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Found 7 papers, 3 papers with code
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
With the increasingly powerful performances and enormous scales of Pretrained Language Models (PLMs), promoting parameter efficiency in fine-tuning has become a crucial need for effective and efficient adaptation to various downstream tasks.
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years.
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting
In particular, a hierarchical convolution structure is introduced to extract the information from the series at various scales.
Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models
We explore how the rise of Large Language Models (LLMs) significantly impacts task performance in the field of Natural Language Processing.
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction
Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.
M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare Data
M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis.
MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering
Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality.
