Search Results for author:
Found 9 papers, 3 papers with code
HDLdebugger: Streamlining HDL debugging with Large Language Models
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role.
ChatPattern: Layout Pattern Customization via Natural Language
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention.
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.
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.
AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity.
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.
Removing Anomalies as Noises for Industrial Defect Localization
Our denoising model outperforms the state-of-the-art reconstruction-based anomaly detection methods for precise anomaly localization and high-quality normal image reconstruction on the MVTec-AD benchmark.
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views.
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
