Search Results for author:
Found 23 papers, 15 papers with code
E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.
TroubleLLM: Align to Red Team Expert
Not only are these methods labor-intensive and require large budget costs, but the controllability of test prompt generation is lacking for the specific testing domain of LLM applications.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
Backpropagation Path Search On Adversarial Transferability
Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation.
On the Robustness of Latent Diffusion Models
Therefore, in this paper, we aim to analyze the robustness of latent diffusion models more thoroughly.
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
DiffUTE: Universal Text Editing Diffusion Model
Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information.
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Recent object detection approaches rely on pretrained vision-language models for image-text alignment.
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner.
DiffusionInst: Diffusion Model for Instance Segmentation
Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers.
Ranked #9 on
Instance Segmentation
on LVIS v1.0 val
Hierarchical Dynamic Image Harmonization
The MGD effectively applies distinct convolution to the foreground and background, learning the representations of foreground and background regions as well as their correlations to the global harmonization, facilitating local visual consistency for the images much more efficiently.
Ranked #2 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
A2: Efficient Automated Attacker for Boosting Adversarial Training
In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings.
document understanding
Optical Character Recognition (OCR)
+1
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech.
A Vertical Federated Learning Framework for Graph Convolutional Network
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data.
