Search Results for author:
Found 6 papers, 1 papers with code
Equivariant Matrix Function Neural Networks
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.
Ranked #3 on
Graph Regression
on ZINC-500k
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness
By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
Understanding stochastic gradient descent (SGD) and its variants is essential for machine learning.
Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.
A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis
In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net).
Charge-Based Prison Term Prediction with Deep Gating Network
Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction.
