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Found 9 papers, 2 papers with code
p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models
In this paper, we present a novel modeling framework that recasts adapter tuning after attention as a graph message passing process on attention graphs, where the projected query and value features and attention matrix constitute the node features and the graph adjacency matrix, respectively.
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality
Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module.
ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers.
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content.
Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion.
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning
Few-shot learning (FSL) via customization of a deep learning network with limited data has emerged as a promising technique to achieve personalized user experiences on edge devices.
Efficient Deep Space Filling Curve
By modeling the pixel grid as a graph, they first adopt GNN to predict the edge weights and then generate a minimum spanning tree (MST) based on the predictions, which is further used to construct the SFC.
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #17 on
Domain Generalization
on PACS
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
