teacher model through labeled samples to optimize first learnable prompt parameters of the teacher model; an unsupervised distillation stage, taking a second visual language model as a student model, freezing second pre-training parameters of the student model, aligning inference results of the student model and the teacher model on unlabeled samples, and migrating discriminative knowledge of the teacher model to second learnable prompt parameters of the student model. The application also provides a prompt learning device based on unsupervised knowledge distillation, a storage medium and an electronic device. Therefore, the application significantly improves the adaptation effect of the visual language model on downstream tasks, improves the generalization performance of the visual language model, and has low training and inference costs.